METHOD FOR DETERMINING PROBABILITY OF SUBJECT WITH MILD COGNITION IMPAIRMENT DEVELOPING ALZHEIMER'S DISEASE WITHIN PREDETERMINED TIME PERIOD

A method is to be implemented by a computing device that stores a risk assessment model, and includes steps of: obtaining an entry of target physiological data and target magnetic resonance imaging (MRI) images of a brain of a subject with mild cognition impairment (MCI); obtaining, based on the target MRI images, voxel values respectively of primitive voxels that are related to grey matter of the brain of the subject; selecting, from among the primitive voxels, any primitive voxel satisfying a filtering criterion as a selected voxel; calculating an average of the voxel value(s) respectively of the selected voxel(s) to obtain an average target voxel value; and obtaining a probability of the subject developing Alzheimer's disease within a predetermined time period by feeding the average target voxel value and the entry of target physiological data into the risk assessment model.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to Taiwanese Invention Patent Application No. 111143143, filed on Nov. 11, 2022.

FIELD

The disclosure relates to a method for determining a probability of a subject with mild cognition impairment (MCI) developing Alzheimer's disease (AD) within a predetermined time period.

BACKGROUND

Alzheimer's disease (AD) is a neurodegenerative disease and is the main cause of dementia. AD usually starts slowly and worsens progressively. A patient with AD would gradually lose his/her body functions. Currently, no treatment can stop or reverse the progression of AD, and only some treatments may temporarily improve symptoms caused by AD. In order to help people prepare to confront AD as early as possible, a method for determining a probability of developing AD in the future is demanded.

SUMMARY

Therefore, an object of the disclosure is to provide a method for determining a probability of a subject with mild cognition impairment (MCI) developing Alzheimer's disease (AD) within a predetermined time period.

According to the disclosure, the method is to be implemented by a computing device that stores a risk assessment model. The method includes steps of:obtaining an entry of target physiological data that is related to the subject, and a plurality of target magnetic resonance imaging (MRI) images of a brain of the subject;obtaining, based on the target MRI images, a plurality of voxel values respectively of a plurality of primitive voxels that are related to grey matter of the brain of the subject;selecting, from among the primitive voxels, any of the primitive voxels that satisfies a filtering criterion as a selected voxel;calculating an average of the voxel value(s) respectively of the selected voxel(s) to obtain an average target voxel value; andobtaining a probability of the subject developing AD within the predetermined time period by feeding the average target voxel value and the entry of target physiological data into the risk assessment model.

DETAILED DESCRIPTION

Referring toFIG.1, an embodiment of a computing device1according to the disclosure is illustrated. The computing device1may be implemented to be a computing server, a desktop computer, a laptop computer, a notebook computer or a tablet computer, but implementation thereof is not limited to what are disclosed herein and may vary in other embodiments. The computing device1includes a storage medium11, a processor12and a display module13. The processor12is electrically connected to the storage medium11and the display module13.

The processor12may be implemented by a central processing unit (CPU), a microprocessor, a micro control unit (MCU), a system on a chip (SoC), or any circuit configurable/programmable in a software manner and/or hardware manner to implement functionalities discussed in this disclosure.

The storage medium11may be implemented by random access memory (RAM), double data rate synchronous dynamic random access memory (DDR SDRAM), read only memory (ROM), programmable ROM (PROM), flash memory, a hard disk drive (HDD), a solid state disk (SSD), electrically-erasable programmable read-only memory (EEPROM) or any other volatile/non-volatile memory devices, but is not limited thereto.

The storage medium11stores a risk assessment model. The risk assessment model is established by using a machine learning algorithm. In particular, the risk assessment model is established by using a Cox proportional hazards model.

The storage medium11further stores a plurality of training data sets that respectively correspond to a plurality of samples (e.g., patients) with mild cognition impairment (MCI). Each of the training data sets contains an observation time interval (e.g., five years) that is related to the corresponding one of the samples, an entry of training physiological data that is related to the corresponding one of the samples and that was obtained at a start time of the observation time interval, a plurality of training magnetic resonance imaging (MRI) images of a brain of the corresponding one of the samples, and an indicator that indicates whether the corresponding one of the samples had developed Alzheimer's disease (AD) at an end time of the observation time interval. It should be noted that the observation time intervals respectively related to the samples may be non-identical (i.e., may not be the same in time length). For each of the training data sets, the entry of training physiological data includes information related to an age of the corresponding one of the samples at the start time of the observation time interval, a medical history of the corresponding one of the samples in aspects of hypercholesterolemia and diabetes, and a score of a mini-mental state examination (MMSE) performed on the corresponding one of the samples.

