Research status and trend of artificial intelligence in the diagnosis of urinary diseases_Journal of Biomedical Engineering

Authors：

QIN Feng ,  YUAN Jiuhong

Andrology Laboratory, Department of Urology, West China Hospital, Sichuan University, Chengdu 610041, P.R.China;

Corresponding?author：

YUAN Jiuhong, Email: jiuhongyuan2107@163.com

Keywords：

artificial intelligence; urinary diseases; cancer; urinary infection; urological calculi; erectile dysfunction; diagnosis

DOI：

10.7507/1001-5515.201910055

Video：

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Recently, artificial intelligence (AI) has been widely applied in the diagnosis and treatment of urinary diseases with the development of data storage, image processing, pattern recognition and machine learning technologies. Based on the massive biomedical big data of imaging and histopathology, many urinary system diseases (such as urinary tumor, urological calculi, urinary infection, voiding dysfunction and erectile dysfunction) will be diagnosed more accurately and will be treated more individualizedly. However, most of the current AI diagnosis and treatment are in the pre-clinical research stage, and there are still some difficulties in the wide application of AI. This review mainly summarizes the recent advances of AI in the diagnosis of prostate cancer, bladder cancer, kidney cancer, urological calculi, frequent micturition and erectile dysfunction, and discusses the future potential and existing problems.

Citation： QIN Feng, YUAN Jiuhong. Research status and trend of artificial intelligence in the diagnosis of urinary diseases. Journal of Biomedical Engineering, 2020, 37(2): 230-235. doi: 10.7507/1001-5515.201910055 Copy

