DOI: 10.25881/20728255_2026_21_1_127

Авторы

Савгачев В.В., Шубин Л.Б.

ФГБОУ ВО «Ярославский государственный медицинский университет», Ярославль

Аннотация

Стремительное развитие технологий искусственного интеллекта (ИИ) в сфере здравоохранения привлекло широкое внимание мирового медицинского сообщества. Особенно заметен потенциал применения ИИ в области травматологии, которая представляет собой гонку со временем. Травма является основной причиной смерти среди людей в возрасте до 40 лет во всем мире, а эффективность и качество лечения напрямую связаны с выживаемостью и прогнозом для пациентов. Традиционная модель лечения травм ограничена такими факторами, как неравномерное распределение медицинских ресурсов, различия в профессиональном опыте и запоздалые диагностические решения, а также существует множество проблем, которые необходимо решать безотлагательно. Вмешательство ИИ открыло возможность революционных изменений в травматологии. Целью данного исследования является всесторонний обзор текущего состояния применения ИИ во всех аспектах травматологии, глубокий анализ его клинической ценности и ограничений, а также предложение практических рекомендаций, основанных на новейших данных.

Ключевые слова: искусственный интеллект, травматология, диагностика травмы, лечение, прогнозы.

Список литературы

1. Середа А.П., Джавадов А.А., Черный А.А. Искусственный интеллект в травматологии и ортопедии. Реальность, фантазии или обман? // Травматология и ортопедия России. – 2024. – Т.30. – №2. – С.181-191. doi: 10.17816/ 2311-2905-17468.

2. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017; 69: 36-40. doi: 10.1016/j.metabol.2017.01.011.

3. Тополь Э. Искусственный интеллект в медицине. Как умные технологии меняют подход к лечению. – М.: Альпина Диджитал, 2019.

4. Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol. 2019; 28: 73-81. doi: 10.1080/13645706. 2019.1575882.

5. Labovitz DL, Shafner L, Reyes Gil M, Virmani D, Hanina A. Using artificial intelligence to reduce the risk of nonadherence in patients on anticoagulation therapy. Stroke. 2017; 48: 1416-9. doi: 10.1161/STROKEAHA.116. 016281.

6. Mayo RC, Leung J. Artificial intelligence and deep learning – radiology’s next frontier? Clin Imaging. 2018; 49: 87-8. doi: 10.1016/j.clinimag. 2017.11.007.

7. Abujaber A, Fadlalla A, Gammoh D, Abdelrahman H, Mollazehi M, El-Menyar A. Using trauma registry data to predict prolonged mechanical ventilation in patients with traumatic brain injury: machine learning approach. PLoS ONE. 2020; 15(7): e0235231.

8. Ahmed FS, Ali L, Joseph BA, Ikram A, Ul Mustafa R, Bukhari SAC. A statistically rigorous deep neural-network approach to predict mortality in trauma patients admitted to the intensive-care unit. J Trauma Acute Care Surg. 2020; 89(4): 736-42.

9. Hale AT, Stonko DP, Lim J, Guillamondegui OD, Shannon CN, Patel MB. Using an artificial neural network to predict traumatic-brain-injury outcomes. J Neurosurg Pediatr. 2018; 23(2): 219-26.

10. Matsuo K, Aihara H, Nakai T, Morishita A, Tohma Y, Kohmura E. Machine learning to predict in-hospital morbidity and mortality after traumatic brain injury. J Neurotrauma. 2020; 37(1): 202-10.

11. Hunter OF, Perry F, Salehi M, et al. Science fiction or clinical reality: a review of the applications of artificial intelligence along the continuum of trauma care. World J Emerg Surg. 2023; 18: 16. doi: 10.1186/s13017-022-00469-1.

12. AlMamlook RE, Kwayu KM, Alkasisbeh MR, Frefer AA. Comparison of machine learning algorithms for predicting traffic accident severity. In: IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT). 2019: 272-6. doi: 10.1109/JEEIT.2019. 8717393.

13. Bao J, Liu P, Ukkusuri SV. A spatiotemporal deep learning approach for city-wide short-term crash-risk prediction with multimodal data. Accid Anal Prev. 2019; 122: 239-54.

14. Mansoor U, Ratrout NT, Rahman SM, Assi K. Crash severity prediction using two-layer ensemble machine learning model for proactive emergency management. IEEE Access. 2020; 8: 210750-62.

15. Torres-Garcia AA, Reyes-García CA, Villaseñor-Pineda L, Mendoza-Montoya O, eds. Biosignal Processing and Classification Using Computational Learning and Intelligence. Academic Press. 2022: 111-29.

