# Artificial Intelligence Algorithm-Based Computed Tomography Image of Both Kidneys in Diagnosis of Renal Dysplasia

Authors: Yonghui Liu; Siai Tang
Journal: Computational and Mathematical Methods in Medicine
 (2022)

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## Abstract

The objective of this study was to explore the accuracy of low-dosage computed tomography (CT) images based on the expectation maximization algorithm denoising algorithm (EM algorithm) in the detection and diagnosis of renal dysplasia, so as to provide reasonable research basis for accuracy improvement of clinical diagnosis of renal dysplasia. 120 patients with renal dysplasia in hospital were randomly selected as the research objects, and they were divided into two groups by random number method, with 60 patients in each group. The low-dosage CT images of patients in the control group were not processed (nonalgorithm group), and the low-dosage CT images of patients in the observation group were denoised using the EM algorithm (algorithm group). In addition, it was compared with the results of the comprehensive diagnosis (gold standard) to analyze the accuracy of the diagnosis of the two groups of patients and the consistency with the results of the pathological diagnosis. The results were compared with those of the comprehensive diagnosis (gold standard) to analyze the accuracy of the diagnosis of the two groups of patients. The results showed that the peak signal-to-noise ratio (PSNR) (15.9 dB) of the EM algorithm was higher than the regularized adaptive matching pursuit (RAMP) algorithm (1.69 dB) and the mean filter (4.3 dB) (P<0.05). The time consumption of EM algorithm (21 s) was shorter than that of PWLS algorithm (34 s) and MS-PWLS algorithm (39 s) (P<0.05). The diagnosis accuracy of dysplasia of single kidney, absence of single kidney, horseshoe kidney, and duplex kidney was obviously higher in the algorithm group than the control group (P<0.05), which were 66.67% vs. 90%, 60% vs. 88.89%, 71.42% vs. 100%, and 60% vs. 88.89%, respectively. The incidence of hypertension in patients with autosomal dominant polycystic kidney disease (ADPKD) (56.77%) was much higher than that of the other diseases (P<0.05). After denoising by the EM algorithm, low-dosage CT image could improve the diagnostic accuracy of several types of renal dysplasia except ADPKD, showing certain clinical application value. In addition, ADPKD was easy to cause hypertension.


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## Body

## 1. Introduction

The kidney is the main excretion filter organ of the human body. Once abnormal development occurs, the body’s metabolism will also be disturbed [1]. Renal dysplasia refers to a defect in the kidney during embryonic development, and the structure of the kidney appears abnormal or deformed after birth. Studies have found that the incidence of neonatal congenital renal dysplasia is about 0.2%, while adult renal dysplasia is mostly related to hypertension and cardiovascular disease [2, 3]. At present, the clinically common renal dysplasia diseases mainly include autosomal dominant polycystic kidney disease (ADPKD) [4], dysplasia/absence of single kidney [5, 6], horseshoe kidney [7], and duplex kidney [8]. Among them, ADPKD has the highest incidence. Renal dysplasia will gradually aggravate and transform into chronic kidney disease, and renal failure and uremia may even occur in severe cases [9, 10]. Therefore, early diagnosis and early treatment of renal dysplasia are clinically very important.In renal dysplasia, ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), and intravenous pyelography are more common in diagnostic examinations. Among them, ultrasound examination has become a commonly used examination method for ADPKD due to its simple and convenient operation, low price, and noninvasiveness [11]. CT and MRI examinations are mostly used for dysplasia/absence of single kidney and horseshoe kidney [12] according to the manifestations of the disease. Intravenous pyelography has become the best examination method for duplex kidney because it can clearly display the shape and behavior of the bilateral renal pelvis [13]. In this study, we evaluated the diagnostic effect of CT examination. CT examination can confirm the location of the kidney and can also detect the size and shape of the kidney. When the diameter of the kidney is less than 2 cm, the sensitivity of ultrasound is reduced. Therefore, CT has certain advantages in the examination of horseshoe kidney, dysplasia of single kidney, and absence of single kidney [14].With the rapid development of science and technology, artificial intelligence algorithms are often applied to the processing of medical imaging images, and CT technology has attracted attention due to the problem of high radiation. Therefore, reducing CT radiation has become a research hotspot in recent years. However, the CT technology will cause serious noise pollution in inspection images. Therefore, artificial intelligence denoising algorithms [15] have been proposed to combine these two technologies. The maximum expectation algorithm (EM algorithm) is a type of optimization algorithm that performs maximum likelihood estimation (MLE) through iteration [16]. The algorithm has good convergence and stability, and the computational complexity and storage overhead are very low. After a large number of studies, the EM algorithm is effective and feasible in the reconstruction and denoising of CT images, and the images after reconstruction and denoising are displayed clearly and accurately [17, 18].In this study, CT images based on the EM denoising algorithm were selected to detect and diagnose the renal dysplasia of patients and evaluate the accuracy of the diagnosis, aiming to provide basic reference for improving the clinical accuracy of the renal dysplasia examination method in the diagnosis of the disease.


