Patent Publication Number: US-2023162351-A1

Title: Diagnosis assistance device, machine learning device, diagnosis assistance method, machine learning method, machine learning program, and alzheimers prediction program

Description:
TECHNICAL FIELD 
     The present invention relates to a technique for predicting if patients who have Alzheimer&#39;s disease neuropathologic change (ADNC) will develop Alzheimer&#39;s disease within a prescribed period, and particularly relates to a prediction technique using artificial intelligence. 
     BACKGROUND ART 
     For the treatment of Alzheimer&#39;s disease (AD), it is necessary to develop early diagnosis techniques, preferably before onset. In response to this necessity, VSRAD (registered trademark) (voxel-based specific regional analysis system for Alzheimer&#39;s disease) has been developed (PTL 1) as an early AD (Alzheimer&#39;s disease) diagnosis assistance system. VSRAD (registered trademark) is image processing and statistical analysis software for reading the degree of atrophy around the parahippocampal gyrus, which is characteristic of early AD, including the prodromal period, from MRI images. Further, the present inventor has developed a technique for predicting whether patients with mild cognitive impairment (MCI), which is considered to be a preliminary stage of Alzheimer&#39;s disease, have progressive pMCI, which will progress to Alzheimer&#39;s disease (progressive MCI) or stable sMCI, which will not progress to Alzheimer&#39;s disease (stable MCI) (PTL 2). 
     MCI is a concept of the boundary region between healthy subjects (NL) and Alzheimer&#39;s disease; however, according to recent guidelines, it is not recommended to set a cutoff value for any test. That is, it is difficult to clearly distinguish between healthy subjects and MCI, and between MCI and Alzheimer&#39;s disease. 
     On the other hand, in recent years, in order to predict the onset of Alzheimer&#39;s disease, detection of Alzheimer&#39;s disease neuropathologic change (ADNC) has been conducted by in-vivo pathological diagnosis. ADNC is identified by two positive findings: deposition of senile plaques (amyloid-β) and neurofibrillary tangles (tau degeneration). At present, amyloid-β deposition and tau degeneration can be detected by spinal fluid tests. In the future, it is expected that blood tests will also be able to detect amyloid-β accumulation and tau degeneration, and amyloid PET and tau PET have been developed. Thus, it is now possible to clearly identify whether patients have ADNC. From the viewpoint of early treatment, the diagnosis of ADNC is expected to become more important than clinical diagnosis in the future. 
     In addition, development of disease-modifying therapies (DMTs), such as reducing amyloid-β accumulation, is underway. For example, in the fall of 2019, aducanumab, developed by Biogen and Eisai, was reported to inhibit the progression of MCI and early Alzheimer&#39;s disease. 
     CITATION LIST 
     Patent Literature 
     PTL 1: WO2013/047278 
     PTL 2: JP6483890B 
     SUMMARY OF INVENTION 
     Technical Problem 
     It is also known that some patients diagnosed as having ADNC do not always develop Alzheimer&#39;s disease. Therefore, it is not appropriate to target all ADNC patients for DMT. At present, it has not been clarified for which patients and what timing DMT should be performed. 
     Accordingly, an object of the present invention is to predict, with high accuracy, the possibility that ADNC patients will develop Alzheimer&#39;s disease within a prescribed period. 
     Solution to Problem 
     The present invention includes the following aspects. 
     Item 1. 
     A diagnosis assistance device predicting a possibility that a subject who has Alzheimer&#39;s disease neuropathologic change will develop Alzheimer&#39;s disease within a prescribed period, 
     the diagnosis assistance device comprising a prediction unit that predicts the possibility according to a machine-learned prediction algorithm. 
     Item 2. 
     The diagnosis assistance device according to Item 1, further comprising: 
     a region segmentation unit that segments a brain image acquired from the subject into gray matter, white matter, and a spinal fluid part, and separates the lateral ventricle from the spinal fluid part; 
     a region-of-interest setting unit that sets multiple regions of interest in each of the gray matter, the white matter, and the lateral ventricle; 
     a t-value and p-value calculation unit that calculates t- and p-values in each region of interest for the volume of each region of interest; and 
     a z-value calculation unit that calculates a z-value of each region of interest based on the t- and p-values, 
     wherein the prediction unit predicts the possibility based on the z-values. 
     Item 3. 
     The diagnosis assistance device according to Item 2, wherein the region segmentation unit determines a boundary between the corpus callosum and surrounding white matter by surface tension and viscosity parameters of a fluid to thereby separate the surrounding white matter. 
     Item 4. 
     The diagnosis assistance device according to Item 2 or 3, wherein when a white matter lesion is present in the white matter, the region-of-interest setting unit extracts the white matter lesion, replaces it with an average signal value of the white matter of the subject, and then sets the regions of interest in the white matter. 
     Item 5. 
     The diagnosis assistance device according to Item 1, further comprising: 
     a region separation unit that separates gray matter from a brain image acquired from the subject; 
     a region-of-interest setting unit that sets multiple regions of interest in the gray matter; 
     a volume calculation unit that calculates the volume of each region of interest; and 
     a z-value calculation unit that calculates a z-value of each region of interest based on the volume, 
     wherein the prediction unit predicts the possibility based on the z-values. 
     Item 6. 
     The diagnosis assistance device according to Item 5, further comprising a covariate correction unit that performs covariate correction on the volume. 
     Item 7. 
     The diagnosis assistance device according to any one of Items 2 to 6, wherein the prediction unit predicts the possibility as a posterior probability from the distance to a hyperplane by a sigmoid function. 
     Item 8. 
     A machine learning device learning the prediction algorithm according to any one of Items 1 to 7, 
     the machine learning device comprising a learning unit that learns the prediction algorithm based on teacher data generated from brain images of multiple persons, and diagnosis results indicating whether each person has developed Alzheimer&#39;s disease before the end of the prescribed period from the acquisition of the brain image. 
     Item 9. 
     The machine learning device according to Item 8, wherein the learning unit is configured from a support vector machine. 
     Item 10. 
     The machine learning device according to Item 8 or 9, wherein the brain images are MRI images. 
     Item 11. 
     The machine learning device according to any one of Items 8 to 10, further comprising a teacher data generation unit that generates the teacher data based on the brain images of multiple persons and the diagnosis results indicating whether each person has developed Alzheimer&#39;s disease before the end of the prescribed period from the acquisition of the brain image. 
     Item 12. 
     The machine learning device according to Item 11, wherein the teacher data generation unit comprises: 
     a region segmentation unit that segments each of the brain images acquired from the persons into gray matter, white matter, and a spinal fluid part, and separates the lateral ventricle from the spinal fluid part; 
     a region-of-interest setting unit that sets multiple regions of interest in each of the gray matter, the white matter, and the lateral ventricle; 
     a t-value and p-value calculation unit that calculates t- and p-values in each region of interest for the volume of each region of interest; and 
     a z-value calculation unit that calculates a z-value of each region of interest based on the t- and p-values, 
     wherein the teacher data includes the diagnosis results and the z-values. 
     Item 13. 
     The machine learning device according to Item 11, wherein the teacher data generation unit comprises: 
     a region separation unit that separates gray matter from each of the brain images acquired from the persons; 
     a region-of-interest setting unit that sets multiple regions of interest in the gray matter; 
     a volume calculation unit that calculates the volume of each region of interest; and 
     a z-value calculation unit that calculates a z-value of each region of interest based on the volume, 
     wherein the teacher data includes the diagnosis results and the z-values. 
     Item 14. 
     The machine learning device according to Item 13, further comprising a covariate correction unit that performs covariate correction on the volume. 
     Item 15. 
     A diagnosis assistance method predicting a possibility that a subject who has Alzheimer&#39;s disease neuropathologic change will develop Alzheimer&#39;s disease within a prescribed period, the method comprising a prediction step of predicting the possibility according to a machine-learned prediction algorithm. 
     Item 16. 
     A machine learning method learning the prediction algorithm according to Item 12, the method comprising a learning step of learning the prediction algorithm based on teacher data generated from brain images of multiple persons and diagnosis results indicating whether each person has developed Alzheimer&#39;s disease before the end of the prescribed period from the acquisition of the brain image. 
     Item 17. 
     A machine learning program causing a computer to learn the prediction algorithm according to Item 15, wherein the machine learning program causes the computer to execute a learning step of learning the prediction algorithm based on teacher data generated from brain images of multiple persons and diagnosis results indicating whether each person has developed Alzheimer&#39;s disease before the end of the prescribed period from the acquisition of the brain image. 
     Item 18. 
     An Alzheimer&#39;s prediction program causing a computer to execute: 
     a teacher data generation step of generating teacher data from brain images of multiple persons and diagnosis results indicating whether each person has developed Alzheimer&#39;s disease before the end of a prescribed period from the acquisition of the brain image, 
     a learning step of learning a prediction algorithm based on the teacher data, and 
     a prediction step of predicting, according to the prediction algorithm, a possibility that a subject who has Alzheimer&#39;s disease neuropathologic change will develop Alzheimer&#39;s disease within the prescribed period; 
     wherein the teacher data generation step comprises: 
     separating gray matter from the brain images acquired from the persons, 
     setting multiple regions of interest in the gray matter, 
     calculating the volume of each region of interest, 
     calculating a z-value of each region of interest based on the volume, and 
     associating the diagnosis results with the z-values to generate the teacher data; and wherein the prediction step comprises: 
     separating gray matter from a brain image acquired from the subject, 
     setting multiple regions of interest in the gray matter, 
     calculating the volume of each region of interest, 
     calculating a z-value of each region of interest based on the volume, and 
     predicting the possibility based on the z-values. 
     Advantageous Effects of Invention 
     According to the present invention, the possibility that ADNC patients will develop Alzheimer&#39;s disease can be predicted with high accuracy. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG.  1    is a block diagram showing a schematic structure of a prediction system according to a first embodiment of the present invention. 
         FIG.  2    is a block diagram showing the function of a machine learning device according to the first embodiment of the present invention. 
         FIG.  3    is a flowchart showing the entire procedure of a machine learning method according to the first embodiment of the present invention. 
         FIG.  4    is a flowchart showing the procedure of a teacher data generation step in the machine learning method according to the first embodiment of the present invention. 
         FIG.  5    is a specific example of a brain image segmentation method. 
         FIG.  6    is an explanatory diagram of the effect of brain image segmentation. 
         FIG.  7    is an explanatory diagram of the effect of brain image segmentation. 
         FIG.  8    is an explanatory diagram of the effect of brain image segmentation. 
         FIG.  9    is an explanatory diagram of the effect of brain image segmentation. 
         FIG.  10    is an explanatory diagram of the effect of lateral ventricle separation. 
         FIG.  11    is an explanatory diagram of an example in which a three-dimensional structure of the corpus callosum was determined and its boundary was clarified. 
         FIG.  12    is a block diagram showing the function of a diagnosis assistance device according to the first embodiment of the present invention. 
         FIG.  13    is a graph showing the period until pMCI patients develop AD. 
         FIG.  14    is a block diagram showing a schematic structure of a prediction system according to a second embodiment of the present invention. 
         FIG.  15    is a block diagram showing the function of a machine learning device according to the second embodiment of the present invention. 
         FIG.  16    is a flowchart showing the entire procedure of a machine learning method according to the second embodiment of the present invention. 
         FIG.  17    is a flowchart showing the procedure of a teacher data generation step in the machine learning method according to the second embodiment of the present invention. 
         FIG.  18  ( a )  is a flowchart showing the procedure of a region separation step, and ( b ) is a flowchart showing the procedure of an image correction step. 
         FIG.  19    is a block diagram showing the function of a diagnosis assistance device according to the second embodiment of the present invention. 
         FIG.  20    is a graph showing the relationships between the number of months elapsed and the ratio of developing AD in groups classified by spinal fluid tests, prediction results by prediction algorithms, etc. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     First Embodiment 
     A first embodiment of the present invention is described below with reference to the attached drawings. The present invention is not limited to the following embodiment. 
     Entire Structure 
       FIG.  1    is a block diagram showing a schematic structure of a prediction system  100  according to the present embodiment. The prediction system  100  comprises a machine learning device  1  and a diagnosis assistance device  2 . The machine learning device  1  learns a prediction algorithm for predicting the possibility that a subject who has Alzheimer&#39;s disease neuropathologic change (ADNC) (hereinafter referred to as “ADNC subject”) will develop Alzheimer&#39;s disease within a prescribed period. The diagnosis assistance device  2  predicts the possibility that the ADNC subject will develop Alzheimer&#39;s disease within a prescribed period, according to the prediction algorithm learned by the machine learning device  1 . The machine learning device  1  and the diagnosis assistance device  2  may be achieved as separate devices, or the machine learning device  1  and the diagnosis assistance device  2  may be configured as a single device. 
     Configuration examples of the machine learning device  1  and diagnosis assistance device  2  are described below. 
     Machine Learning Device 
       FIG.  2    is a block diagram showing the function of the machine learning device  1  according to the present embodiment. The machine learning device  1  can be configured, for example, from a general-purpose personal computer, and comprises CPU (not shown), a main storage device (not shown), an auxiliary storage device  11 , and the like as hardware configurations. In the machine learning device  1 , the CPU reads out various programs stored in the auxiliary storage device  11  to the main storage device and executes them, thereby executing various kinds of arithmetic processing. The auxiliary storage device  11  can be configured, for example, from a hard disk drive (HDD) or a solid-state drive (SSD). Further, the auxiliary storage device  11  may be built into the machine learning device  1 , or may be provided as an external storage device separate from the machine learning device  1 . 
     The machine learning device  1  has the function of learning a prediction algorithm D 4  for predicting the possibility that an ADNC subject will develop Alzheimer&#39;s disease within a prescribed period (e.g., within 5 years). ADNC patients are classified into the following groups:
         patients who have already developed Alzheimer&#39;s disease (AD),   progressive mild cognitive impairment patients who will develop Alzheimer&#39;s disease within a prescribed period (pMCI), and   stable mild cognitive impairment patients who will not develop Alzheimer&#39;s disease in the future (sMCI).
 