For example, an entry of training physiological data related to one of the samples (hereinafter referred to as a examinee) and a plurality of training MRI images of a brain of the examinee were obtained on Jan. 1, 2016, the observation time interval corresponding to the examinee is three years, and the indicator corresponding to the examinee indicates that the examinee has not developed AD as of Jan. 1, 2019 (Jan. 1, 2016 and Jan. 1, 2019 are three years apart). In another example, an entry of training physiological data related to another one of the samples (hereinafter referred to as another examinee) and a plurality of training MRI images of a brain of said another examinee were obtained on Jan. 1, 2016, the observation time interval corresponding to said another examinee is two years, and the indicator corresponding to said another examinee indicates that said another examinee has developed AD as of Jan. 1, 2018 (which is two years from Jan. 1, 2016).

Referring toFIGS.2to7, an embodiment of a method for determining a probability of a subject with MCI developing AD within a predetermined time period (e.g., five years) according to the disclosure is illustrated. The method includes a model-establishing procedure and a risk-assessing procedure. In the model-establishing procedure, the risk assessment model is established based on the training data sets. It should be noted that the predetermined time period is not greater than a greatest one among the observation time intervals respectively contained in the training data sets (hereinafter also referred to as a maximum time limit). Moreover, in this embodiment, a plurality of probabilities of the subject developing AD respectively within a plurality of predetermined successive time periods are to be obtained, and a sum of the predetermined successive time periods is not greater than the maximum time limit. In this embodiment, there are five predetermined successive time periods, but a total number of the predetermined successive time periods is not limited to five. For example, the maximum time limit is five years, and the predetermined successive time periods are respectively within a first year, within a second year, within a third year, within a fourth year and within a fifth year.

Specifically, referring toFIG.2, an embodiment of the model-establishing procedure is illustrated. The model-establishing procedure includes steps601to610delineated below.

It should be noted that steps601to608are performed with respect to each of the training data sets.

In step601, the processor12of the computing device1obtains the entry of training physiological data and the training MRI images, and obtains, based on the training MRI images, a plurality of voxel values respectively of a plurality of preliminary voxels that are related to grey matter of the brain of the corresponding one of the samples.

Specifically, referring toFIG.3, the step of obtaining a plurality of voxel values includes sub-steps601aand601bdelineated below.

In sub-step601a, for each of the training MRI images included in the training data set, the processor12performs image recognition on the training MRI image to determine a plurality of regions of the training MRI image that are the grey matter, white matter and cerebrospinal fluid of the brain of the corresponding one of the samples.

In sub-step601b, for each of the training MRI images included in the training data set, the processor12performs image analysis on the regions of the training MRI image that are the grey matter to obtain the voxel values respectively of the preliminary voxels.

It should be noted that in practice, sub-steps601aand601bare implemented by utilizing a MATLAB toolbox “Data Processing & Analysis for Brain Imaging (DPABI)” with the training MRI images as input.

It should be noted that steps602to604are performed with respect to each of the preliminary voxels.

In step602, based on the observation time intervals, the entries of training physiological data and the indicators of the training data sets, and the voxel value of the preliminary voxel thus obtained, the processor12determines whether the preliminary voxel has a significant impact on AD development. When it is determined that the preliminary voxel has a significant impact on AD development, a procedure flow of the method proceeds to step603to designate the preliminary voxel as a candidate voxel. Otherwise, when it is determined that the preliminary voxel does not have a significant impact on AD development, the procedure flow proceeds to step604to not designate the preliminary voxel as a candidate voxel.

In sub-step602a, the processor12determines a significance value for the preliminary voxel by performing a statistical analysis on the observation time intervals, the entries of training physiological data and the indicators of the training data sets, and the voxel value of the preliminary voxel. In this embodiment, the statistical analysis is Cox survival regression, and the significance value is a p-value obtained by using Cox survival regression.

In sub-step602b, the processor12determines whether the significance value thus determined is less than a significance threshold. The processor12determines that the preliminary voxel has a significant impact on AD development when it is determined that the significance value thus determined is less than the significance threshold. The processor12determines that the preliminary voxel does not have a significant impact on AD development when it is determined that the significance value thus determined is not less than the significance threshold. The significance threshold is exemplarily 0.05, but is not limited thereto.

Following step603, steps605to607are performed with respect to each of the candidate voxels designated in step603. In step605, the processor12determines whether the candidate voxel is critical based on N number of preliminary voxels that are closest to the candidate voxel, where N is an integer not less than two. In this embodiment, N is exemplarily 125, but is not limited thereto. When it is determined that the candidate voxel is critical, the procedure flow proceeds to step606to designate the candidate voxel as a training voxel. On the other hand, when it is determined that the candidate voxel is not critical, the procedure flow proceeds to step607to not designate the candidate voxel as a training voxel.