## 2. Methods

### 2.1. Research Objects

In this study, 120 patients with renal dysplasia who were treated in hospital from March 2018 to March 2021 were randomly selected as the research objects. There were 75 male patients and 45 female patients. The age range of the patients was between 30 and 70 years old, with an average age of52.01±12.89 years old. According to the results of conventional CT examinations, there were 55 patients with ADPKD, 22 patients with dysplasia of single kidney, 19 patients with absence of single kidney, 13 patients with horseshoe kidney, and 12 patients with duplex kidney. All research objects were divided into two groups according to the random number method, which were set as nonalgorithm group and algorithm group, with 60 patients in each group. Among them, the CT images of nonalgorithm group patients are not processed, and the CT images of algorithm group patients are denoised using EM algorithm. In addition, the results were compared with those of comprehensive diagnosis (gold standard) to evaluate its application value in various types of renal dysplasia diseases. This study was approved by medical ethics committee of hospital and patients, and their families had signed the informed consent forms.Patients included in this study had to meet the following criteria: patients who were over 18 years of age; the number of renal cysts in ADPKD patients was2≤thenumberofrenalcysts≤10; the CT image showed good, and there was no defect for detecting the lesion; and patients with complete clinical examination results.Patients meeting below criteria had to be excluded: patients with renal atrophy caused by pyelonephritis, renal vascular disease, etc.; patients with renal tumors; patients with discomfort to CT examination; and patients with impaired consciousness.


### 2.2. Low-Dose CT Image Denoising Based on EM Algorithm

It was supposed that the low-dose CI projection data after system calibration and logarithmic transformation wasY, the ideal data was X, and the additive spatial nonstationary Gaussian noise was n, then, below equation could be acquired:
(1)Y=X+n,(2)X=X1,X2,⋯,XMT,(3)Y=Y1,Y2,⋯,YMT.In the above equations,M represented the number of data. When the noise mean was approximately 0, the nonlinear relationship between the noise variance and the statistical mean of the corresponding data was as follows:
(4)σi2=fiexpuiμ.In the above equation (4), ui represented the mean value of the projection data Xi on the i-th detector; σi2 represented the noise variance of the data Xi; fi was a parameter of the i-th detector, and μ was determined by the configuration of the CT equipment.Low-dose CT images are affected by the phenomenon of “photon starvation” [19], resulting in an extremely low signal-to-noise ratio. Therefore, the projection data has been severely polluted by noise, and the traditional algorithm for solving the extreme value of the objective function is inferior. The EM algorithm solves this problem with a “incomplete data” solution method. This algorithm can not only realize the estimation of the ideal projection image X but also realize the estimation of the parameters β and σi2i=1,2,⋯,M.It was supposed that the number of iterations wast, then, the iteration process of the EM algorithm was as follows:The expected values could be calculated with below equations:(5)Mβ,βt=Elnpx;βy;βt,σt,xt−1,(6)uit=Exiy;βt,σit,xt−1,(7)Mx,xt=Elnpxyy;βt+1,σt+1,xt.The maximization can be realized with equations (8)–(10):
(8)βt+1=argmaxβ≥0Mβ,βt,(9)σi2t+1=fiexpuitμ,(10)xt+1=argmaxxMx,xt.In the above three equations,px;β represented the prior probability density, pxy referred to the posterior probability density, and E represented the expected value.The algorithm could be realized with below operations and calculations:For parameterβ, the below equation can be obtained by calculation:
(11)βt+1=argminβE−ln−1βMzexp−Ux2β2y,βt,σt.After a serious of calculation, the final iterative expression ofβ was given as follows:
(12)βt+12=EUxy,βt,σtM.In equation (12) above, U represented the smoothness of adjacent pixels, and z represented a constant that had nothing to do with β.Forσi2t,i=1,2,⋯,M, the below equation can be get by a series of calculations:
(13)σi2t+1=fiexpExiy;βt,σit,xt−1μ.For the ideal imageX¯¯, the below equation can be get by a series of calculations:
(14)xt+1=argmaxx≥0E∑iMyi−xi22σi2+12β2∑i=1M∑j∈Nixi−xj2y;βt+1,σt+1,xt.In equation (14) above, Ni represented the neighborhood set of pixel i, and j referred to the number of neighborhood sets. The optimal iterative expression of xii=1,2,⋯,M was obtained through the iterated conditional modes (ICM) algorithm:
(15)xit+1=β2t+1+σ2t+1∑j∈NiExiy;βt+1,σt+1,xitβ2t+1+aσ2t+1.In the above equation,a represented the number of pixels adjacent to xi.The observation showed that when solving forβ2, σi2, and xi, the calculation process is related to Ux or xi mathematical expectation under the condition of y,βt,σit,xt−1, then
(16)EUxy,βt,σit=∫Uxpxy;βt,σit,xt−1dx,(17)Exiy,βt,σi2t=∫xipxiy;βt,σit,xt−1dxi.Due to the large computational difficulty and complexity of the imagesx and xi, the Gauss Markov Random Field (GMRF) was used as the Gaussian function, so the local posteriori probability density function (a posteriori local pdf) of the ideal image x can be expressed as follows:
(18)pxiy,β,σ;xi,j∈Ni=12πviexp−12vi2xi−αi2.In equation (18) above, vi represented the mean value of the Gaussian distribution, and αi represented the mean square error of the Gaussian distribution. Then, combine the above formula with the posterior probability model of the projected image to perform a series of calculations, and two equations below could be obtained:
(19)αi=1/2β2∑j∈Nixj+yi/4σi2∑j∈Ni1/2β2+1/4σi2,(20)vi=∑j∈Ni2β2+1σi2.After the samplex was incorporated in equations (12), (13), and (15), the below equations could be obtained:
(21)βt+12≈∑k=1kUxkt−1KM,(22)σi2t+1≈fiexp1μk∑k=1kxikt−1,(23)xit+1=β2t+1yi+σi2t+1/K∑j∈Ni∑k=1kxiktβ2t+1+aσ2t+1.Thek in the above equations represented the number of samples.The peak signal to noise ratio (PSNR) was used to evaluate the denoising effect of the EM algorithm, and the calculation expression was as follows:(24)PSNR=10log1022521/N1×N2∑i=0N1∑j=0N2Gi,j−Fi,j2.In equation (24) above, F represented the original image, G represented the image after denoising and reconstruction, and N1 and N2 referred to the width and height of the image, respectively.


