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

Author:

Liu Yonghui1ORCID,Tang Siai2ORCID

Affiliation:

1. Department of Urology Surgery, The Third Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421001 Hunan, China

2. Department of Endocrine Nephrology, The Third Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421001 Hunan, China

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.

Publisher

Hindawi Limited

Subject

Applied Mathematics,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,Modeling and Simulation,General Medicine

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Retracted: Artificial Intelligence Algorithm-Based Computed Tomography Image of Both Kidneys in Diagnosis of Renal Dysplasia;Computational and Mathematical Methods in Medicine;2023-12-06

2. An effective image-denoising method with the integration of thresholding and optimized bilateral filtering;Multimedia Tools and Applications;2023-04-26

3. A Review on Artificial Intelligence in Orthopaedics;2022 9th International Conference on Computing for Sustainable Global Development (INDIACom);2022-03-23

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