16. Amiri AM, Sadri A, Nadimi N, Shams M. A comparison between artificial neural network and hybrid intelligent genetic algorithm in predicting the severity of fixed-object crashes among elderly drivers. Accid Anal Prev. 2020; 138: 105468.

17. Assi K. Prediction of traffic-crash-severity using deep neural networks: a comparative study. In: International Conference on Innovation and Intelligence for Informatics, Computing and Technologies (3ICT). 2020: 1-6. doi: 10.1109/3ICT51146.2020.9311974.

18. Nederpelt CJ, Mokhtari AK, Alser O, Tsiligkaridis T, et al. Development of a field artificial-intelligence triage tool: Confidence in the prediction of shock, transfusion, and definitive surgical therapy in patients with truncal gunshot wounds. J Trauma Acute Care Surg. 2021; 90(6): 1054-60.

19. El Hechi MW, Maurer LR, Levine J, Zhuo D, et al. Validation of the artificial intelligence–based predictive optimal trees in emergency surgery risk (POTTER) calculator in emergency-general-surgery and emergency-laparotomy patients. J Am Coll Surg. 2021; 232(6): 912-9.

20. Gorczyca MT, Toscano NC, Cheng JD. The trauma-severity-model: An ensemble machine-learning approach to risk-prediction. Comput Biol Med. 2019; 108: 9-19.

21. Shahi N, Shahi AK, Phillips R, Shirek G, et al. Decision-making in pediatric-blunt-solid-organ-injury: A deep-learning approach to predict massive-transfusion, need-for-operative-management, and mortality-risk. J Pediatr Surg. 2021; 56(2): 379-84.

22. He W, Fu X, Chen S. Advancing polytrauma care: developing and validating machine learning models for early mortality prediction. J Transl Med. 2023; 21: 664. doi: 10.1186/s12967-023-04487-8.

23. Paydar S, Parva E, Ghahramani Z, Pourahmad S, et al. Do clinical and paraclinical findings have the power to predict critical conditions of injured patients after traumatic injury resuscitation? Using data-mining artificial intelligence. Chin J Traumatol. 2021; 24(1): 48-52.

24. Maurer LR, Bertsimas D, Bouardi HT, El Hechi M, et al. Trauma-outcome-predictor: An artificial-intelligence-interactive smartphone-tool to predict outcomes in trauma patients. J Trauma Acute Care Surg. 2021; 91(1): 93-9.

25. McCall HC, Richardson CG, Helgadottir FD, Chen FS. Evaluating a web-based social anxiety intervention: A randomized controlled trial among university students. J Med Internet Res. 2018; 20: e91. doi: 10.2196/ jmir.8630.

26. Sinsky C, Colligan L, Li L, Prgomet M, et al. Allocation of physician time in ambulatory practice: A time and motion study in four specialties. Ann Intern Med. 2016; 165: 753-60. doi: 10.7326/M16-0961.

27. Cheng C-Y, Chiu I-M, Hsu M-Y, Pan H-Y, et al. Deep learning-assisted detection of abdominal free fluid in Morison’s pouch during focused assessment with sonography in trauma. Front Med. 2021; 8: 707437.

28. Rashidi HH, Sen S, Palmieri TL, Blackmon T, et al. Early recognition of burn-and-trauma-related acute-kidney-injury: A pilot-comparison-of-machine-learning-techniques. Scientific Reports. 2020; 10(1): 205-6.

29. Stonko DP, Dennis BM, Betzold RD, Peetz AB, Gunter OL, Guillamondegui OD. Artificial intelligence can predict daily trauma volume and average acuity. J Trauma Acute Care Surg. 2018; 85(2): 393-7.

30. Corban J, Lorange JP, Laverdiere C, Khoury J, et al. Artificial intelligence in the management of anterior cruciate ligament injuries. Orthop J Sports Med. 2021; 9(7): 23259671211014206. doi: 10.1177/23259671211014206.

31. Staziaki PV, Wu D, Rayan JC, Santo IDO, et al. Machine learning combining CT-findings and clinical-parameters improves prediction of length-of-stay and ICU-admission in torso-trauma. Eur Radiol. 2021; 31(7): 5434-41.

32. Worldwide Antimicrobial Resistance National/International Network Group (WARNING) Collaborators. Ten golden rules for optimal antibiotic use in hospital settings: the WARNING call to action. World J Emerg Surg. 2023; 18: 50. doi: 10.1186/s13017-023-00518-3.

33. Lisacek-Kiosoglous AB, Powling AS, Fontalis A, Gabr A, et al. Artificial intelligence in orthopaedic surgery. Bone Joint Res. 2023; 12(7): 447-54. doi: 10.1302/2046-3758.127.BJR-2023-0111.R1.