### 2.3. CT Examination

All patients in this study were inspected with speed dual CT scanner and were examined in a supine position. The scanning parameters were set as follows: voltage was 120 kv, current was 130 mA, layer thickness was 7 mm, and the layer distance was 10 mm.The enhanced scan was performed as follows: the contrast agent Uweixian was intravenously injected (specification: 300 mol/mL, dose: 1.5 ~ 2.0 mL/kg, injection speed: 1.5 ~ 2.0 mL/s). The whole abdomen of the patient was scanned, and the delay scan was performed after 15~30 minutes.All patients used the same instrument for examination, and the same doctor performed the examination. The examination results were reviewed by two chief physicians with rich clinical experience and advanced qualifications (with more than 20 years of work experience).


### 2.4. Observation Indicators

The results were compared with the results of comprehensive diagnosis to analyze the diagnostic accuracy of the two groups of inspection methods for each type of renal dysplasia. The consistency of the two sets of inspection methods was analyzed to calculate the Kappa value for the diagnosis of various types of renal dysplasia. Different Kappa values had different meanings, as shown in Table1 for details.Table 1
Meaning of diagnostic consistency of Kappa value.
Kappa valueMeaningKappa=1The diagnosis result was exactly the same.Kappa=−1Diagnosis result was completely inconsistent.Kappa≥0.75The consistency of the diagnosis was quite satisfactory.0.75>Kappa≥0.4The consistency of the diagnosis was relatively satisfactory.Kappa<0.4The consistency of the diagnosis was not ideal.In order to understand the relationship between renal dysplasia and hypertension and other diseases, it would monitor patients with a cardiac color Doppler ultrasound machine (GE E9) and a bedside machine (GE vivid I), and observe whether there was a connection between renal dysplasia and the changes of blood pressure.


### 2.5. Statistical Analysis

SPSS22.0 statistical software was used to analyze the data, and the measurement data conforming to the normal distribution were expressed asmean±standarddeviation (X¯±s), with two decimal places. The comparison between the two groups was performed by paired t test, and the comparison between the two groups was performed by independent sample t test. Quantitative data conforming to a skewed distribution are represented by the median (range), and the Wilcixon rank-sum test was used for comparison between the two groups. Enumeration data was expressed as a percentage (%), with 2 decimal place; and the comparison between the two groups adopts the continuity correction of the x2 test. The P value retained 3 decimal places, and the difference was statistically significant when P<0.05.


## 2.1. Research Objects

In this study, 120 patients with renal dysplasia who were treated in hospital from March 2018 to March 2021 were randomly selected as the research objects. There were 75 male patients and 45 female patients. The age range of the patients was between 30 and 70 years old, with an average age of52.01±12.89 years old. According to the results of conventional CT examinations, there were 55 patients with ADPKD, 22 patients with dysplasia of single kidney, 19 patients with absence of single kidney, 13 patients with horseshoe kidney, and 12 patients with duplex kidney. All research objects were divided into two groups according to the random number method, which were set as nonalgorithm group and algorithm group, with 60 patients in each group. Among them, the CT images of nonalgorithm group patients are not processed, and the CT images of algorithm group patients are denoised using EM algorithm. In addition, the results were compared with those of comprehensive diagnosis (gold standard) to evaluate its application value in various types of renal dysplasia diseases. This study was approved by medical ethics committee of hospital and patients, and their families had signed the informed consent forms.Patients included in this study had to meet the following criteria: patients who were over 18 years of age; the number of renal cysts in ADPKD patients was2≤thenumberofrenalcysts≤10; the CT image showed good, and there was no defect for detecting the lesion; and patients with complete clinical examination results.Patients meeting below criteria had to be excluded: patients with renal atrophy caused by pyelonephritis, renal vascular disease, etc.; patients with renal tumors; patients with discomfort to CT examination; and patients with impaired consciousness.