In sub-step605a, the processor12counts a total number of preliminary voxels that are designated as the candidate voxels among the N number of preliminary voxels that are closest to the candidate voxel.

In sub-step605b, the processor12determines whether the total number of preliminary voxels that are designated as the candidate voxels among the N number of preliminary voxels is greater than a critical threshold. The processor12determines that the candidate voxel is critical when it is determined that the total number of preliminary voxels that are designated as the candidate voxels among the N number of preliminary voxels is greater than the critical threshold. The processor12determines that the candidate voxel is not critical when it is determined that the total number of preliminary voxels that are designated as the candidate voxels among the N number of preliminary voxels is not greater than the critical threshold. The critical threshold is exemplarily 63, but is not limited thereto.

Following step606, in step608, the processor12calculates an average of the voxel values of all of the training voxels that are designated in step606(with respect to all of the preliminary voxels), so as to obtain an average training voxel value.

In step609, the processor12determines a filtering criterion based on the training voxels for all of the training data sets. In this embodiment, the filtering criterion includes positions respectively of the training voxels that are designated in step606.

In step610, the processor12establishes, by using a machine learning algorithm (i.e., a Cox proportional hazards model), the risk assessment model based on the entries of training physiological data, the observation time intervals and the indicators that are contained in the training data sets, and the average training voxel values for all training data sets. It is worth to note that the risk assessment model is established by using the average training voxel values, which are related to the training voxels that are determined to have significant impact on AD development, so accuracy of determination made by using the risk assessment model may be ensured.

Referring toFIG.6, an embodiment of the risk-assessing procedure is illustrated. The risk-assessing procedure includes steps701to706delineated below.

In step701, the processor12obtains an entry of target physiological data that is related to the subject, and a plurality of target MRI images of a brain of the subject. The entry of target physiological data includes information related to an age of the subject, a medical history of the subject in aspects of hypercholesterolemia and diabetes, and a score of an MMSE performed on the subject. It should be noted that the way of obtaining the entry of target physiological data and the target MRI images may be implemented by receiving the same through a wired or wireless communication network from a source terminal (e.g., a database server, not shown), or by receiving the same from an external storage device (e.g., a flash drive, not shown) that is electrically connected to the computing device1, but is not limited to what are disclosed herein and may vary in other embodiments.

In step702, the processor12obtains, based on the target MRI images, a plurality of voxel values respectively of a plurality of primitive voxels that are related to grey matter of the brain of the subject.

In sub-step702a, for each of the target MRI images, the processor12performs image recognition on the target MRI image to determine a plurality of regions of the target MRI image that are the grey matter, white matter and cerebrospinal fluid of the brain.

In sub-step702b, for each of the target MRI images, the processor12performs image analysis on the regions of the target MRI image that are the grey matter to obtain the voxel values respectively of the primitive voxels.

It should be noted that in practice, sub-steps702aand702bare implemented by utilizing a MATLAB toolbox “Data Processing & Analysis for Brain Imaging (DPABI)” with the target MRI images as input.

In step703, the processor12selects, from among the primitive voxels, any of the primitive voxels that satisfies the filtering criterion (i.e., is located at a position corresponding to any one of the positions included in the filtering criterion) determined in step609as a selected voxel.

In step704, the processor12calculates an average of the voxel value(s) respectively of the selected voxel(s), so as to obtain an average target voxel value.

In step705, the processor12obtains the plurality of probabilities of the subject developing AD respectively within the plurality of predetermined successive time periods by feeding the average target voxel value and the entry of target physiological data into the risk assessment model.

In step706, the processor12presents, via the display module13, the probabilities of the subject developing AD respectively within the predetermined successive time periods in a form of a curve graph (e.g., a line chart exemplarily shown inFIG.8) where a curve is plotted to express the probabilities against the predetermined successive time periods. Particularly, the curve graph includes a horizontal axis that represents time and a vertical axis that represents probability. In this way, a medical practitioner is able to view the curve graph to assess a health condition of the subject. For example, in a scenario where the entry of target physiological data and the target MRI images correspond to a health condition of the subject on Dec. 31, 2021, the processor12may utilize the risk assessment model to obtain five probabilities of the subject developing AD respectively within five successive years (i.e., years 2022, 2023, 2024, 2025 and 2026).

To sum up, for the method according to the disclosure, the computing device1derives an average target voxel value from target MRI images of a brain of a subject with MCI, and feeds into the risk assessment model the average target voxel value and an entry of target physiological data that is related to the subject so as to obtain a probability of the subject developing AD within a predetermined time period. When multiple probabilities of the subject developing AD respectively within multiple predetermined successive time periods are obtained, the computing device1presents the probabilities in a form of a curve graph. In this way, people may be able to prepare to deal with issues related to AD and/or to prevent the onset of AD.