34. Zhang X, Zhang D, Zhang X, Zhang X. Artificial intelligence applications in the diagnosis and treatment of bacterial infections. Front Microbiol. 2024; 15: 1449844. doi: 10.3389/fmicb.2024.1449844.

35. Nourelahi M, Dadboud F, Khalili H, Niakan A, Parsaei H. A machine-learning model for predicting favorable outcome in severe-traumatic-brain-injury patients after six-month follow-up. Acute Crit Care. 2022; 37: 45-52.

36. Rau CS, Wu SC, Chuang JF, Huang CY, Liu HT, Chien PC, et al. Machine-learning models of survival prediction in trauma patients. Journal of Clinical Medicine. 2019; 8(6): 799. doi: 10.3390/jcm8060799.

37. Kurmis AP, Ianunzio JR. Artificial intelligence in orthopedic surgery: Evolution, current state, and future directions. Arthroplasty. 2022; 4(1): 9. doi: 10.1186/s42836-022-00112-z.

38. El Hechi M, Gebran A, Bouardi HT, Maurer LR, et al. Validation of the artificial-intelligence-based trauma-outcomes-predictor (TOP) in patients aged ≥65 years. Surgery. 2022; 171(6): 1687-94.

39. Innocenti B, Radyul Y, Bori E. The use of artificial intelligence in orthopedics: Applications and limitations of machine learning in diagnosis and prediction. Applied Sciences. 2022; 12(21): 10775. doi: 10.3390/app122110775.

40. Kumar V, Patel S, Baburaj V, Vardhan A, et al. Current understanding on artificial intelligence and machine learning in orthopaedics – A scoping review. J Orthop. 2022; 34: 201-6. doi: 10.1016/j.jor.2022.08.020.

41. Masters K. Artificial intelligence in medical education. Med Teacher. 2019; 41(9): 976-980.

42. Katznelson G, Gerke S. The need for health AI ethics in medical school education. AdvHealthSciEducTheoryPract. 2021; 26(4): 1447-1458.

43. Гажва С.И., Горбатов Р.О., Ююрихина М.Н., Тетерин А.И., Янышева К.Л. 3D-технологии в медицине // Аддитивные технологии. – 2023. – №2. – С.70-77.

44. Park SH, Do KH, Kim S, et.al. What Should Medical Students Know About Artificial Intelligence in Medicine? Educ Eval Health Prof. 2019; 16: 16-21.

45. Кошечкин К.А., Хохлов А.Л. Этические проблемы внедрения искусственного интеллекта в здравоохранении // Медицинская этика. – 2024. – №1. – С.12-19. doi: 10.24075/medet.2024.006.

46. Хайдарова Н.Т.К. Конфиденциальность и защита данных с учетом применения искусственного интеллекта в рабочих процессах // Central Asian Journal of Education and Innovation. – 2024. – Т.3. – №5-3. – С.137-141. doi: 10.5281/zenodo.11408148.

47. Cheng K, Guo Q, He Y, Lu Y, et al. Artificial intelligence in sports medicine: Could GPT-4 make human doctors obsolete? Ann Biomed Eng. 2023; 51(8): 1658-62. doi: 10.1007/s10439-023-03213-1.

48. Мельников А.А. Потенциальная ответственность врачей, использующих искусственный интеллект // Дальневосточный медицинский журнал. – 2024. – №1. – С.77-80. doi: 10.35177/1994-5191-2024-1-13.

49. Ghantasala GS, Dilip K, Vidyullatha P, et al. Enhanced ovarian cancer survival prediction using temporal analysis and graph neural networks. BMC Med Inform Decis Mak. 2024; 24: 299. doi: 10.1186/s12911-024-02665-2.

50. Gyftopoulos S, Lin D, Knoll F, Doshi AM, Rodrigues TC, Recht MP. Artificial intelligence in musculoskeletal imaging: Current status and future directions. AJR Am J Roentgenol. 2019; 213(3): 506-13. doi: 10.2214/AJR.19. 21117.

51. Шадеркин И.А. Роль искусственного интеллекта в телемедицине России // Журнал телемедицины и электронного здравоохранения. – 2019. – Т.5. – №1. – С.38-40. doi: 10.29188/ 2542-2413-2019-5-1-38-40.

Для цитирования

Савгачев В.В., Шубин Л.Б. Применение искусственного интеллекта в травматологии. Вестник НМХЦ им. Н.И. Пирогова. 2026;21(1):127-133. https://doi.org/10.25881/20728255_2026_21_1_127