## 2.2. Low-Dose CT Image Denoising Based on EM Algorithm

It was supposed that the low-dose CI projection data after system calibration and logarithmic transformation wasY, the ideal data was X, and the additive spatial nonstationary Gaussian noise was n, then, below equation could be acquired:
(1)Y=X+n,(2)X=X1,X2,⋯,XMT,(3)Y=Y1,Y2,⋯,YMT.In the above equations,M represented the number of data. When the noise mean was approximately 0, the nonlinear relationship between the noise variance and the statistical mean of the corresponding data was as follows:
(4)σi2=fiexpuiμ.In the above equation (4), ui represented the mean value of the projection data Xi on the i-th detector; σi2 represented the noise variance of the data Xi; fi was a parameter of the i-th detector, and μ was determined by the configuration of the CT equipment.Low-dose CT images are affected by the phenomenon of “photon starvation” [19], resulting in an extremely low signal-to-noise ratio. Therefore, the projection data has been severely polluted by noise, and the traditional algorithm for solving the extreme value of the objective function is inferior. The EM algorithm solves this problem with a “incomplete data” solution method. This algorithm can not only realize the estimation of the ideal projection image X but also realize the estimation of the parameters β and σi2i=1,2,⋯,M.It was supposed that the number of iterations wast, then, the iteration process of the EM algorithm was as follows:The expected values could be calculated with below equations:(5)Mβ,βt=Elnpx;βy;βt,σt,xt−1,(6)uit=Exiy;βt,σit,xt−1,(7)Mx,xt=Elnpxyy;βt+1,σt+1,xt.The maximization can be realized with equations (8)–(10):
(8)βt+1=argmaxβ≥0Mβ,βt,(9)σi2t+1=fiexpuitμ,(10)xt+1=argmaxxMx,xt.In the above three equations,px;β represented the prior probability density, pxy referred to the posterior probability density, and E represented the expected value.The algorithm could be realized with below operations and calculations:For parameterβ, the below equation can be obtained by calculation:
(11)βt+1=argminβE−ln−1βMzexp−Ux2β2y,βt,σt.After a serious of calculation, the final iterative expression ofβ was given as follows:
(12)βt+12=EUxy,βt,σtM.In equation (12) above, U represented the smoothness of adjacent pixels, and z represented a constant that had nothing to do with β.Forσi2t,i=1,2,⋯,M, the below equation can be get by a series of calculations:
(13)σi2t+1=fiexpExiy;βt,σit,xt−1μ.For the ideal imageX¯¯, the below equation can be get by a series of calculations:
(14)xt+1=argmaxx≥0E∑iMyi−xi22σi2+12β2∑i=1M∑j∈Nixi−xj2y;βt+1,σt+1,xt.In equation (14) above, Ni represented the neighborhood set of pixel i, and j referred to the number of neighborhood sets. The optimal iterative expression of xii=1,2,⋯,M was obtained through the iterated conditional modes (ICM) algorithm:
(15)xit+1=β2t+1+σ2t+1∑j∈NiExiy;βt+1,σt+1,xitβ2t+1+aσ2t+1.In the above equation,a represented the number of pixels adjacent to xi.The observation showed that when solving forβ2, σi2, and xi, the calculation process is related to Ux or xi mathematical expectation under the condition of y,βt,σit,xt−1, then
(16)EUxy,βt,σit=∫Uxpxy;βt,σit,xt−1dx,(17)Exiy,βt,σi2t=∫xipxiy;βt,σit,xt−1dxi.Due to the large computational difficulty and complexity of the imagesx and xi, the Gauss Markov Random Field (GMRF) was used as the Gaussian function, so the local posteriori probability density function (a posteriori local pdf) of the ideal image x can be expressed as follows:
(18)pxiy,β,σ;xi,j∈Ni=12πviexp−12vi2xi−αi2.In equation (18) above, vi represented the mean value of the Gaussian distribution, and αi represented the mean square error of the Gaussian distribution. Then, combine the above formula with the posterior probability model of the projected image to perform a series of calculations, and two equations below could be obtained:
(19)αi=1/2β2∑j∈Nixj+yi/4σi2∑j∈Ni1/2β2+1/4σi2,(20)vi=∑j∈Ni2β2+1σi2.After the samplex was incorporated in equations (12), (13), and (15), the below equations could be obtained:
(21)βt+12≈∑k=1kUxkt−1KM,(22)σi2t+1≈fiexp1μk∑k=1kxikt−1,(23)xit+1=β2t+1yi+σi2t+1/K∑j∈Ni∑k=1kxiktβ2t+1+aσ2t+1.Thek in the above equations represented the number of samples.The peak signal to noise ratio (PSNR) was used to evaluate the denoising effect of the EM algorithm, and the calculation expression was as follows:(24)PSNR=10log1022521/N1×N2∑i=0N1∑j=0N2Gi,j−Fi,j2.In equation (24) above, F represented the original image, G represented the image after denoising and reconstruction, and N1 and N2 referred to the width and height of the image, respectively.


