Use of intelligent analysis in urology


Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription or Fee Access

Abstract

The main methods of intellectual analysis (IA) used in modern medicine are described in the review. The main areas for IA application in the healthcare are diagnostics, treatment, prognosis of the course and efficiency of treatment in various diseases. The IA is based on mathematical methods and algorithms. The basic concepts of IA along with examples of its use in urological practice are presented in the review.

Full Text

Restricted Access

About the authors

E. Kh Harbedia

FGAOU VO I.M. Sechenov First Moscow State Medical University

Email: harbediyaliza@inbox.ru
6-year student, Institute of Urology and Reproductive Health

L. M Rapoport

FGAOU VO I.M. Sechenov First Moscow State Medical University

Email: leonidrapoport@yandex.ru
Ph.D., MD, professor, Deputy Director on Medical care at the Institute of Urology and Reproductive Health

V. N Gridin

Design Information Technologies Center Russian Academy of Sciences

Email: info@ditc.ras.ru
Doctor in technical science, professor, scientific chief

D. G Tsarichenko

FGAOU VO I.M. Sechenov First Moscow State Medical University

Email: tsarichenkodg@yandex.ru
Ph.D., MD, professor at the Institute of Urology and Reproductive Health

I. A Kuznetsov

Design Information Technologies Center Russian Academy of Sciences

Email: info@ditc.ras.ru
Ph.D. in technical science, head of the laboratory

E. S Sirota

FGAOU VO I.M. Sechenov First Moscow State Medical University

Email: essirota@mail.ru
Ph.D., MD, Institute ofUrology and Reproductive Health

Yu. G Alyaev

FGAOU VO I.M. Sechenov First Moscow State Medical University

Email: ugalyaev@mail.ru
corresponding member of RAS, Ph.D., MD, professor at the Institute of Urology and Reproductive Health