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18.	Merisaari H, Movahedi P, Perez I M, et al. Fitting methods for intravoxel incoherent motion imaging of prostate cancer on region of interest level: repeatability and Gleason score prediction. Magnetic Resonance in Medicine, 2017, 77(3): 1249-1264.
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22.	Loch T, Leuschner I, Genberg C, et al. Artificial neural network analysis (ANNA) of prostatic transrectal ultrasound. Prostate, 1999, 39(3): 198-204.
23.	Ikeda A, Nosato H, Kochi Y, et al. Support system of cystoscopic diagnosis for bladder cancer based on artificial intelligence. J Endourol, 2019.
24.	Garapati S S, Hadjiiski L, Cha K H, et al. Urinary bladder cancer staging in CT urography using machine learning. Med Phys, 2017, 44(11): 5814-5823.
25.	Xu Xiaopan, Zhang Xi, Tian Qiang, et al. Three-dimensional texture features from intensity and high-order derivative maps for the discrimination between bladder tumors and wall tissues via MRI. Int J Comput Assist Radiol Surg, 2017, 12(4): 645-656.
26.	Shao C H, Chen C L, Lin Jiayou, et al. Metabolite marker discovery for the detection of bladder cancer by comparative metabolomics. Oncotarget, 2017, 8(24): 38802-38810.
27.	Kouznetsova V L, Kim E, Romm E L, et al. Recognition of early and late stages of bladder cancer using metabolites and machine learning. Metabolomics, 2019, 15(7): 94.
28.	Azuaje F, Kim S Y, Perez Hernandez D, et al. Connecting histopathology imaging and proteomics in kidney cancer through machine learning. J Clin Med, 2019, 8(10): E1535.
29.	Zheng Hong, Ji Jiansong, Zhao Liangcai, et al. Prediction and diagnosis of renal cell carcinoma using nuclear magnetic resonance-based serum metabolomics and self-organizing maps. Oncotarget, 2016, 7(37): 59189-59198.
30.	Haifler M, Pence I, Sun Yu, et al. Discrimination of malignant and normal kidney tissue with short wave infrared dispersive Raman spectroscopy. J Biophotonics, 2018, 11(6): e201700188.
31.	王正, 王金申, 劉志, 等. 基于人體血液學檢測的機器學習輔助泌尿系腫瘤篩查. 泌尿外科雜志:電子版, 2017, 9(4): 9-14.
32.	Parakh A, Le eH, Lee J H, et al. Urinary stone detection on CT images using deep convolutional neural networks: evaluation of model performance and generalization. Radiol Artif Intell, 2019, 1(4): e180066.
33.	de Perrot T, Hofmeister J, Burgermeister S, et al. Differentiating kidney stones from phleboliths in unenhanced low-dose computed tomography using radiomics and machine learning. Eur Radiol, 2019, 29(9): 4776-4782.
34.	Kazemi Y, Mirroshandel S A. A novel method for predicting kidney stone type using ensemble learning. Artif Intell Med, 2018, 84: 117-126.
35.	Burke A E, Thaler K M, Geva M, et al. Feasibility and acceptability of home use of a smartphone-based urine testing application among women in prenatal care. Am J Obstet Gynecol, 2019, 221(5): 527-528.
36.	Bayne C E, Majd M, Rushton H G. Diuresis renography in the evaluation and management of pediatric hydronephrosis: what have we learned?. J Pediatr Urol, 2019, 15(2): 128-137.
37.	Blum E S, Porras A R, Biggs E, et al. Early detection of ureteropelvic junction obstruction using signal analysis and machine learning: a dynamic solution to a dynamic problem. J Urol, 2018, 199(3): 847-852.
38.	Cerrolaza J J, Peters C A, Martin A D, et al. Quantitative ultrasound for measuring obstructive severity in children with hydronephrosis. Journal of Urology, 2016, 195(4): 1093-1098.
39.	袁久洪, 秦鋒. 便攜式排尿記錄儀: 中國, 201610006224.2. 2018.12.11.
40.	Qin Feng, Gao Liang, Qian Shengqiang, et al. Advantages and limitations of sleep-related erection and rigidity monitoring: a review. Int J Impot Res, 2018, 30(4): 192-201.
41.	袁久洪, 秦鋒. 智能性功能康復儀及其控制方法: 中國, 201310163158.6. 2016.2.17.
42.	Yuan Jiuhong, Qin Feng. Intelligent monitor of erectile function: US, 9888878. 2018.2.13.
43.	Li Lingli, Fan Wenliang, Li Jun, et al. Abnormal brain structure as a potential biomarker for venous erectile dysfunction: evidence from multimodal MRI and machine learning. Eur Radiol, 2018, 28(9): 3789-3800.
44.	高芳, 張翼燕. 日本和韓國加快完善人工智能發展頂層設計. 科技中國, 2018, 8: 88-92.
45.	Graham J. Artificial intelligence, machine learning, and the FDA. (2016)[2019-10-25]. https://www.forbes.com/sites/theapothecary/2016/08/19/artificial-intelligence-machine-learning-and-the-fda/9811c1b1aa19.