In the present embodiment, among ADNC patients, AD and pMCI are referred to as “ADNC spectrum.” That is, the machine learning device  1  has the function of learning prediction algorithms D 4  and D 5  for predicting the possibility that an ADNC subject is ADNC spectrum.
       

     In order to achieve this function, the machine learning device  1  comprises a teacher data generation unit  12  and a learning unit  13  as function blocks. The teacher data generation unit  12  is a function block that generates teacher data D 3  from brain images D 1  and diagnosis results D 2  of multiple persons. The multiple persons are preferably, but are not limited to, patients who have been diagnosed as having ADNC, and may include persons who have been diagnosed as having mild cognitive impairment and healthy subjects. The learning unit  13  is a function block that learns the prediction algorithms D 4  and D 5  based on the teacher data D 3 . The teacher data generation unit  12  and the learning unit  13  are achieved by executing machine learning programs stored in the auxiliary storage device  11 . 
     The machine learning device  1  is accessible to a diagnosis information database DB. The diagnosis information database DB stores brain images D 1  of multiple persons, and diagnosis results D 2  indicating whether each person is ADNC spectrum. The diagnosis results D 2  indicate whether each parson has developed Alzheimer&#39;s disease before the end of the prescribed period from the acquisition of the brain image D 1 . The phrase “before the end of the prescribed period from the acquisition of the brain image D 1 ” may include not only a time period from the acquisition of the brain image D 1  to the end of the prescribed period, but also a time period before the acquisition of the brain image D 1 . That is, the diagnosis results D 2  may include only diagnosis results indicating whether each person has developed AD from the acquisition of the brain image D 1  to the end of the prescribed period, and may further contain diagnosis results indicating whether each person has developed AD at the time of acquisition of the brain image D 1 . 
     In the present embodiment, the brain images D 1  are three-dimensional MRI images. It is desirable to prepare at least certain numbers of the brain images D 1  and the diagnosis results D 2  available to obtain a statistical significance for each of a target group of Alzheimer&#39;s disease patients, a target group of patients reaching Alzheimer&#39;s disease, and a target group of patients not reaching Alzheimer&#39;s disease. The diagnosis information database DB may be owned by one medical institution or shared by multiple medical institutions. 
     The teacher data generation unit  12  comprises, as function blocks for generating the teacher data D 3 , a brain image acquisition unit  121 , a region segmentation unit  122 , an image correction unit  123 , a region-of-interest setting unit  124 , a volume calculation unit  125 , a t-value and p-value calculation unit  126 , a z-value calculation unit  127 , and a diagnosis result acquisition unit  128 . 
     The brain image acquisition unit  121  acquires a brain image D 1  from the diagnosis information database DB. The region segmentation unit  122  to the z-value calculation unit  127  set multiple regions of interest (ROI) in the brain region for the acquired brain image D 1 , and perform arithmetic processing, such as calculation of the z-value of each region of interest. The specific details of the arithmetic processing performed by each of the region segmentation unit  122  to the z-value calculation unit  127  are described later. 
     The diagnosis result acquisition unit  128  acquires the diagnosis result D 2  of each person, whose brain image D 1  has been acquired, from the diagnosis information database DB. The teacher data generation unit  12  associates the z-value of each region of interest with the diagnosis result D 2  for each person to generate teacher data D 3 , and stores the data in the auxiliary storage device  11 . 
     The learning unit  13  comprises a first learning unit  131  and a second learning unit  132 . The first learning unit  131  learns a prediction algorithm D 4  based on the teacher data D 3 , and stores the learned prediction algorithm D 4  in the auxiliary storage device  11 . The second learning unit  132  further learns the prediction algorithm D 4 , and stores a learned prediction algorithm D 5  in the auxiliary storage device  11 . The machine learning method is not particularly limited; however, in the present embodiment, the first learning unit  131  and the second learning unit  132  are configured from support vector machines. 
     Machine Learning Method 
     The machine learning method according to the present embodiment is performed by using the machine learning device  1  shown in  FIG.  2   .  FIG.  3    is a flowchart showing the entire procedure of the machine learning method according to the present embodiment.  FIG.  4    is a flowchart showing the procedure of a teacher data generation step in the machine learning method according to the present embodiment. 
     In step S 1  shown in  FIG.  3   , the brain image acquisition unit  121  and the diagnosis result acquisition unit  128  respectively acquire brain images D 1  and diagnosis results D 2  of multiple persons from the diagnosis information database DB. The brain image D 1  and diagnosis result D 2  of one person may be acquired, or the brain images D 1  and diagnosis results D 2  of multiple persons may be acquired at one time. 
     In step S 2 , the teacher data generation unit  12  generates teacher data D 3  from the acquired brain images D 1  and diagnosis results D 2 . 
       FIG.  4    is a flowchart showing the specific processing procedure of step S 2  for producing teacher data. Step S 2  mainly comprises steps S 21  to S 27 . 
     In step S 21 , the region segmentation unit  122  separates and removes tissues other than the brain from the acquired brain image D 1 , further segments the brain image, from which tissues other than the brain have been separated and removed, into gray matter, white matter, and a spinal fluid part, and separates the lateral ventricle from the spinal fluid part. In the present embodiment, in order to prevent brain lesions from being ignored by standardization using a conventional method such as SPM, the region segmentation unit  122  segments the brain image using a signal intensity-dependent maximum-likelihood method and a posterior probability method. For the purpose of preventing the incorporation of white matter lesions into the gray matter occurring as a result of the above, a multichannel segmentation technique that introduces FLAIR images into segmentation is now available. 
     Specifically, as shown in  FIG.  5   , a FLAIR image with low space information is complemented to an image with higher space information by three-dimensional brain image data, and only white matter lesions are then extracted, followed by filling (replacement) with the average signal value of the white matter of the subject. This allows separation with unprecedented accuracy in the present embodiment, as shown in  FIG.  6   . 
       FIGS.  7  to  9    are other examples showing the effects of the method of the present embodiment. In  FIGS.  7  to  9   , according to conventional methods, white areas are incorporated into the upper two locations of the gray matter, and the white matter is missing in the upper two locations of the white matter. Use of the method of the present embodiment allows separation with unprecedented accuracy. 
     Thereafter, if necessary, the image quality of the brain image may be evaluated, and if the image quality is below a certain level, displaying a warning or like processing may be performed. 
     In step S 22 , the image correction unit  123  nonlinearly transforms the brain segmented in step S 21  into coordinates in MNI space. In the transformation, the image correction unit  123  converts the tensor quantity for each voxel into a signal value. 
     In step S 23 , the region-of-interest setting unit  124  sets multiple regions of interest in the brain regions included in the brain image, i.e., gray matter, white matter, and lateral ventricle. In the present embodiment, the region-of-interest setting unit  124  segments the gray matter into 290 parts, the white matter into 33 parts, and the lateral ventricle separated from other brain ventricles into 2 parts (left and right lateral ventricles), and sets each of the segmented regions as a region of interest. 
     As described above, the region segmentation unit  122  separates the lateral ventricle from the spinal fluid part. In general brain atrophy, the brain surface shrinks toward the center (a gap is formed between the skull and the brain surface); however, when there are white matter lesions, the lateral ventricle expands compensatory and the brain surface shrinks from inside to outside. Because of this, in the present embodiment, the region segmentation unit  122  separates the lateral ventricle. As a result of this procedure, the boundary between the lateral ventricle, gray matter, and white matter can be accurately determined, thereby increasing discrimination accuracy. 
       FIG.  10    shows the effect of separation of the lateral ventricle in the present embodiment. When 3 cases determined by conventional methods are compared with the present embodiment in the lower right portion, the boundary between the lateral ventricle and the white matter is accurately obtained, as shown separated by a dashed line in the figure. This allows discrimination with higher accuracy than before. 
     The gray matter can be segmented by using 108 automated anatomical labeling (AAL) regions, 8 regions such as the entorhinal area related to Alzheimer&#39;s disease prepared by the present inventor, 118 Brodmann regions, and 56 Loni Probabilistic Brain Atlas 40 (LPBA40) regions. The white matter can be segmented by regions of interest uniquely created in MNI space. 
     In conventional methods, the size of the corpus callosum can be evaluated only by its cross-sectional area in a sagittal section. In the present embodiment, for the three-dimensional evaluation of this, the boundary between the corpus callosum and the surrounding white matter is determined by the surface tension and viscosity parameters of a fluid to thereby separate the surrounding white matter. 
     Since the corpus callosum is continuous with the subcortical white matter without boundaries, a special technique is required to create its regions of interest. More specifically, in a three-dimensional brain image, a virtual fluid is placed in the frontal and occipital parts of the corpus callosum, and a situation in which the virtual fluid expands three-dimensionally in the brain is simulated to determine its boundary. Typically, assuming water droplets equivalent to the spinal fluid, the frontal and occipital side shapes of the corpus callosum are determined from the shape of the water droplets as they spread freely, based on their surface tension and viscosity, thereby determining the shapes of the gray matter and white matter in contact with the corpus callosum. This can clarify the boundary surface that actually has a fine and intricate three-dimensional shape by a simple but highly accurate method. 
       FIG.  11    shows an example in which the three-dimensional structure of the corpus callosum is determined and its boundary is clarified by the method of the present embodiment. This clarifies the boundary between the gray matter and white matter, increasing discrimination accuracy. Further, the lateral ventricle volume can be measured with high accuracy by excluding the corpus callosum that falls within the region of interest for the lateral ventricle. 
     In conventional methods such as SPM, this processing is performed by obtaining a posterior probability by Bayesian estimation, and intermediate values between 0 and 1 are defined as partial volume. However, this would ignore outlier values due to lesions, which does not satisfy the object of the present invention. In the present embodiment, Bayesian estimation is used only in the initial stages such as affine transformation and skull strip, and the segmentation processing is performed using a maximum likelihood estimation method based on signal values of the image. White matter lesions are problematic in this case; however, in the present embodiment, as shown in  FIG.  5   , the problem is solved by using a three-dimensional brain image, complementing the space information of a FLAIR image, extracting white matter lesions from the FLAIR image with excellent contrast, and pasting them into the three-dimensional image. 
     The lateral ventricle can be segmented by using a template prepared beforehand in MNI space by the inventor. 
     In step S 24 , the volume calculation unit  125  calculates the volume of each region of interest. In the present embodiment, the volume calculation unit  125  calculates the volume using the Jacobian matrix in the tensor transformation. The reason for calculating volume instead of concentration is that volume values can be used universally. For example, even if z-values after statistical processing are the same, volume values may be different. In conventional methods, after calculating the volume for each voxel, the volume is calculated as the sum of voxels within the region of interest, whereas in the present embodiment, the calculation is performed using the region of interest as one unit. Theoretically, the results are the same for both methods; however, in practice, calculating the volume for each region of interest is more accurate because volume values for each voxel are more susceptible to noise. 
     In step S 25 , the t-value and p-value calculation unit  126  calculates a t-value by replacing the t-distribution with the normal distribution. For this purpose, the commonly used IXI database is used as a control group. The IXI database has around 100 cases of normal brain for each age group, and there is no problem in replacing them as the normal distribution. 
     Specifically, if the values (volume for each region of interest) to be examined in the population are normally distributed (if the number of subjects in the survey is large, values are estimated to be normally distributed), in order to examine whether there is a statistically significant difference in average values between two groups (healthy subjects and Alzheimer&#39;s disease patients), the t-value is determined by the following mathematical formula. 
     
       
         
           
             t 
             = 
             
               
                 
                   x 
                   _ 
                 
                 - 
                 
                   μ 
                   0 
                 
               
               
                 s 
                 / 
                 
                   n 
                 
               
             
           
         
       
         
         
           
               x : sample mean 
             s: sample standard deviation 
             n: sample size 
           
         
       