## 2.3. CT Examination

All patients in this study were inspected with speed dual CT scanner and were examined in a supine position. The scanning parameters were set as follows: voltage was 120 kv, current was 130 mA, layer thickness was 7 mm, and the layer distance was 10 mm.The enhanced scan was performed as follows: the contrast agent Uweixian was intravenously injected (specification: 300 mol/mL, dose: 1.5 ~ 2.0 mL/kg, injection speed: 1.5 ~ 2.0 mL/s). The whole abdomen of the patient was scanned, and the delay scan was performed after 15~30 minutes.All patients used the same instrument for examination, and the same doctor performed the examination. The examination results were reviewed by two chief physicians with rich clinical experience and advanced qualifications (with more than 20 years of work experience).


## 2.4. Observation Indicators

The results were compared with the results of comprehensive diagnosis to analyze the diagnostic accuracy of the two groups of inspection methods for each type of renal dysplasia. The consistency of the two sets of inspection methods was analyzed to calculate the Kappa value for the diagnosis of various types of renal dysplasia. Different Kappa values had different meanings, as shown in Table1 for details.Table 1
Meaning of diagnostic consistency of Kappa value.
Kappa valueMeaningKappa=1The diagnosis result was exactly the same.Kappa=−1Diagnosis result was completely inconsistent.Kappa≥0.75The consistency of the diagnosis was quite satisfactory.0.75>Kappa≥0.4The consistency of the diagnosis was relatively satisfactory.Kappa<0.4The consistency of the diagnosis was not ideal.In order to understand the relationship between renal dysplasia and hypertension and other diseases, it would monitor patients with a cardiac color Doppler ultrasound machine (GE E9) and a bedside machine (GE vivid I), and observe whether there was a connection between renal dysplasia and the changes of blood pressure.


## 2.5. Statistical Analysis

SPSS22.0 statistical software was used to analyze the data, and the measurement data conforming to the normal distribution were expressed asmean±standarddeviation (X¯±s), with two decimal places. The comparison between the two groups was performed by paired t test, and the comparison between the two groups was performed by independent sample t test. Quantitative data conforming to a skewed distribution are represented by the median (range), and the Wilcixon rank-sum test was used for comparison between the two groups. Enumeration data was expressed as a percentage (%), with 2 decimal place; and the comparison between the two groups adopts the continuity correction of the x2 test. The P value retained 3 decimal places, and the difference was statistically significant when P<0.05.


## 3. Results

### 3.1. The Denoising Effect of EM Algorithm

Figure1 showed the comparison on denoising effects of the EM algorithm and regularized adaptive matching pursuit (RAMP) algorithm [20], mean filter [21], penalized weighted least squares (PWLS) [22], and multiscale PWLS (MS-PWLS) [23]. As given in Figure 2, PSNR (15.9 dB) of the EM algorithm was higher than that of the RAMP algorithm and mean filter (P<0.05), but the results of the PWLS algorithm and MS-PWLS algorithm were not much different (P>0.05). At the same time, the time consumption of EM algorithm, PWLS algorithm, and MS-PWLS algorithm when processing images was further compared, as shown in Figure 3. Through observation, it was found that the time consumption of EM algorithm was shorter than that of PWLS algorithm and MS-PWLS algorithm (P<0.05).Figure 1
Comparison on PSNR. Note: “∗” meant the comparison was statistically significant (P<0.05).Figure 2
Denoising effects of different types of renal dysplasia. Note: images in column A ~ F showed the images of normal kidney, ADPKD, dysplasia of single kidney, absence of single kidney, horseshoe kidney, and duplex kidney, respectively.Figure 3
Comparison on time consumption of denoising of different algorithms. Note: “∗” meant the comparison was statistically significant (P<0.05).


### 3.2. Comparison on Basic Data of Patients

After statistical analysis, the gender distribution of the two groups of patients showed there were 39 males and 21 females in the nonalgorithm group and 36 males and 24 females in the algorithm group. After comparison, there was no significant statistical difference between the distribution of males and females in the two groups (P>0.05). The average age of the nonalgorithm group was 53.91±12.73 years old and that in the algorithm group was 52.33±15.01 years old, showing no significant difference (P>0.05). In terms of disease distribution, there were 29 patients with ADPKD, 12 patients with dysplasia of single kidney, 10 patients with absence of single kidney, 7 patients with horseshoe kidney, and 6 patients with duplex kidney in the nonalgorithm group. There were 26 patients with ADPKD, 10 patients with dysplasia of single kidney, 9 patients with absence of single kidney, 6 patients with horseshoe kidney, and 6 patients with duplex kidney in the algorithm group. There was no great difference in the disease distribution between two groups (P>0.05). The details were shown in Figure 4.Figure 4
Disease type distribution. Note: A ~ E in the above figure referred to ADPKD, dysplasia of single kidney, absence of single kidney, horseshoe kidney, and duplex kidney, respectively.