1. He Jianxing, Baxter S L, Xu Jie, et al. The practical implementation of artificial intelligence technologies in medicine. Nat Med, 2019, 25(1): 30-36.
2. Chen J, Remulla D, Nguyen J H, et al. Current status of artificial intelligence applications in urology and their potential to influence clinical practice. BJU Int, 2019, 124(4): 567-577.
3. Goldenberg S L, Nir G, Salcudean S E. A new era: artificial intelligence and machine learning in prostate cancer. Nat Rev Urol, 2019, 16(7): 391-403.
4. de Dombal F T, Leaper D J, Staniland J R, et al. Computer-aided diagnosis of acute abdominal pain. Br Med J, 1972, 2(5804): 9-13.
5. Kermany D S, Goldbaum M, Cai Wenjia, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell, 2018, 172(5): 1122-1131.
6. Liang Huiying, Tsui B Y, Ni Hao, et al. Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Nat Med, 2019, 25(3): 433-438.
7. Yaeger K A, Martini M, Yaniv G, et al. United States regulatory approval of medical devices and software applications enhanced by artificial intelligence. Health Policy Technol, 2019, 8(2): 192-197.
8. United States Food and Drug Administration. July 2018 510(K) Clearances.[2019-10-25]. https://www.fda.gov/MedicalDevices/ProductsandMedicalProcedures/DeviceApprovalsandClearances/510kClearances/ucm615930.htm.
9. Miller D D, Brown E W. Artificial intelligence in medical practice: the question to the answer?. American Journal of Medicine, 2018, 131(2): 129-133.
10. 張珺倩, 張遠, 尹勇, 等. 機器學習在腫瘤放射治療領域應用進展. 生物醫學工程學雜志, 2019, 36(5): 879-884.
11. Kim J K, Yook I H, Choi M J, et al. A performance comparison on the machine learning classifiers in predictive pathology staging of prostate cancer. Stud Health Technol Inform, 2017, 245: 1273.
12. Zhang C, Zhang Q, Gao X, et al. High accuracy and effectiveness with deep neural networks and artificial intelligence in pathological diagnosis of prostate cancer: initial results. European Urology Supplements, 2018, 17(2): e304-e308.
13. Kwak J T, Hewitt S M. Multiview boosting digital pathology analysis of prostate cancer. Comput Methods Programs Biomed, 2017, 142: 91-99.
14. Nguyen T H, Sridharan S, Macias V, et al. Automatic gleason grading of prostate cancer using quantitative phase imaging and machine learning. J Biomed Opt, 2017, 22(3): 36015.
15. Wang Jing, Wu Chenjiang, Bao Meiling, et al. Using support vector machine analysis to assess PartinMR: a new prediction model for organ-confined prostate cancer. J Magn Reson Imaging, 2018, 48(2): 499-506.
16. Algohary A, Viswanath S, Shiradkar R, et al. Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: preliminary findings. J Magn Reson Imaging, 2018, 48: 818-828.
17. Ginsburg S B, Algohary A, Pahwa S, et al. Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: preliminary findings from a multi-institutional study. J Magn Reson Imaging, 2017, 46(1): 184-193.
18. Merisaari H, Movahedi P, Perez I M, et al. Fitting methods for intravoxel incoherent motion imaging of prostate cancer on region of interest level: repeatability and Gleason score prediction. Magnetic Resonance in Medicine, 2017, 77(3): 1249-1264.
19. Fehr D, Veeraraghavan H, Wibmer A, et al. Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images. Proc Natl Acad Sci U S A, 2015, 112(46): E6265-E6273.
20. Wang Rui, Wang Jing, Gao Ge, et al. Prebiopsy mp-MRI can help to improve the predictive performance in prostate cancer: a prospective study in 1, 478 consecutive patients. Clin Cancer Res, 2017, 23(14): 3692-3699.
21. 謝立平, 鄭祥義, 王瀟, 等. 人工智能超聲CT檢查在前列腺癌早期診斷中的價值. 中華泌尿外科雜志, 2015, 36(11): 822-825.
22. Loch T, Leuschner I, Genberg C, et al. Artificial neural network analysis (ANNA) of prostatic transrectal ultrasound. Prostate, 1999, 39(3): 198-204.
23. Ikeda A, Nosato H, Kochi Y, et al. Support system of cystoscopic diagnosis for bladder cancer based on artificial intelligence. J Endourol, 2019.