    
     The degree of freedom is n−1. 
     The p-value indicates what t-value can be used to set the boundary to be statistically significant from the t-value obtained by the above mathematical formula. The z-value is determined by replacing the p-value on the T-distribution by the p-value on the Z-distribution. 
     In step S 26 , the z-value calculation unit  127  calculates the z-value in each region of interest based on the t and p-values in each region of interest. As a result, the z-values in the multiple regions of interest are calculated from the brain image D 1 . 
     The z-value is for statistical validation determined from the t- and p-values. Specifically, in healthy subjects, the distribution of the volumes of regions of interest at a site is determined and applied to normal distribution. The z-value indicates standard deviation showing to which position of the normal distribution the volume of the region of interest at the same site of patients corresponds. In the case of a normal distribution (mean=0, standard deviation=1), the standard deviation value is obtained as the z-value. In the present embodiment, the t-test is performed, and the obtained value is thus a t-value. If the population has a normal distribution, this value is almost the same as the z-value. The z-value in this case is a z-value when the z-test is performed, and refers to a z-value indicating standard deviation. 
     Since the z-values are standardized values, they are suitable as input values for the subsequent artificial intelligence. This is because the weighting is not biased in the early stage when artificial intelligence learning extracts target features from the input values. 
     In step S 27 , the data including the multiple regions of interest and z-values are associated with the diagnosis results D 2  to generate teacher data D 3 . 
     S 2  shown in  FIG.  3    is terminated by steps S 21  to S 27  described above. The generated teacher data D 3  is stored in the auxiliary storage device  11 , and steps S 1  and S 2  are repeated until the teacher data D 3  is sufficiently stored in the auxiliary storage device  11  (YES in step S 3 ). 
     Subsequently, in step S 4 , the first learning unit  131  learns a prediction algorithm D 4  (SVMst) based on the teacher data D 3  stored in the auxiliary storage device  11 . In the present embodiment, the learning unit  13  performs learning by a support vector machine (SVM) using a radial basis function (RBF) kernel. At this time, the optimal value of the hyperparameter is determined using the leave-one-out method for cross-validation, and versatile discriminant boundaries between a target group of Alzheimer&#39;s disease patients, a target group of patients reaching Alzheimer&#39;s disease, and a target group of patients not reaching Alzheimer&#39;s disease are determined. The learned prediction algorithm D 4  is stored in the auxiliary storage device  11 . 
     Subsequently, in step S 5 , the second learning unit  132  further performs additional learning by imputing scores of the Mini-Mental State Examination (MMSE; a set of questions developed for dementia diagnosis by Folstein et al. in 1975 in the U.S.) into the prediction algorithm D 4  from the diagnosis information database DB to generate a prediction algorithm D 5  (SVMcog). The prediction algorithm D 5  is stored in the auxiliary storage device  11 . 
     Learning by the first learning unit  131  and learning by the second learning unit  132  may be performed in parallel. Specifically, the first learning unit  131  may learn the diagnosis results D 2  as correct answer labels, and the z-value of each region of interest as a diagnostic input variable, and the second learning unit  132  may learn the diagnosis results D 2  as correct answer labels, and the z-value of each region of interest and the MMSE score as input variables. 
     Diagnosis Assistance Device 
     The form of disease determination using the learned prediction algorithm D 4  is described below. 
       FIG.  12    is a block diagram showing the function of a diagnosis assistance device  2  according to the present embodiment. As with the machine learning device  1  shown in  FIG.  2   , the diagnosis assistance device  2  can be configured, for example, from a general-purpose personal computer. That is, the diagnosis assistance device  2  comprises CPU (not shown), a main storage device (not shown), an auxiliary storage device  21 , and the like as hardware configurations. In the diagnosis assistance device  2 , the CPU reads out various programs stored in the auxiliary storage device  21  to the main storage device and executes them, thereby executing various kinds of arithmetic processing. The auxiliary storage device  21  can be configured, for example, from a hard disk drive (HDD) or a solid-state drive (SSD), and stores the learned prediction algorithms D 4  and D 5 . Further, the auxiliary storage device  21  may be built into the diagnosis assistance device  2 , or may be provided as an external storage device separate from the diagnosis assistance device  2 . 
     The diagnosis assistance device  2  is connected to an MRI device  3 , and a brain image of the subject acquired by the MRI device  3  is sent to the diagnosis assistance device  2 . The brain image of the subject acquired by the MRI device  3  may be once stored in a recording medium, and the brain image may be input into the diagnosis assistance device  2  via the recording medium. 
     The diagnosis assistance device  2  has the function of predicting the possibility that the subject will develop Alzheimer&#39;s disease within a prescribed period (e.g., within 5 years) (i.e., the possibility that the subject is ADNC spectrum), based on the brain image of the subject. In order to achieve this function, the diagnosis assistance device  2  comprises an image processing unit  22  and a prediction unit  23  as function blocks. 
     In the ADNI database, regarding 284 patients who developed Alzheimer&#39;s disease after diagnosis as mild cognitive impairment (pMCI),  FIG.  13    shows the period from diagnosis to onset. The data reveal that 87.3% of the pMCI patients developed the disease within 3 years, 95.8% within 4 years, and 97.5% within 5 years. Therefore, the prescribed period is not particularly limited, but is preferably 3 to 5 years. 
     The image processing unit  22  sets multiple regions of interest in the brain region for the brain image acquired from the outside, performs arithmetic processing, such as calculation of the z-value of each region of interest, and outputs the z-value of each region of interest to the prediction unit  23 . In order to generate the z-value of each region of interest, the image processing unit  22  comprises a brain image acquisition unit  221 , a region segmentation unit  222 , an image correction unit  223 , a region-of-interest setting unit  224 , a volume calculation unit  225 , a t-value and p-value calculation unit  226 , and a z-value calculation unit  227 . These function blocks have the same functions as the brain image acquisition unit  121 , region segmentation unit  122 , image correction unit  123 , region-of-interest setting unit  124 , volume calculation unit  125 , t-value and p-value calculation unit  126 , and z-value calculation unit  127  of the teacher data generation unit  12  shown in  FIG.  2   . 
     The brain image of the subject is acquired by the brain image acquisition unit  221 . Thereafter, each of the region segmentation unit  222  to the z-value calculation unit  227  performs the processing of steps S 21  to S 27  shown in  FIG.  4   , and generates the z-value of each region of interest. 
     The prediction unit  23  predicts the possibility that the subject is ADNC spectrum, according to the prediction algorithm D 4 . In the present embodiment, the prediction unit  23  predicts the possibility that the subject is ADNC spectrum, based on the z-value of each region of interest generated by the image processing unit  22 . The prediction result is displayed, for example, on a display  4  connected to the diagnosis assistance device  2 . The possibility that the subject is ADNC spectrum can be determined by a sigmoid function as a posterior probability (0 to 1) from the distance to a hyperplane (hyperplane in elementary geometry, a generalization of the two-dimensional plane to other dimensions). Further, the diagnosis assistance device  2  may simply predict whether the subject is ADNC spectrum. 
     From the above, the diagnosis assistance device  2  predicts the possibility that the subject is ADNC spectrum, using the prediction algorithm D 4 . The prediction algorithm D 4  is obtained by machine learning in the machine learning device  1 , and machine learning using a sufficient amount of teacher data D 3  makes it possible to increase the prediction accuracy of the diagnosis assistance device  2 . Thus, in the present embodiment, the possibility that the subject is ADNC spectrum can be predicted with high accuracy by using artificial intelligence. 
     Additional Notes 
     The present invention is not limited to the above embodiment, and can be changed in various ways within the scope shown in the claims. Forms obtained by suitably combining the technical means disclosed in the embodiment are also included in the technical scope of the present invention. 
     For example, MRI images are used as brain images in the above embodiment; however, X-ray CT images, SPECT images, PET images, or the like may also be used. Further, changes over time in MRI images using tensor-based morphometry may also be used. 
     In the above embodiment, the machine learning device  1  comprises both the teacher data generation unit  12  and the learning unit  13 ; however, the teacher data generation unit  12  and the learning unit  13  may be achieved as separate devices. That is, teacher data D 3  generated in a device other than the machine learning device  1  may be input into the machine learning device  1 , and the machine learning device  1  may only learn the prediction algorithms D 4  and/or D 5 . 
     Similarly, the image processing unit  22  and prediction unit  23  of the diagnosis assistance device  2  may be achieved as separate devices. In this case, the z-value of each region of interest generated in a device other than the diagnosis assistance device  2  may be input into the diagnosis assistance device  2 , and the diagnosis assistance device  2  may only perform prediction based on the prediction algorithms D 4  and/or D 5 . 
