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. |
张珺倩, 张远, 尹勇, 等. 机器学习在肿瘤放射治疗领域应用进展. 生物医学工程学杂志, 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. |
谢立平, 郑祥义, 王潇, 等. 人工智能超声CT检查在前列腺癌早期诊断中的价值. 中华泌尿外科杂志, 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, 刘志, 等. 基于人体血液学检测的机器学习辅助泌尿系肿瘤筛查. 泌尿外科杂志:电子版, 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, 秦锋. 便携式排尿记录仪: 中国, 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, 秦锋. 智能性功能康复仪及其控制方法: 中国, 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, 张翼燕. 日本和韩国加快完善人工智能发展顶层设计. 科技中国, 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.
|