### 3.3. Diagnostic Accuracy of Different Types of Renal Dysplasia

Table2 was the comparative statistics on CT examination diagnosis results of ADPKD and the comprehensive diagnosis results. It was calculated that the accuracy of nonalgorithm group for 29 patients with ADPKD was 62.07%; the accuracy of CT image based on EM algorithm for 29 patients with ADPKD in the algorithm group was 69.23%, showing no obviously statistically significant (P>0.05).Table 2
Statistics on diagnostic accuracy of ADPKD.
GroupsComprehensive diagnosis (cases)Total (cases)ADPKDNon-ADPKDNonalgorithm group(n=29 cases)181129Algorithm group(n=26 cases)18826Table3 was the comparative statistics on CT examination diagnosis results of dysplasia of single kidney and the comprehensive diagnosis results. It was calculated that the accuracy of nonalgorithm group for 12 patients with dysplasia of single kidney was 66.67%; the accuracy of CT image based on EM algorithm for 10 patients with dysplasia of single kidney in the algorithm group was 90.00%, showing statistically significant difference (P<0.05).Table 3
Statistics on diagnostic accuracy of dysplasia of single kidney.
GroupsComprehensive diagnosis (cases)Total (cases)Dysplasia of single kidneyNondysplasia of single kidneyNonalgorithm group(n=12 cases)8412Algorithm group(n=10 cases)9110Table4 was the comparative statistics on CT examination diagnosis results of absence of single kidney and the comprehensive diagnosis results. It was calculated that the accuracy of nonalgorithm group for 10 patients with absence of single kidney was 60.00%; the accuracy of CT image based on EM algorithm for 9 patients with absence of single kidney in the algorithm group was 88.89%, showing statistically significant difference (P<0.05).Table 4
Statistics on diagnostic accuracy of absence of single kidney.
GroupsComprehensive diagnosis (cases)Total (cases)Absence of single kidneyNonabsence of single kidneyNonalgorithm group(n=10 cases)7310Algorithm group(n=9 cases)819Table5 was the comparative statistics on CT examination diagnosis results of horseshoe kidney and the comprehensive diagnosis results. It was calculated that the accuracy of nonalgorithm group for 7 patients with horseshoe kidney was 71.42%; the accuracy of CT image based on EM algorithm for 6 patients with horseshoe kidney in the algorithm group was 100.00%, showing statistically significant difference (P<0.05).Table 5
Statistics on diagnostic accuracy of horseshoe kidney.
GroupsComprehensive diagnosis (cases)Total (cases)Horseshoe kidneyNonhorseshoe kidneyNonalgorithm group(n=7 cases)527Algorithm group(n=6 cases)606Table6 was the comparative statistics on CT examination diagnosis results of duplex kidney and the comprehensive diagnosis results. It was calculated that the accuracy of nonalgorithm group for 6 patients with duplex kidney was 50.00%; the accuracy of CT image based on EM algorithm for 6 patients with duplex kidney in the algorithm group was 85.33%, showing statistically significant difference (P<0.05).Table 6
Statistics on diagnostic accuracy of duplex kidney.
GroupsComprehensive diagnosis (cases)TotalDuplex kidneyNonduplex kidneyNonalgorithm group(n=6 cases)336Algorithm group(n=6 cases)516


### 3.4. Diagnostic Consistency of Various Renal Dysplasia

Figure5 showed the consistency comparison between the CT diagnosis results and the comprehensive diagnosis of various types of renal dysplasia in the two groups. The comparison found that, except for ADPKD, the consistency of the diagnostic results of all types of renal dysplasia in the algorithm group was higher than that of the nonalgorithm group, and the comparison was statistically significant (P<0.05).Figure 5
Comparison on Kappa values on diagnosis of various renal dysplasia. (A: ADPKD; B: single kidney dysplasia; C: single kidney loss; D: horseshoe kidney; E: duplicate kidney). “∗” meant the comparison was statistically significant (P<0.05).


### 3.5. Correlation between Various Types of Renal Dysplasia and Hypertension

Figure6 showed the comparison of the probability of hypertension in patients with various types of renal dysplasia. It was observed that the probability of hypertension in ADPKD patients was 56.77%, and the probabilities in patients with dysplasia of single kidney, absence of single kidney, horseshoe kidney, and duplex kidney were all below 20%. The incidence of hypertension in ADPKD was much higher than that of other syndromes, and there was statistical significance (P<0.05).Figure 6
Comparison of the probability of hypertension in patients with various types of renal dysplasia. (A: ADPKD; B: single kidney dysplasia; C: single kidney loss; D: horseshoe kidney; E: duplicate kidney). “∗” meant the comparison was statistically significant (P<0.05).


## 3.1. The Denoising Effect of EM Algorithm

Figure1 showed the comparison on denoising effects of the EM algorithm and regularized adaptive matching pursuit (RAMP) algorithm [20], mean filter [21], penalized weighted least squares (PWLS) [22], and multiscale PWLS (MS-PWLS) [23]. As given in Figure 2, PSNR (15.9 dB) of the EM algorithm was higher than that of the RAMP algorithm and mean filter (P<0.05), but the results of the PWLS algorithm and MS-PWLS algorithm were not much different (P>0.05). At the same time, the time consumption of EM algorithm, PWLS algorithm, and MS-PWLS algorithm when processing images was further compared, as shown in Figure 3. Through observation, it was found that the time consumption of EM algorithm was shorter than that of PWLS algorithm and MS-PWLS algorithm (P<0.05).Figure 1
Comparison on PSNR. Note: “∗” meant the comparison was statistically significant (P<0.05).Figure 2
Denoising effects of different types of renal dysplasia. Note: images in column A ~ F showed the images of normal kidney, ADPKD, dysplasia of single kidney, absence of single kidney, horseshoe kidney, and duplex kidney, respectively.Figure 3
Comparison on time consumption of denoising of different algorithms. Note: “∗” meant the comparison was statistically significant (P<0.05).