     In the above embodiment, the teacher data D 3  for learning the prediction algorithms D 4  and D 5  are generated from brain images of multiple ADNC patients, and diagnosis results indicating whether each patient is ADNC spectrum; however, the present invention is not limited thereto. For example, teacher data may be generated from brain images of AD patients, MCI patients, and healthy subjects without Alzheimer&#39;s disease. Even when a diagnosis assistance device is constructed using a prediction algorithm learned based on such teacher data, the possibility that subjects will develop Alzheimer&#39;s disease can be predicted with higher accuracy than by using conventional techniques, as shown in the Examples provided later. 
     The technique disclosed in PTL 2 predicts whether patients with mild cognitive impairment (MCI) will develop Alzheimer&#39;s disease within a prescribed period, which is different from the present invention. However, since there are no clear criteria for determining whether patients have MCI, it is difficult to accurately select prediction targets. In contrast, in the present invention, the prediction targets are ADNC spectrum, without clearly distinguishing between healthy subjects and MCI or between MCI and Alzheimer&#39;s disease. That is, the prediction targets may be subjects who have already developed AD or subjects who will develop AD in the future, as long as they are ADNC spectrum. Therefore, it is possible to accurately assign teacher labels during learning and to prevent the accuracy of prediction results from deteriorating. In addition, by using the present invention to predict that subjects who have not developed Alzheimer&#39;s disease will progress to Alzheimer&#39;s disease in the future, ADNC patients who should be targeted for disease-modifying therapies (DMTs) using expensive aducanumab etc. in the future can be appropriately selected. 
     In the above embodiment, the region segmentation units  122  and  222  segment a brain image into gray matter, white matter, and a spinal fluid part, and separate the lateral ventricle from the spinal fluid part. Alternatively, only the gray matter may be separated from the image. In this case, the region-of-interest setting units  124  and  224  set multiple regions of interest in the gray matter, the t-value and p-value calculation units  126  and  226  calculate the t- and p-values in each region of interest for the volume of each region of interest set in the gray matter, and the z-value calculation units  127  and  227  calculate the z-value of each region of interest based on the t- and p-values. 
     Second Embodiment 
     A second embodiment of the present invention is described below with reference to the attached drawings. The present invention is not limited to the following embodiment. The members having the same functions as those of the first embodiment described above are denoted by the same reference numerals, and their explanations are omitted. 
     Entire Structure 
       FIG.  14    is a block diagram showing a schematic structure of a prediction system  100 ′ according to the present embodiment. The prediction system  100 ′ comprises a machine learning device  1 ′ and a diagnosis assistance device  2 ′. The machine learning device  1 ′ learns a prediction algorithm for predicting the possibility that an ADNC subject will develop Alzheimer&#39;s disease within a prescribed period. The diagnosis assistance device  2 ′ predicts the possibility that an ADNC subject will develop Alzheimer&#39;s disease within a prescribed period, according to the prediction algorithm learned by the machine learning device  1 ′. The machine learning device  1 ′ and the diagnosis assistance device  2 ′ may be achieved as separate devices, or the machine learning device  1 ′ and the diagnosis assistance device  2 ′ may be configured as a single device. 
     Configuration examples of the machine learning device  1 ′ and diagnosis assistance device  2 ′ are described below. 
     Machine Learning Device 
       FIG.  15    is a block diagram showing the function of the machine learning device  1 ′ according to the present embodiment. The hardware configurations of the machine learning device  1 ′ may be the same as those of the machine learning device  1  shown in  FIG.  1   . 
     The machine learning device  1 ′ has the function of learning prediction algorithms D 4 ′ and D 5 ′ for predicting the possibility that an ADNC subject will develop Alzheimer&#39;s disease within a prescribed period (e.g., within 5 years). 
     In order to achieve this function, the machine learning device  1 ′ comprises a teacher data generation unit  12 ′ and a learning unit  13  as function blocks. The teacher data generation unit  12 ′ is a function block that generates teacher data D 3 ′ from brain images D 1 ′ and diagnosis results D 2 ′ of multiple persons. The multiple persons are patients diagnosed as having ADNC and healthy subjects. 
     The learning unit  13  is a function block that learns the prediction algorithms D 4 ′ and D 5 ′ based on the teacher data D 3 ′. The teacher data generation unit  12 ′ and the learning unit  13  are achieved by executing machine learning programs stored in the auxiliary storage device  11 . 
     The machine learning device  1 ′ is accessible to a diagnosis information database DB. The diagnosis information database DB stores brain images D 1 ′ of multiple persons, and diagnosis results D 2 ′ indicating whether each person is ADNC spectrum and whether each person is a healthy subject. In the present embodiment, the brain images D 1 ′ are three-dimensional MRI images. 
     As function blocks for generating the teacher data D 3 ′, the teacher data generation unit  12 ′ comprises a brain image acquisition unit  121 , a region separation unit  122 ′, an image correction unit  123 ′, a region-of-interest setting unit  124 ′, a volume calculation unit  125 ′, a covariate correction unit  126 ′, a z-value calculation unit  127 ′, and a diagnosis result acquisition unit  128 . 
     The brain image acquisition unit  121  acquires a brain image D 1  from the diagnosis information database DB. The region separation unit  122 ′ to the z-value calculation unit  127 ′ set multiple regions of interest (ROI) in the brain region for the acquired brain image D 1 , and perform arithmetic processing, such as calculation of z-values of the regions of interest. The specific details of the arithmetic processing of each of the region separation unit  122 ′ to the z-value calculation unit  127 ′ are described later. 
     The diagnosis result acquisition unit  128  acquires the diagnosis result D 2 ′ of each person, whose brain image D 1 ′ has been acquired, from the diagnosis information database DB. The teacher data generation unit  12 ′ associates the z-value of each region of interest with the diagnosis result D 2  for each person to generate teacher data D 3 ′, and stores the data in the auxiliary storage device  11 . 
     The learning unit  13  comprises a first learning unit  131  and a second learning unit  132 . The first learning unit  131  learns a prediction algorithm D 4 ′ based on the teacher data D 3 ′, and stores the learned prediction algorithm D 4 ′ in the auxiliary storage device  11 . The second learning unit  132  further learns the prediction algorithm D 4 ′, and stores a learned prediction algorithm D 5 ′ in the auxiliary storage device  11 . Although the machine learning method is not particularly limited, in the present embodiment, the first learning unit  131  and the second learning unit  132  are configured from support vector machines, as with the first embodiment. 
     Machine Learning Method 
     The machine learning method according to the present embodiment is performed by using the machine learning device  1 ′ shown in  FIG.  15   .  FIG.  16    is a flowchart showing the entire procedure of the machine learning method according to the present embodiment.  FIG.  17    is a flowchart showing the procedure of a teacher data generation step in the machine learning method according to the present embodiment. 
     In step S 1 ′ shown in  FIG.  16   , the brain image acquisition unit  121  and the diagnosis result acquisition unit  128  acquire, respectively, brain images D 1 ′ and diagnosis results D 2 ′ of multiple persons from the diagnosis information database DB. The brain image D 1 ′ and diagnosis result D 2 ′ of one person may be acquired, or the brain images D 1 ′ and diagnosis results D 2 ′ of multiple persons may be acquired at one time. 
     In step S 2 ′, the teacher data generation unit  12 ′ generates teacher data D 3 ′ from the acquired brain images D 1 ′ and diagnosis results D 2 ′. 
       FIG.  17    is a flowchart showing the specific processing procedure of step S 2 ′ for generating teacher data. Step S 2 ′ mainly comprises steps S 21 ′ to S 26 ′. 
     In step S 21 ′, the region separation unit  122 ′ separates gray matter tissue from the brain image D 1 ′. Specifically, the region separation unit  122 ′ performs the processing of steps S 211  to S 213  shown in  FIG.  18 ( a ) . 
     In step S 211 , the brain image is subjected to alignment processing with the brain image segmented into voxel units. Specifically, in order to match the brain image to the shape of a standard brain image so as to separate gray matter with good accuracy in step S 212  described later, four types of transformation (translation, rotation, scaling, and shear) are performed by linear transformation (affine transformation) to correct the spatial location and angle of the brain image. Specifically, four types of transformation parameters (translation, rotation, scaling, and shear) are obtained for each of the x, y, and z directions in three-dimensional space ( 12  parameters in total), such that the sum of squares of errors between the brain image and the standard brain image template is minimized. Then, using the obtained parameters, the brain image is subjected to affine transformation to achieve spatial alignment of the brain image with respect to the standard brain image whose location, size, etc. are predefined. In this alignment processing, it is also effective to add, in addition to linear transformation, nonlinear transformation to further approximate the shape to the standard brain image. Since the cubic voxels segmented beforehand are deformed by the deformation of the brain into the standard brain image by this alignment processing, voxel segmentation is performed again on the brain image after the alignment processing. 
     In step S 212 , gray matter extraction processing is performed using the newly segmented voxels. The input T1-weighted brain image includes three types of tissue: gray-colored gray matter corresponding to nerve cells, white matter brighter in color than the gray matter and corresponding to nerve fibers, and substantially black-colored cerebrospinal fluid. Therefore, in the gray matter extraction processing, attention is focused on gray matter tissue, and the processing of extracting the gray matter tissue by voxel unit is performed. Specifically, the gray matter is separated from the brain image including the gray matter, white matter, and cerebrospinal fluid by clustering into these three clusters. For this clustering processing, a model of concentration values and a model of existence probability of the three tissues with respect to spatial locations can be used. 
     The model of concentration values is obtained by modeling of different distributions of voxel concentration values for each tissue. The tissues are arranged in the order of white matter, gray matter, and cerebrospinal fluid, in descending order of their concentration values (i.e., closer to white). It is assumed that the histogram of concentration values after separating each of them will be normal distribution. 
     The model of existence probability of the three tissues with respect to spatial locations expresses, as probability, the difference in the spatial distribution of tissues in brain images due to individual differences. It is assumed that each voxel belongs to any of the tissues, and that the existence probability of each tissue is known in advance according to its spatial location. 
     Optimal tissue distribution in which the above two assumptions both hold is estimated. The existence probability calculated beforehand for each voxel for the respective tissues of gray matter, white matter, and cerebrospinal fluid from the brain images of many healthy subjects is used as a template, whereby a brain image in which gray matter tissue is extracted three-dimensionally can be obtained. If the gray matter tissue is separated by voxel units based on the existence probability, fine irregularities occur on the boundary surface etc., resulting in an unnatural shape. 
     Accordingly, in step S 213 , image smoothing (first image smoothing) is performed on the gray matter brain image from which the gray matter tissue is extracted. In this step, for the purpose of improving the S/N ratio of the image and making the smoothness of the image equal to that of a template image used for the next second standardization, the image is smoothed by a three-dimensional Gaussian kernel. The half-value width (FWHM) of the filter used for smoothing is, for example, about 8 mm. As specific processing, the three-dimensional convolution of a three-dimensional brain image and a three-dimensional Gaussian function is performed. This can be done by sequentially performing one-dimensional convolution in the x, y, and z directions. 
     As a result of the processing of S 211  to S 213  described above, the region separation unit  122 ′ separates the gray matter tissue from the brain image D 1 ′. 
     Next, in step S 22 ′, the image correction unit  123 ′ deforms the gray matter brain image separated and smoothed in step  21 ′ to match a standardized template (hereinafter referred to as the “template”), so that it can be accurately segmented into regions of interest in the subsequent processing steps. Specifically, the image correction unit  123 ′ performs the processing of steps S 221  to S 223  shown in  FIG.  18 ( b ) . 
     In step S 221 , deformation called “anatomic standardization” is applied to the smoothed gray matter brain image, and global correction for the size of the entire brain and local correction for the size of part of the brain are performed in order to absorb differences in the anatomical shape and size of the gray matter brain image due to individual differences. 
     Specifically, using linear and nonlinear transformations, the smoothed gray matter brain image is subjected to image processing to minimize the sum of squares of errors from the template. The gray matter brain image template used here is the average image obtained from brain images from which gray matter tissue is extracted from many healthy subjects. In this anatomic standardization processing, linear transformation is first used for global correction of position, size, and angle, and then nonlinear transformation is used for correction of local shapes such as irregularities. The linear transformation performed here is affine transformation as in step S 211 . The nonlinear transformation transforms the original image by nonlinear transformation using DARTEL in each of the x and y directions. 
     In step S 222 , the gray matter brain image deformed by anatomic standardization (hereinafter also referred to as the standardized brain image) is subjected to voxel-segmentation again and then subjected to image smoothing (second image smoothing). This processing is to improve the S/N ratio of the above standardized brain image and to make the smoothness of the image equal to that of the images of healthy subjects, which will be used as a standard for later comparison. The half-value width (FWHM) of the filter is about 12 to 15 mm, for example. Specifically, this processing can be achieved in the same manner as in the first image smoothing process in step S 213 , except for the difference in the FWHM value. By performing image smoothing again in this way, individual differences that do not perfectly match in the anatomic standardization processing can be reduced. 
     In step S 223 , concentration correction is performed on the standardized brain image after the second image smoothing. The voxel concentration value, which corresponds to the pixel value in units of voxels, is corrected. A constant value is added to or subtracted from the voxel value of the standardized brain image so that the average voxel value of the standardized brain image matches the average voxel value of the gray matter brain imaging template. 
     As a result of the processing of S 221  to S 223  described above, the image correction unit  123 ′ corrects the gray matter brain image to match the template. 
     In step S 23 ′, the region-of-interest setting unit  124 ′ sets N-number of regions of interest (ROI) in the gray matter separated by the region separation unit  123 ′. In the present embodiment, 290 regions of interest (N=290) obtained by segmentation based on four atlases are set. The four atlases are 108 automated anatomical labeling (AAL) regions, 8 regions such as the entorhinal area related to Alzheimer&#39;s disease prepared by the present inventor, 118 Brodmann regions, and 56 Loni Probabilistic Brain Atlas 40 (LPBA40) regions. A total of 290 of these regions are designated as the regions of interest. 
     In step S 24 ′, the volume calculation unit  125 ′ calculates the volume X of each region of interest. In the present embodiment, the gray matter separated from the brain image is partially or fully compressed or extended by anatomic standardization processing. At this time, the color of the compressed part of the image becomes white, and the color of the extended part becomes black. The volume calculation unit  125 ′ corrects the volume of each region of interest in the anatomically standardized gray matter based on the concentration of the image to calculate the original volume in a space corresponding to each region of interest before standardization. 
     However, it has been confirmed that the volume of each region of interest calculated by the volume calculation unit  125 ′ is biased due to the age and intracranial volume. Accordingly, in step S 25 ′, correction calculation is performed in the covariate correction unit  126 ′ to avoid the influence of those biases. The value obtained by this covariate correction is used as the X-value to be able to evaluate the atrophy state of gray matter tissue in each region of interest under the same conditions. 
     Not surprisingly, the gray matter tissues in the teacher data have a bias that is not present in the gray matter tissues of healthy subjects. This bias is a feature of Alzheimer&#39;s disease. Accordingly, in the present embodiment, the X-values of the regions of interest of the gray matter tissues of healthy subjects are used as standard reference data, and the X-values of the regions of interest of the gray matter tissues in the teacher data are used as comparison data, so that features of the teacher data can be obtained. The distribution of the region-of-interest X-values of the gray matter tissues of healthy subjects is known to be normal distribution if the sample size is large enough, and its normal distribution can be defined by the mean value μ and standard deviation σ. 
     Accordingly, in the present embodiment, before machine learning using the teacher data is executed, the mean value μ and standard deviation σ are calculated for each region of interest to identify the normal distribution state of the X-value for each region of interest using the aforementioned processing step for brain image data of healthy subjects obtained from the IXI database beforehand. By identifying these 290 pairs of mean values μ and standard deviation σ, it is possible to convert the X-values to z-values in machine learning and diagnosis assistance processing. The calculated mean values μ and standard deviation values σ may be stored in the auxiliary storage device  11  etc. 
     Further, the z-value calculation unit  127 ′ calculates the z-value from the X-value for each region of interest in the brain image of the ADNC patient based on the mean value μ and standard deviation σ for each region of interest. Specifically, the z-value is calculated by assigning the X-value, mean value p, and standard deviation σ to the following equation: 
         z =( X −μ)/σ
 