References

  1. Blobel B. Challenges and Solutions for Designing and Managing pHealth Ecosystems. Front. Med. 2019;6:83. doi: 10.3389/fmed.2019.00083.
  2. Blobel B., Ruotsalainen P., Brochhausen M., Oemig F., Uribe G.A. Autonomous Systems and Artificial Intelligence in Healthcare Transformation to 5P Medicine - Ethical Challenges. Stud. Health Technol. Inform. 2020;270:1089-1093. doi: 10.3233/SHTI200330.
  3. Suarez-Ibarrola R., Hein S., Reis G., Gratzke C., Miernik A. Current and future applications of machine and deep learning in urology: a review of the literature on urolithiasis, renal cell carcinoma, and bladder and prostate cancer. World J. Urol. 2020;38(10):2329-2347. doi: 10.1007/s00345-019-03000-5.
  4. Checcucci E. et al. Artificial intelligence and neural networks in urology: current clinical applications. Minerva Urol. Nefrol. 2020;72(1):49-57. doi: 10.23736/S0393-2249.19.03613-0.
  5. Pai R.K. et al. A review of current advancements and limitations of artificial intelligence in genitourinary cancers. Am. J. Clin. Exp. Urol. 2020;8(5):152-162.
  6. Davenport T., Kalakota R. The potential for artificial intelligence in healthcare. Future healthcare journal. 2019;6(2):94-98. Doi: 10.7861/ futurehosp.6-2-94.
  7. Topol E.J. High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine. 2019;25(1):44-56. Doi: 10.1038/ s41591-018-0300-7.
  8. Ferris T.G., Shields A., Ph D., Rosenbaum S., Blumenthal D. Jha2009. 2009.
  9. Sood R.R. et al. 3D Registration of pre-surgical prostate MRI and histopathology images via super-resolution volume reconstruction. Med. Image Anal. 2021;69:101957. doi: 10.1016/j.media.2021.101957.
  10. Zhang G.M.Y., Shi B., Xue H.D., Ganeshan B., Sun H., Jin, Z.Y. Can quantitative CT texture analysis be used to differentiate subtypes of renal cell carcinoma? Clin. Radiol. 2019;74(4):287-294. Doi: 10.1016/j. crad.2018.11.009.
  11. Shkolyar E., Jia X., Chang T.C., Trivedi D., Mach K.E., Meng M.Q., Xing L., Liao J.C. Augmented Bladder Tumor Detection Using Deep Learning. Eur Urol. 2019;76(6):714-718. doi: 10.1016/j.eururo.2019.08.032.
  12. Tong F., Shahid M., Jin P., Jung S., Kim W.H., Kim J. Classification of the urinary metabolome using machine learning and potential applications to diagnosing interstitial cystitis. Bladder (San Franc). 2020;7(2):e43. doi: 10.14440/bladder.2020.815.
  13. Choo M.S., Uhmn S., Kim J.K., Han J.H., Kim D.H., Kim J., Lee S.H. A Prediction Model Using Machine Learning Algorithm for Assessing Stone-Free Status after Single Session Shock Wave Lithotripsy to Treat Ureteral Stones. J. Urol. 2018;200(6):1371-1377. doi: 10.1016/j.juro.2018.06.077.
  14. Liu H., Tang K., Peng E., Wang L., Xia D., Chen Z. Predicting Prostate Cancer Upgrading of Biopsy Gleason Grade Group at Radical Prostatectomy Using Machine Learning-Assisted Decision-Support Models. Cancer Manag Res. 2020;12:13099-13110. doi: 10.2147/CMAR.S286167.
  15. Ершов А.В., Капсаргин Ф.П., Бережной А.Г. Создание нейросетевой системы поддержки в выборе тактики лечения при мочекаменной болезни. Урологические ведомости. 2019. Спецвыпуск. URL: https://cyberleninka.ru/article/n/sozdanie-neyrosetevoy-sistemy-podderzhki-v-vybore-taktiki-lecheniya-pri-mochekamennoy-bolezni (дата обращения: 19.02.2021
  16. Жариков О.Г., Ковалев В.А., Литвин А.А. Современные возможности использования некоторых экспертных систем в медицине. Врач и информационные технологии. 2008;5:24-30
  17. Aghazadeh M.A., Jayaratna I.S., Hung A.J., Pan M.M., Desai M.M., Gill I.S., Goh A.C. External validation of Global Evaluative Assessment of Robotic Skills (GEARS). Surgical Endoscopy. 2015;29(11):3261-3266. doi: 10.1007/s00464-015-4070-8
  18. Ghani K.R., Liu Y., Law H. et al. Video analysis of skill and technique (Vast): machine learning to assess surgeons performing robotic prostatectomy. J. Urol. 2017;197:e891.
  19. Ганцев Ш.Х., Зимичев А.А., Хрисанов Н.Н., Климентьева М.С. Применение нейронной сети в прогнозировании рака мочевого пузыря. Медицинский вестник Башкортостана. 2010. № 3. URL: https://cyberleninka.ru/article/n/primenenie-neyronnoy-seti-v-prognozirovanii-raka-mochevogo-puzyrya (дата обращения: 19.02.2021
  20. Шапиро Л., Стокман Дж. Компьютерное зрение = Computer Vision. М.: Бином. Лаборатория знаний. 2006. 752 с. ISBN 5-94774-384-1
  21. Black K.M., Law H., Aldoukhi A., Deng J., Ghani.KR Deep learning computer vision algorithm for detecting kidney stone composition. BJU Int. 2020;125(6):920-924. doi: 10.1111/bju.15035.
  22. Nosrati M.S., Amir-Khalili A., Peyrat J.M. et al. Endoscopic scene labelling and augmentation using intraoperative pulsatile motion and colour appearance cues with preoperative anatomical priors. Int J. Comput Assist Radiol Surg 2016;11:1409-1418.
  23. Wang F., Zhang C., Guo F., Ji J., Lyu J., Cao Z., Yang B. Navigation of Intelligent/Interactive Qualitative and Quantitative Analysis ThreeDimensional Reconstruction Technique in Laparoscopic or Robotic Assisted Partial Nephrectomy for Renal Hilar Tumors. J. Endourol. 2019;33(8):641-646. doi: 10.1089/end.2018.0570.
  24. Bilimoria K.Y., Liu Y., Paruch J.L., Zhou L., Kmiecik T.E., Ko C.Y., Cohen M.E. Development and evaluation of the universal ACS NSQIP surgical risk calculator: a decision aid and informed consent tool for patients and surgeons. J. Am Coll Surg. 2013;217(5):833-42. e1-3. Doi: 10.1016/j. jamcollsurg.2013.07.385.
  25. Winoker J.S., Paulucci D.J., Anastos H., Waingankar N., Abaza R., Eun D.D., Bhandari A., Hemal A.K., Sfakianos J.P., Badani K.K. Predicting Complications Following Robot-Assisted Partial Nephrectomy with the ACS NSQIP® Universal Surgical Risk Calculator. J. Urol. 2017;198(4):803- 809. doi: 10.1016/j.juro.2017.04.021.
  26. Sirota E.S., Rapoport L.M., Gridin V.N., Tsarichenko D.G., Kuznetsov I.A., Sirota A.E., Alyaev Yu.G. Analysis of the learning curve in laparoscopic partial nephrectomy in patients with localized renal parenchymal lesions depending on the nephrometric score. Urologiia. 2020;6:11-18. doi: 10.18565/urology.2020.6.00-00.

Supplementary files

Supplementary Files
Action
1. JATS XML

Copyright (c) 2021 Bionika Media

This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies