Искусственный интеллект для персонализированного подхода к перкутанной нефролитотрипсии
Автор: Щамхалова К.К., Меринов Д.С., Артемов А.В., Гурбанов Ш.Ш., Инамов Р.Р., Аполихин О.И., Каприн А.Д.
Журнал: Экспериментальная и клиническая урология @ecuro
Рубрика: Экспериментальная урология
Статья в выпуске: 1 т.17, 2024 года.
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Введение. В последние годы, все больше появляется исследований по созданию искусственного интеллекта (ИИ) для помощи в диагностике и определения тактики лечения пациентов в разных областях медицины. Применение алгоритмов позволяет сократить время на обработку больших объемов информации и дать «второе мнение» специалисту при сложных и нестандартных клинических случаях. С целью повышения эффективности перкутанной нефролитотрипсии нами был создан алгоритм ИИ для создания персонализированного подхода в хирургическом лечении нефролитиаза.
Мочекаменная болезнь, перкутанная нефролитотрипсия, искусственный интеллект
Короткий адрес: https://sciup.org/142241812
IDR: 142241812 | DOI: 10.29188/2222-8543-2024-17-1-24-34
Список литературы Искусственный интеллект для персонализированного подхода к перкутанной нефролитотрипсии
- Türk C, Petřík A, Sarica K, Seitz C, Skolarikos A, Straub M. EAU guidelines on inter- ventional treatment for urolithiasis. Eur Urol 2016;69(3):475–82. https://doi.org/10.1016/ j.eururo.2015.07.041
- Kallidonis P, Kyriazis I, Kotsiris D, Koutava A, Kamal W, Liatsikos E. Papillary vs non- papillary puncture in percutaneous nephrolithotomy: a prospective randomized trial. J Endourol 2017;31(S1):S4–S9. https://doi.org/10.1089/end.2016.0571
- Kyriazis I, Kallidonis P, Vasilas M, Panagopoulos V, Kamal W, Liatsikos E. Challenging the wisdom of puncture at the calyceal fornix in percutaneous nephrolithotripsy: feasibility and safety study with 137 patients operated via a non-calyceal percutaneous track. World J Urol 2017;35(5):795–801. https://doi.org/10.1007/s00345-016-1919-y
- Kallidonis P, Kalogeropoulou C, Kyriazis I, Apostolopoulos D, Kitrou P, Kotsiris D. Per- cutaneous nephrolithotomy puncture and tract dilation: evidence on the safety of ap- proaches to the infundibulum of the middle renal calyx. Urology 2017;107:43–8. https://doi.org/10.1016/j.urology.2017.05.038
- Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minimally Invasive Therapy & Allied Technologies 2019;28(2):73-81. https://doi.org/10.1080/13645706.2019.1575882.
- Checcucci E, Amparore D, Volpi G, Piramide F, De Cillis S, Piana A et al. Percutaneous puncture during PCNL: new perspective for the future with virtual imaging guidance. World J Urol 2022;40(3):639-50. https://doi.org/10.1007/s00345-021-03820-4.
- Antoniou V, Gauhar V, Kallidonis P, Skolarikos A, Veneziano D, Liatsikos E et al. Edu- cation and training evolution in urolithiasis: A perspective from European School of Urol- ogy. Asian J Urol 2023;10(3):281-8. https://doi.org/10.1016/j.ajur.2023.01.004.
- Nedbal C, Cerrato C, Jahrreiss V, Castellani D, Pietropaolo A, Galosi AB et al. Th role of 'artifi intelligence, machine learning, virtual reality, and radiomics' in PCNL: a review of publication trends over the last 30years. Ther Adv Urol 2023;15:17562872231196676. https://doi.org/10.1177/17562872231196676.
- McDonald L, Ramagopalan SV, Cox AP, Oguz M. Unintended consequences of machine learning in medicine. J Americаn Med Assoc 2017;318(6):517–8. https://doi.org/10.12688/ f1000research.12693.1
- Lee JG, Jun S, Cho YW, Lee H, Kim GB, Seo JB, Kim N. Deep learning in medical im- aging: general overview. Korean J Radiol 2017;18(4):570–84. https://doi.org/10.3348/ kjr.2017.18.4.570.
- McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, et al. International evaluation of an AI system for breast cancer screening. Nature 2020;577(7788):89-94. https://doi.org/10.1038/s41586-019-1799-6.
- Ahmed A, Brychcy A, Abouzid A, Witt M, Kaczmarek E. Perception of Pathologists in Poland of Artifi Intelligence and Machine Learning in Medical Diagnosis—A Cross- Sectional Study. J Pers Med 2023;13:962. https://doi.org/10.3390/jpm13060962
- Wishahi M, El Feel A, Elkhouly A, Fahmy A, Roshdy M, Elbaz AG, et al. Concerns about stone free rate and procedure events of percutaneous nephrolithotripsy (PCNL) for 2-4 cm kidney stones by standard-PCNL vs mini-PCNL- comparative randomised study. BMC Urol 2023;23(1):96. https://doi.org/10.1186/s12894-023-01270-1.
- Th pa BB, Niranjan V. Mini PCNL Over Standard PCNL: What Makes it Better? Surg J (N Y) 2020;6(1):e19-e23. https://doi.org/10.1055/s-0040-1701225.
- Jiang G, Li J, Long H, Qiulin C, Jin R, Yaodong Y et al. Study on risk factors, bacterial species, and drug resistance of acute pyelonephritis associated with ureteral stent after per- cutaneous nephrolithotomy. Eur J Clin Microbiol Infect Dis 2021;40(4):707-13. https://doi.org/10.1007/s10096-020-04050-z.