     In step S 26 ′, the data including the regions of interest in the brain image of the ADNC patient and the z-values are associated with the diagnosis result D 2  to generate teacher data D 3 ′. 
     S 2 ′ shown in  FIG.  16    is terminated by steps S 21 ′ to S 26 ′ described above. The generated teacher data D 3 ′ is stored in the auxiliary storage device  11 , and steps S 1 ′ and S 2 ′ are repeated until the teacher data D 3 ′ is sufficiently stored in the auxiliary storage device  11  (YES in step S 3 ). 
     Subsequently, in step S 4 , the first learning unit  131  learns a prediction algorithm D 4 ′ (SVMst) based on the teacher data D 3 ′ stored in the auxiliary storage device  11 . In step S 5 , the second learning unit  132  performs additional learning by inputting scores of the Mini-Mental State Examination into the prediction algorithm D 4 ′ to generate a prediction algorithm D 5 ′ (SVMcog). 
     Diagnosis Assistance Device 
     The form of disease determination using the learned prediction algorithm D 4 ′ is described below. 
       FIG.  19    is a block diagram showing the function of a diagnosis assistance device  2 ′ according to the present embodiment. The hardware configurations of the diagnosis assistance device  2 ′ may be the same as those of the diagnosis assistance device  2  shown in  FIG.  1   . 
     The diagnosis assistance device  2 ′ has the function of predicting the possibility that a subject will develop Alzheimer&#39;s disease within a prescribed period (e.g., within 5 years) (i.e., the possibility that the subject is ADNC spectrum), based on the brain image of the subject. In order to achieve this function, the diagnosis assistance device  2 ′ comprises an image processing unit  22 ′ and a prediction unit  23  as function blocks. 
     The image processing unit  22 ′ separates gray matter from the brain image acquired from the outside, sets multiple regions of interest in the gray matter, performs arithmetic processing, such as calculation of z-values of the regions of interest, and outputs the z-value of each region of interest to the prediction unit  23 . In order to generate the z-value of each region of interest, the image processing unit  22 ′ comprises a brain image acquisition unit  221 , a region separation unit  222 ′, an image correction unit  223 ′, a region-of-interest setting unit  224 ′, a volume calculation unit  225 ′, a covariate correction unit  226 ′, and a z-value calculation unit  227 ′. These function blocks have the same functions as the brain image acquisition unit  121 , region separation unit  123 ′, region-of-interest setting unit  124 ′, volume calculation unit  125 ′, covariate correction unit  126 ′, and z-value calculation unit  127 ′ of the teacher data generation unit  12 ′ shown in  FIG.  15   . 
     The brain image of the subject is acquired by the brain image acquisition unit  221 . Thereafter, each of the region separation unit  223 ′ to the z-value calculation unit  227 ′ performs the processing of steps S 21 ′ to S 26 ′ shown in  FIG.  17    to generate z-values of the regions of interest in the gray matter. 
     The prediction unit  23  predicts the possibility that the subject is ADNC spectrum, according to the prediction algorithm D 4 ′. In the present embodiment, the prediction unit  23  predicts the possibility that the subject is ADNC spectrum, based on the z-value of each region of interest generated by the image processing unit  22 ′. The prediction result is displayed, for example, on a display  4  connected to the diagnosis assistance device  2 ′. 
     After the diagnosis result of the subject is obtained, the brain image data of the subject may be associated with the diagnosis result to generate teacher data, and the teacher data may be used for relearning. This can increase the prediction accuracy of the prediction algorithm as time advances. 
     EXAMPLES 
     Examples of the present invention are described below; however, the present invention is not limited to the following Examples. 
     Example 1 
     In Example 1, the North American ADNI database (NA-ADNI) was used as the diagnosis information database DB shown in  FIG.  2   . The present inventor extracted 1314 cases with existence of MRI brain image data from the NA-ADNI. The breakdown of the cases was as follows: 359 AD patients, 412 MCI patients, and 543 healthy subjects (NL). Among the MCI patients, 284 pMCI patients progressed to AD during follow-up, and 128 sMCI patients could be followed up for over 4 years with no progression to AD. 
     645 cases randomly extracted from the above 1314 cases were used as teacher data, and the prediction algorithm D 4  (SVMst) of  FIG.  2    was learned based on the teacher data D 3 . Similarly, MMSE sores were input into the prediction algorithm D 4  to generate the prediction algorithm D 5  (SVMcog) of  FIG.  2   . 
     The possibility that the subject would be ADNC spectrum was predicted by SVMst and SVMcog using the above teacher data as evaluation data. Specifically, accuracy (Accuracy), sensitivity (Sensitivity), specificity (Specificity), positive predictive value (PPV), negative predictive value (NPV), F1 value, Matthews correlation coefficient (MCC), relative risk (Relative risk), odds after diagnosis (Odds), and area under the curve (AUC; the area of a part under the ROC curve, ranging from 0 to 1. Values closer to 1 indicate higher discrimination ability. When the discrimination ability is random, AUC=0.5) were calculated as indicators of prediction accuracy. The results are shown in Table 1. 
     