24. Garapati S S, Hadjiiski L, Cha K H, et al. Urinary bladder cancer staging in CT urography using machine learning. Med Phys, 2017, 44(11): 5814-5823.
25. Xu Xiaopan, Zhang Xi, Tian Qiang, et al. Three-dimensional texture features from intensity and high-order derivative maps for the discrimination between bladder tumors and wall tissues via MRI. Int J Comput Assist Radiol Surg, 2017, 12(4): 645-656.
26. Shao C H, Chen C L, Lin Jiayou, et al. Metabolite marker discovery for the detection of bladder cancer by comparative metabolomics. Oncotarget, 2017, 8(24): 38802-38810.
27. Kouznetsova V L, Kim E, Romm E L, et al. Recognition of early and late stages of bladder cancer using metabolites and machine learning. Metabolomics, 2019, 15(7): 94.
28. Azuaje F, Kim S Y, Perez Hernandez D, et al. Connecting histopathology imaging and proteomics in kidney cancer through machine learning. J Clin Med, 2019, 8(10): E1535.
29. Zheng Hong, Ji Jiansong, Zhao Liangcai, et al. Prediction and diagnosis of renal cell carcinoma using nuclear magnetic resonance-based serum metabolomics and self-organizing maps. Oncotarget, 2016, 7(37): 59189-59198.
30. Haifler M, Pence I, Sun Yu, et al. Discrimination of malignant and normal kidney tissue with short wave infrared dispersive Raman spectroscopy. J Biophotonics, 2018, 11(6): e201700188.
31. 王正, 王金申, 劉志, 等. 基于人體血液學檢測的機器學習輔助泌尿系腫瘤篩查. 泌尿外科雜志:電子版, 2017, 9(4): 9-14.
32. Parakh A, Le eH, Lee J H, et al. Urinary stone detection on CT images using deep convolutional neural networks: evaluation of model performance and generalization. Radiol Artif Intell, 2019, 1(4): e180066.
33. de Perrot T, Hofmeister J, Burgermeister S, et al. Differentiating kidney stones from phleboliths in unenhanced low-dose computed tomography using radiomics and machine learning. Eur Radiol, 2019, 29(9): 4776-4782.
34. Kazemi Y, Mirroshandel S A. A novel method for predicting kidney stone type using ensemble learning. Artif Intell Med, 2018, 84: 117-126.
35. Burke A E, Thaler K M, Geva M, et al. Feasibility and acceptability of home use of a smartphone-based urine testing application among women in prenatal care. Am J Obstet Gynecol, 2019, 221(5): 527-528.
36. Bayne C E, Majd M, Rushton H G. Diuresis renography in the evaluation and management of pediatric hydronephrosis: what have we learned?. J Pediatr Urol, 2019, 15(2): 128-137.
37. Blum E S, Porras A R, Biggs E, et al. Early detection of ureteropelvic junction obstruction using signal analysis and machine learning: a dynamic solution to a dynamic problem. J Urol, 2018, 199(3): 847-852.
38. Cerrolaza J J, Peters C A, Martin A D, et al. Quantitative ultrasound for measuring obstructive severity in children with hydronephrosis. Journal of Urology, 2016, 195(4): 1093-1098.
39. 袁久洪, 秦鋒. 便攜式排尿記錄儀: 中國, 201610006224.2. 2018.12.11.
40. Qin Feng, Gao Liang, Qian Shengqiang, et al. Advantages and limitations of sleep-related erection and rigidity monitoring: a review. Int J Impot Res, 2018, 30(4): 192-201.
41. 袁久洪, 秦鋒. 智能性功能康復儀及其控制方法: 中國, 201310163158.6. 2016.2.17.
42. Yuan Jiuhong, Qin Feng. Intelligent monitor of erectile function: US, 9888878. 2018.2.13.
43. Li Lingli, Fan Wenliang, Li Jun, et al. Abnormal brain structure as a potential biomarker for venous erectile dysfunction: evidence from multimodal MRI and machine learning. Eur Radiol, 2018, 28(9): 3789-3800.
44. 高芳, 張翼燕. 日本和韓國加快完善人工智能發展頂層設計. 科技中國, 2018, 8: 88-92.
45. Graham J. Artificial intelligence, machine learning, and the FDA. (2016)[2019-10-25]. https://www.forbes.com/sites/theapothecary/2016/08/19/artificial-intelligence-machine-learning-and-the-fda/9811c1b1aa19.

Journal of Biomedical Engineering

Research status and trend of artificial intelligence in the diagnosis of urinary diseases

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