## 3.2. Comparison on Basic Data of Patients

After statistical analysis, the gender distribution of the two groups of patients showed there were 39 males and 21 females in the nonalgorithm group and 36 males and 24 females in the algorithm group. After comparison, there was no significant statistical difference between the distribution of males and females in the two groups (P>0.05). The average age of the nonalgorithm group was 53.91±12.73 years old and that in the algorithm group was 52.33±15.01 years old, showing no significant difference (P>0.05). In terms of disease distribution, there were 29 patients with ADPKD, 12 patients with dysplasia of single kidney, 10 patients with absence of single kidney, 7 patients with horseshoe kidney, and 6 patients with duplex kidney in the nonalgorithm group. There were 26 patients with ADPKD, 10 patients with dysplasia of single kidney, 9 patients with absence of single kidney, 6 patients with horseshoe kidney, and 6 patients with duplex kidney in the algorithm group. There was no great difference in the disease distribution between two groups (P>0.05). The details were shown in Figure 4.Figure 4
Disease type distribution. Note: A ~ E in the above figure referred to ADPKD, dysplasia of single kidney, absence of single kidney, horseshoe kidney, and duplex kidney, respectively.


## 3.3. Diagnostic Accuracy of Different Types of Renal Dysplasia

Table2 was the comparative statistics on CT examination diagnosis results of ADPKD and the comprehensive diagnosis results. It was calculated that the accuracy of nonalgorithm group for 29 patients with ADPKD was 62.07%; the accuracy of CT image based on EM algorithm for 29 patients with ADPKD in the algorithm group was 69.23%, showing no obviously statistically significant (P>0.05).Table 2
Statistics on diagnostic accuracy of ADPKD.
GroupsComprehensive diagnosis (cases)Total (cases)ADPKDNon-ADPKDNonalgorithm group(n=29 cases)181129Algorithm group(n=26 cases)18826Table3 was the comparative statistics on CT examination diagnosis results of dysplasia of single kidney and the comprehensive diagnosis results. It was calculated that the accuracy of nonalgorithm group for 12 patients with dysplasia of single kidney was 66.67%; the accuracy of CT image based on EM algorithm for 10 patients with dysplasia of single kidney in the algorithm group was 90.00%, showing statistically significant difference (P<0.05).Table 3
Statistics on diagnostic accuracy of dysplasia of single kidney.
GroupsComprehensive diagnosis (cases)Total (cases)Dysplasia of single kidneyNondysplasia of single kidneyNonalgorithm group(n=12 cases)8412Algorithm group(n=10 cases)9110Table4 was the comparative statistics on CT examination diagnosis results of absence of single kidney and the comprehensive diagnosis results. It was calculated that the accuracy of nonalgorithm group for 10 patients with absence of single kidney was 60.00%; the accuracy of CT image based on EM algorithm for 9 patients with absence of single kidney in the algorithm group was 88.89%, showing statistically significant difference (P<0.05).Table 4
Statistics on diagnostic accuracy of absence of single kidney.
GroupsComprehensive diagnosis (cases)Total (cases)Absence of single kidneyNonabsence of single kidneyNonalgorithm group(n=10 cases)7310Algorithm group(n=9 cases)819Table5 was the comparative statistics on CT examination diagnosis results of horseshoe kidney and the comprehensive diagnosis results. It was calculated that the accuracy of nonalgorithm group for 7 patients with horseshoe kidney was 71.42%; the accuracy of CT image based on EM algorithm for 6 patients with horseshoe kidney in the algorithm group was 100.00%, showing statistically significant difference (P<0.05).Table 5
Statistics on diagnostic accuracy of horseshoe kidney.
GroupsComprehensive diagnosis (cases)Total (cases)Horseshoe kidneyNonhorseshoe kidneyNonalgorithm group(n=7 cases)527Algorithm group(n=6 cases)606Table6 was the comparative statistics on CT examination diagnosis results of duplex kidney and the comprehensive diagnosis results. It was calculated that the accuracy of nonalgorithm group for 6 patients with duplex kidney was 50.00%; the accuracy of CT image based on EM algorithm for 6 patients with duplex kidney in the algorithm group was 85.33%, showing statistically significant difference (P<0.05).Table 6
Statistics on diagnostic accuracy of duplex kidney.
GroupsComprehensive diagnosis (cases)TotalDuplex kidneyNonduplex kidneyNonalgorithm group(n=6 cases)336Algorithm group(n=6 cases)516


## 3.4. Diagnostic Consistency of Various Renal Dysplasia

Figure5 showed the consistency comparison between the CT diagnosis results and the comprehensive diagnosis of various types of renal dysplasia in the two groups. The comparison found that, except for ADPKD, the consistency of the diagnostic results of all types of renal dysplasia in the algorithm group was higher than that of the nonalgorithm group, and the comparison was statistically significant (P<0.05).Figure 5
Comparison on Kappa values on diagnosis of various renal dysplasia. (A: ADPKD; B: single kidney dysplasia; C: single kidney loss; D: horseshoe kidney; E: duplicate kidney). “∗” meant the comparison was statistically significant (P<0.05).