       
         
           
               
               
               
             
               
                   
                 TABLE 1 
               
               
                   
                   
               
               
                   
                 SVMst 
                 SVMcog 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
            
               
                   
                 Accuracy (%) 
                 91.3 
                 93.3 
               
               
                   
                 Sensitivity (%) 
                 93.3 
                 92.4 
               
               
                   
                 Specificity (%) 
                 89.4 
                 94.1 
               
               
                   
                 PPV(%) 
                 89.5 
                 93.8 
               
               
                   
                 NPV(%) 
                 93.2 
                 92.7 
               
               
                   
                 F1(%) 
                 91.4 
                 93.1 
               
               
                   
                 MCC(%) 
                 82.7 
                 86.6 
               
               
                   
                 Relative risk 
                 13.2 
                 12.9 
               
               
                   
                 Odds 
                 117.8 
                 194.6 
               
               
                   
                 AUC 
                 0.9595 
                 0.9736 
               
               
                   
                   
               
            
           
         
       
     
     The above results revealed that the possibility of ADNC spectrum could be predicted with high accuracy by using the prediction algorithms D 4  and D 5  of Example 1. 
     Example 2 
     In Example 2, it was evaluated whether the prediction algorithm D 4  (SVMst) and prediction algorithm D 5  (SVMcog) generated in Example 1 were over-learned. Specifically, 669 cases out of the above 1314 cases of the NA-ADNI, except for the 645 cases extracted in Example 1, were used as evaluation data to calculate the prediction accuracy of the possibility that the subject would be ADNC spectrum. The prediction algorithm disclosed in PTL 1 (VSRAD) was also prepared as a comparative example, and the prediction accuracy of VSRAD was similarly calculated. The results are shown in Table 2. 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 2 
               
               
                   
                   
               
               
                   
                 SVMst 
                 SVMcog 
                 VSRAD 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
            
               
                   
                 Accuracy (%) 
                 89.0 
                 91.6 
                 78.6 
               
               
                   
                 Sensitivity (%) 
                 84.7 
                 89.1 
                 78.3 
               
               
                   
                 Specificity (%) 
                 93.1 
                 94.0 
                 78.9 
               
               
                   
                 PPV(%) 
                 92.0 
                 93.3 
                 77.8 
               
               
                   
                 NPV(%) 
                 86.6 
                 90.2 
                 79.4 
               
               
                   
                 F1(%) 
                 88.2 
                 91.2 
                 78.0 
               
               
                   
                 MCC(%) 
                 78.2 
                 83.3 
                 57.2 
               
               
                   
                 Relative risk 
                 6.8 
                 9.5 
                 3.8 
               
               
                   
                 Odds 
                 74.2 
                 128.0 
                 13.5 
               
               
                   
                 AUC 
                 0.9420 
                 0.9681 
                 0.8498 
               
               
                   
                   
               
            
           
         
       
     
     The results demonstrated that the learned prediction algorithms D 4  and D 5  were not over-learned, and that their prediction accuracy was higher than that of VSRAD. 
     Example 3 
     In Example 3, the prediction accuracy of the possibility of AD was evaluated for the prediction algorithm D 4  (SVMst) generated in Example 1. Specifically, in addition to the NA-ADNI, data extracted from the Japanese ADNI database (JADNI) and the Australian ADNI database (AIBL) were also used as evaluation data. VSRAD disclosed in PTL 1 was also prepared as a comparative example, and the same data as the teacher data of Example 1 was analyzed by VSRAD to calculate the prediction accuracy of the learned VSRAD. Table 3 shows the results, including the breakdown of AD and NL in the NA-ADNI, JADNI, and AIBL. 
     
       
         
           
               
               
               
             
               
                   
                 TABLE 3 
               
             
            
               
                   
                   
               
               
                   
                 SVMst 
                 VSRAD 
               
            
           
           
               
               
               
               
               
               
               
            
               
                   
                 NA- 
                   
                   
                 NA- 
                   
                   
               
               
                 Database 
                 ADNI 
                 AIBL 
                 JADNI 
                 ADNI 
                 AIBL 
                 JADNI 
               
               
                   
               
               
                 AD/NL 
                 176/269 
                 72/448 
                 148/152 
                 359/543 
                 72/448 
                 148/152 
               
               
                 Accuracy 
                 89.4 
                 91.5 
                 87.3 
                 80.4 
                 73.0 
                 79.5 
               
               
                 (%) 
               
               
                 Sensitivity 
                 77.8 
                 90.3 
                 85.1 
                 80.5 
                 79.2 
                 89.3 
               
               
                 (%) 
               
               
                 Specificity 
                 97.0 
                 91.7 
                 89.5 
                 80.3 
                 72.0 
                 69.9 
               
               
                 (%) 
               
               
                 PPV(%) 
                 94.5 
                 63.7 
                 83.7 
                 73.0 
                 31.3 
                 74.3 
               
               
                 NPV(%) 
                 87.0 
                 98.3 
                 83.2 
                 86.2 
                 95.5 
                 87.0 
               
               
                 F1(%) 
                 85.4 
                 74.7 
                 86.9 
                 76.6 
                 44.9 
                 81.1 
               
               
                 MCC(%) 
                 78.1 
                 71.3 
                 74.7 
                 60.0 
                 37.1 
                 60.2 
               
               
                 Odds 
                 114.6 
                 103.1 
                 48.7 
                 16.8 
                 9.79 
                 19.34 
               
               
                 AUC 
                 0.9450 
                 0.9506 
                 0.9453 
                 0.8612 
                 0.8423 
                 0.7960 
               
               
                   
               
            
           
         
       
     
     From the results, the learned prediction algorithm D 4  showed higher prediction accuracy also in the prediction of the possibility of AD, in comparison with the conventional prediction algorithm. In addition, the prediction algorithm D 4  showed a similar degree of prediction accuracy even in the evaluation of the data extracted from the multiple databases other than the database used as the teacher data, demonstrating that this algorithm was highly versatile. 
     Example 4 
     In Example 4, the prediction accuracy of the possibility of developing AD within a prescribed period in the presymptomatic stage of ADNC spectrum was examined for the prediction algorithm D 4  (SVMst) and prediction algorithm D 5  (SVMcog) generated in Example 1, and the prediction algorithm (VSRAD) disclosed in PTL 1. The results are shown in Table 4. As a result, in the positive case in VSRAD, the relative risk of developing AD in the future was 1.9 times higher than the negative case, whereas those in SVMst and SVMcog were 3.5 times and 3.6 times, respectively. This revealed that when the subject was predicted to be ADNC spectrum by the prediction algorithms D 4  and D 5 , the risk of developing AD was 3.5 to 3.6 times higher than when the subject was not predicted to be ADNC spectrum. 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 4 
               
               
                   
                   
               
               
                   
                 SVMst 
                 SVMcog 
                 VSRAD 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
            
               
                   
                 Accuracy (%) 
                 86.5 
                 87.0 
                 72.0 
               
               
                   