- Bhojani N, Paranjpe R, Cutone B, Eisner BH. Infectious Complications, Healthcare Resource Use, and Medical Costs Associated with Delays in Percutaneous Nephrolithotomy Among Patients with Stone Disease and Ureteral Stent Placement. J Endourol 2023;37(6):654-9. https://doi.org/10.1089/end.2022.0489.
- Patel AP, Bui D, Pattaras J, Ogan K. Upper pole urologist-obtained percutaneous renal access for PCNL is safe and effi us. Can J Urol 2017;24(2):8754-8.
- Soares RMO, Zhu A, Talati VM, Nadler RB. Upper Pole Access for Prone Percutaneous Nephrolithotomy: Advantage or Risk? Urology 2019;134:66-71. https://doi.org/10.1016/j.urology.2019.08.031.
- Lightfoot M, Ng C, Engebretsen S, Wallner C, Huang G, Li R et al. Analgesic use and complications following upper pole access for percutaneous nephrolithotomy. J Endourol 2014;28(8):909-14. https://doi.org/10.1089/end.2014.0035.
- Kriegshauser JS, Silva AC, Paden RG, He M, Humphreys MR, Zell SI, et al. Ex vivo renal stone characterization with single-source dual-energy computed tomography: a mul- tiparametric approach. Acad Radiol 2016;23(8):969–76. https://doi.org/10.1016/ j.acra.2016.03.009
- Kazemi Y, Mirroshandel SA. A novel method for predicting kidney stone type using ensemble learning. Artif Intell Med 2018;84:117–26. https://doi.org/10.1016/ j.artmed.2017.12.001
- Aminsharif A, Irani D, Tayebi S, Jafari Kafash T, Shabanian T, Parsaei H. Predicting the Postoperative Outcome of Percutaneous Nephrolithotomy with Machine Learning System: Soft re Validation and Comparative Analysis with Guy's Stone Score and the CROES Nomogram. J Endourol 2020;34(6):692-9. https://doi.org/10.1089/end.2019.0475
- Aminsharifi A, Irani D, Pooyesh S, Parvin H, Dehghani S, Yousofi K et al. Artifcial Neural Network System to Predict the Postoperative Outcome of Percutaneous Nephrolitho- tomy. J Endourol 2017;31(5):461-7. https://doi.org/10.1089/end.2016.0791
- Shabaniyan T, Parsaei H, Aminsharifi A, Movahedi MM, Jahromi AT, Pouyesh S et al. An artifcial intelligence-based clinical decision support system for large kidney stone treatment. Australas Phys Eng Sci Med 2019;42(3):771–9. https://doi.org/10.1007/ s13246-019-00780-3
- Tang A, Tam R, Cadrin-Chênevert A, Guest W, Chong J, Barfett J, et al. Canadian As- sociation of Radiologists white paper on artifi intelligence in radiology. Canad Assoc Radiol J 2018;69(2):120-35. https://doi.org/10.1016/j.carj.2018.02.002.
- Wagner MW, Namdar K, Biswas A, Monah S, Khalvati F, Ertl-Wagner BB. Radiomics. Machine learning, and artificial intelligence-what the neuroradiologist needs to know. Neuroradiology 2021;63(12):1957-67. https://doi.org/10.1007/s00234-021-02813-9
- Nedbal C, Cerrato C, Jahrreiss V, Castellani D, Pietropaolo A, Galosi AB et al. Th role of 'artifi intelligence, machine learning, virtual reality, and radiomics' in PCNL: a review of publication trends over the last 30years. Ther Adv Urol 2023;15:17562872231196676. https://doi.org/10.1177/17562872231196676.
- Infante G, Miceli R, Ambrogi F. Sample size and predictive performance of machine learning methods with survival data: A simulation study. Stat Med 2023;42(30):5657-75. https://doi.org/10.1002/sim.9931.
- Kuwabara M, Ikawa F, Sakamoto S, Okazaki T, Ishii D, Hosogai M et al. Effectiveness of tuning an artificial intelligence algorithm for cerebral aneurysm diagnosis: a study of 10,000 consecutive cases. Sci Rep 2023;13(1):16202. https://doi.org/10.1038/s41598-023-43418-x.
- Jin C, Chen W, Cao Y, Xu Z, Tan Z, Zhang X et al. Development and evaluation of an artificial intelligence system for COVID-19 diagnosis. Nat Com- mun 2020;11(1):5088. https://doi.org/10.1038/s41467-020-18685-1.
- Shoshan Y, Bakalo R, Gilboa-Solomon F, Ratner V, Barkan E, Ozery-Flato M et al. Artificial Intelligence for Reducing Workload in Breast Cancer Screening with Digital Breast Tomosynthesis. Radiology 2022;303(1):69-77. https://doi.org/10.1148/radiol.211105.
- Maugeri O, Di Grazia E, D'Arrigo L, Agliozzo R, Calvano G, Trovato F et al. Supine mini percutaneous nephrolithotomy in horseshoe kidney. Arch Ital Urol Androl 2023;95(3):11605. https://doi.org/10.4081/aiua.2023.11605.
- Blackburne AT, Rivera ME, Gettman MT, Patterson DE, Krambeck AE. Endoscopic Management of Urolithiasis in the Horseshoe Kidney. Urology 2016;90:45-9. https://doi.org/10.1016/j.urology.2015.12.042.