## 3.5. Correlation between Various Types of Renal Dysplasia and Hypertension

Figure6 showed the comparison of the probability of hypertension in patients with various types of renal dysplasia. It was observed that the probability of hypertension in ADPKD patients was 56.77%, and the probabilities in patients with dysplasia of single kidney, absence of single kidney, horseshoe kidney, and duplex kidney were all below 20%. The incidence of hypertension in ADPKD was much higher than that of other syndromes, and there was statistical significance (P<0.05).Figure 6
Comparison of the probability of hypertension in patients with various types of renal dysplasia. (A: ADPKD; B: single kidney dysplasia; C: single kidney loss; D: horseshoe kidney; E: duplicate kidney). “∗” meant the comparison was statistically significant (P<0.05).


## 4. Discussion

Because there are many types of clinical manifestations of renal dysplasia, and the combination of imaging methods selected for different renal dysplasia diseases is not the same. In order to make the clinical examination more convenient, this study adopted the EM algorithm to denoise the CT images and then applied it in the examination of various types of renal dysplasia to compare the examination results with the comprehensive examination results and evaluate the accuracy of the diagnosis.A large number of studies have shown that the EM algorithm has certain advantages both in terms of the visualization of the restored image and the quantitative analysis of the noise-resolution relationship [24]. The research results in this study showed that the PSNR of the EM algorithm was obviously higher than that of the RAMP algorithm and mean filter, but it was not much different from the results of the PWLS algorithm and the MS-PWLS algorithm. However, the time consumption of the EM algorithm was greatly lower than that of the PWLS algorithm and the MS-PWLS algorithm. Therefore, compared with other algorithms, this algorithm has a better effect on CT image processing. Zhou et al. (2018) [25] proposed that compared with filtered back projection algorithm and prior image constrained compressed sensing algorithm, image-based weighted imaging using the maximum a posteriori expectation-maximization algorithm greatly improved carrier to noise ratio and reduced the noise of the final weighted image. Zeng (2018) [26] pointed out in their research that the EM algorithm only needed to adjust one parameter in the application process, that was, the number of iterations, so it was very convenient to use. The above results all indicate the good application value of EM algorithm in CT image processing.Studies have suggested that the main examination method for ADPKD is ultrasound, while CT will be insufficient in the diagnosis of ADPKD. Because CT examination cannot distinguish between cysts and cyst-like lesions, CT often cannot give ADPKD patients a more accurate diagnosis [27]. It was concluded in this study that the accuracy (62.07% vs. 69.23%) and consistency (0.37 vs. 0.32) of the two CT images in the diagnosis of ADPKD were not high, and the difference was not significant (P>0.05), suggesting the image denoising did not improve the diagnosis effect of the disease, indicating that there was no obvious relationship between the clarity of the diagnosis image display of the disease. In comparison, the diagnostic accuracy of other types of renal dysplasia before and after image processing has changed significantly. The results showed that the diagnostic accuracy of algorithm group was better than that of nonalgorithm group (P<0.05), indicating that dysplasia of single kidney, absence of single kidney, horseshoe kidney, and duplex kidney can be properly diagnosed by CT examination. CT imaging is also often used to determine the precise positional relationship between the external renal artery and the kidney before horseshoe kidney surgery to prevent iatrogenic injury [28]. Absence of single kidney and duplex kidney can also be observed through CT scans. Studies have shown that the clarity of CT has a certain impact on their diagnostic accuracy [29]. This is consistent with the results of this study that the accuracy of CT diagnosis of dysplasia of single kidney, absence of single kidney, horseshoe kidney, and duplex kidney would be improved with the clarity of CT image display. In addition, it was found that the incidence of hypertension in patients with ADPKD (56.77%) was much higher than other types (below 20%) (P<0.05), indicating that ADPKD has a certain relationship with abnormal blood pressure.


## 5. Conclusion

In this study, the EM algorithm was adopted to denoise CT images, and various types of renal dysplasia were diagnosed based on the CT images, and then the results were compared with the diagnosis results with the comprehensive examination results to evaluate the accuracy of the diagnosis. The results showed that CT after denoising by the EM algorithm could improve the diagnostic accuracy of several renal dysplasia diseases except ADPKD, which showed certain clinical application value, and ADPKD was easy to cause hypertension. However, the sample sizes of the various types of renal dysplasia diseases in this study were too small, which may lead to certain errors in the results of the study. Therefore, specific studies were needed to provide support. However, the general trend of the research results was still reliable, that was to say, the auxiliary role of artificial intelligence algorithms in the field of medical imaging was high, and its development prospects were considerable.



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