                 Sensitivity (%) 
                 83.9 
                 84.7 
                 70.8 
               
               
                   
                 Specificity (%) 
                 92.1 
                 92.1 
                 74.6 
               
               
                   
                 PPV(%) 
                 95.8 
                 95.9 
                 85.8 
               
               
                   
                 NPV(%) 
                 72.5 
                 73.4 
                 54.0 
               
               
                   
                 F1(%) 
                 89.5 
                 89.9 
                 77.6 
               
               
                   
                 MCC(%) 
                 72.1 
                 72.9 
                 42.5 
               
               
                   
                 Relative risk 
                 3.5 
                 3.6 
                 1.9 
               
               
                   
                 AUC 
                 0.9271 
                 0.9313 
                 0.7865 
               
               
                   
                   
               
            
           
         
       
     
     The above results revealed that the possibility that ADNC patients would develop Alzheimer&#39;s disease within a prescribed period could be predicted with high accuracy by using the diagnosis assistance device (diagnosis assistance method) of Example 2. 
     Example 5 
     In Example 5, the prediction accuracy of the possibility of AD by the prediction algorithm D 4  (SVMst) generated in Example 1 was compared with that of two radiologists with an experience of over 20 years. Specifically, 100 cases of AD and 100 cases of NL (200 cases in total) were randomly extracted from the NA-ADNI database. Further, MRI brain images of AD and MRI brain images of NL (10 cases each) were presented to the radiologists to learn the diagnosis methods of AD and NL. After a few days, the radiologists were asked to diagnose whether the above 200 cases, including the already presented 20 cases, were AD or NL, then the results of VSRAD were presented, and the radiologists were asked again for diagnosis. In addition, using the above 200 cases as evaluation data, the possibility of AD or NL was predicted by the prediction algorithm D 4 , and its prediction accuracy was calculated. Table 5 shows the comparison between the diagnosis accuracy of the two radiologists and the prediction accuracy of the prediction algorithm D 4 . 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 5 
               
             
            
               
                   
                   
               
               
                   
                 Radiologist 1 
                 Radiologist 2 
                   
               
            
           
           
               
               
               
               
               
               
            
               
                 VSRAD 
                 No 
                 With 
                 No 
                 With 
                 SVMst 
               
               
                 assistance 
                 assistance 
                 assistance 
                 assistance 
                 assistance 
                 — 
               
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                 Accuracy (%) 
                 57.5 
                 70.0 
                 70.0 
                 73.0 
                 90.5 
               
               
                 Sensitivity (%) 
                 57.9 
                 69.2 
                 70.4 
                 76.1 
                 97.6 
               
               
                 Specificity (%) 
                 57.1 
                 70.8 
                 69.6 
                 70.5 
                 85.2 
               
               
                 PPV (%) 
                 55 
                 72 
                 69 
                 67 
                 83 
               
               
                 NPV(%) 
                 60 
                 68 
                 71 
                 79 
                 98 
               
               
                 F1 (%) 
                 56.4 
                 70.6 
                 69.7 
                 71.3 
                 89.7 
               
               
                 MCC (%) 
                 15.0 
                 40.0 
                 40.0 
                 46.3 
                 81.9 
               
               
                 Odds 
                 1.8 
                 5.5 
                 5.4 
                 7.6 
                 239.2 
               
               
                   
               
            
           
         
       
     
     From the above results, for example, the radiologists under the assistance of VSRAD showed a diagnosis accuracy of 70% and 73%, whereas SVMst showed a prediction accuracy of 90.5%. Thus, the prediction accuracy of SVMst was clearly higher than the diagnosis accuracy of the radiologists. 
     Example 6 
     In Example 6, it was examined whether the deposition of brain amyloid-β could be predicted by the diagnosis assistance device (diagnosis assistance method) according to the present invention. Regarding the criteria for the presence or absence of brain amyloid-β deposition, an amyloid-β value of 192 pg/ml or less in the spinal fluid in the NA-ADNI database was defined as positive (present). In the NA-ADNI database, 415 cases were diagnosed as ADNC spectrum by the prediction algorithm D 4  (SVMst) of Example 1, and 90.6% (376 cases) thereof was positive regarding brain amyloid-β deposition. From this, it can be assumed that the diagnosis assistance device (diagnosis assistance method) according to the present invention accurately understood the pathological conditions of AD. 
     Example 7 
     In Example 7, to what extent the prediction algorithm D 4  (SVMst) of Example 1 could predict AD development within a prescribed period was examined using the progression-free survival curve. The targets were pMCI and sMCI cases in the NA-ADNI database. Of these cases, those who underwent biomarker measurement in spinal fluid tests, and AV-45 (amyloid PET) tests were selected.  FIG.  20    shows the number of subjects (n) in each group and the relationship between the number of months elapsed and the ratio of developing AD. A(+) and pT(+) indicate that the spinal fluid biomarkers of amyloid-β and phosphorylated protein are positive, respectively. 
     Table 6 shows the hazard ratio in each biomarker and its confidence intervals. tT(+) indicates that the spinal fluid biomarker of tau protein was positive. ADNC is a group with A(+) and pT(+), and is assumed to have pathological AD clinical conditions. The hazard ratio of ADNC with A(+) and pT(+) was 2.18, whereas the hazard ratio of the case predicted to be positive by the prediction algorithm D 4  (SVMst) of Example 1 was 3.59, indicating that there was a higher risk of developing AD than ADNC patients. 
     
       
         
           
               
               
               
               
               
               
             
               
                   
                 TABLE 6 
               
               
                   
                   
               
               
                   
                   
                   
                 Hazard 
                 Lower 
                 Upper 
               
               
                   
                 Level 1 
                 /Level 2 
                 Ratio 
                 95% 
                 95% 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                 MRI 
                 SVMst (+) 
                 SVMst (−) 
                 3.59 
                 2.87 
                 4.49 
               
               
                   
                 VSRAD(+) 
                 VSRAD(−) 
                 1.86 
                 1.52 
                 2.28 
               
               
                 AV-45 
                 AV-45(+) 
                 AV-45(−) 
                 1.98 
                 1.51 
                 2.59 
               
               
                 CSF 
                 A(+) 
                 A(−) 
                 2.12 
                 1.60 
                 2.81 
               
               
                   
                 tT(+) 
                 tT(−) 
                 1.78 
                 1.39 
                 2.27 
               
               
                   
                 pT(+) 
                 pT(−) 
                 1.70 
                 1.21 
                 2.37 
               
               
                   
                 A(+)pT(+) 
                 A(−)pT(−) 
                 2.18 
                 1.49 
                 3.18 
               
               
                   
                 A(+)pT(+) 
                 A(−)pT(+) 
                 2.17 
                 1.51 
                 3.14 
               
               
                   
                 A(+)pT(−) 
                 A(−)pT(−) 
                 1.41 
                 0.67 
                 2.95 
               
               
                   
                 A(+)pT(+) 
                 A(+)pT(−) 
                 1.55 
                 0.79 
                 3.02 
               
               
                   
                 A(−)pT(+) 
                 A(−)Tp(−) 
                 1.00 
                 0.62 
                 1.62 
               
               
                   
               
            
           
         
       
     
     REFERENCE SIGNS LIST 
     
         
           1 : machine learning device 
           1 ′: machine learning device 
           11 : auxiliary storage device 
           12 : teacher data generation unit 
           12 ′: teacher data generation unit 
           121 : brain image acquisition unit 
           122 : region segmentation unit 
           122 ′: region separation unit 
           123 : image correction unit 
           123 ′: image correction unit 
           124 : region-of-interest setting unit 
           124 ′: region-of-interest setting unit 
           125 : volume calculation unit 
           125 ′: volume calculation unit 
           126 : t-value and p-value calculation unit 
           126 ′: covariate correction unit 
           127 : z-value calculation unit 
           127 ′: z-value calculation unit 
           128 : diagnosis result acquisition unit 
           13 : learning unit 
           131 : first learning unit 
           132 : second learning unit 
           2 : diagnosis assistance device 
           2 ′: diagnosis assistance device 
           21 : auxiliary storage device 
           22 : image processing unit 
           22 ′: image processing unit 
           221 : brain image acquisition unit 
           222 : region segmentation unit 
           222 ′: region separation unit 
           223 : image correction unit 
           223 ′: image correction unit 
           224 : region-of-interest setting unit 
           224 ′: region-of-interest setting unit 
           225 : volume calculation unit 
           225 ′: volume calculation unit 
           226 : t-value and p-value calculation unit 
           226 ′: covariate correction unit 
           227 : z-value calculation unit 
           227 ′: z-value calculation unit 
           23 : prediction unit 
           3 : MRI device 
           4 : display 
         D 1 : brain image 
         D 1 ′: brain image 
         D 2 : diagnosis result 
         D 2 ′: diagnosis result 
         D 3 : teacher data 
         D 3 ′: teacher data 
         D 4 : prediction algorithm 
         D 4 ′: prediction algorithm 
         D 5 : prediction algorithm 
         D 5 ′: prediction algorithm 
         DB: diagnosis information database