radiologyРадиология — практикаRadiology - Practice2713-0118Центральный научно-исследовательский институт лучевой диагностики10.52560/2713-0118-2025-4-81-91radiology-786Research ArticleПРОДОЛЖЕННОЕ МЕДИЦИНСКОЕ ОБРАЗОВАНИЕCONTINUING MEDICAL EDUCATIONПрименение искусственного интеллекта при заболеваниях опорно-двигательного аппарата (обзор литературы)Application of Artificial Intelligence in Diseases of the Musculoskeletal Systems (Literature Review)https://orcid.org/0009-0001-7362-5461УшаковаЮ. А.UshakovaYu. A.

Ушакова Юлия Алексеевна, студентка 6 курса

Рязань

Ushakova Yulia Alekseevna, 6th year student

Ryazan

jalekseeva97@bk.ruФГБОУ ВО «РязГМУ имени академика И. П. Павлова» Минздрава РоссииРоссияFederal State Budgetary Educational Institution of Higher Education «I. P.Pavlov Ryazan State Medical University» of the Ministry of Healthcare of the Russian FederationRussian Federation202524082025048191Copyright © Ушакова Ю.А., 20252025Ушакова Ю.А.Ushakova Y.A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.radp.ru/jour/article/view/786Цель исследования

Цель исследования. Анализ исследований, посвященных применению искусственного интеллекта (ИИ) при заболеваниях опорно-двигательного аппарата (ОДА) для определения эффективности внедрения новых технологий.

Материалы и методы

Материалы и методы. Для обзора литературы отобраны наиболее цитируемые исследования по применению ИИ в диагностике и лечении ОДА, размещенные в научных базах данных в открытом доступе.

Результаты

Результаты. Исследования, описанные в обзоре научных статей, демонстрируют огромный потенциал ИИ в диагностике заболеваний опорно-двигательного аппарата и показывают, как он может быть полезным для врачей и пациентов.

Заключение

Заключение. Внедрение искусственного интеллекта в ортопедию открывает новые горизонты для улучшения качества медицинского оборудования. Специализированная аппаратура, мобильные приложения не только облегчают процесс мониторинга состояния пациентов, но и делают его более персонализированным, что способствует быстрой и эффективной реабилитации. Новые технологии позволяют минимизировать время на стационарное лечение и оптимизировать ресурсы. Искусственный интеллект выводит диагностику и лечение на новый уровень, предсказывая сложные клинические результаты с высокой точностью.

Aim

Aim. The analysis of scientific articles devoted to the use of artificial intelligence (AI) in diseases of the musculoskeletal system (ODE) to determine the effectiveness of the introduction of new technologies based on artificial intelligence.

Materials and Methods

Materials and Methods. For the literature review, the most cited studies on the use of AI in the diagnosis and treatment of ODE were selected, which are publicly available in scientific databases.

Results

Results. The research described in the review of scientific articles demonstrates the great potential of artificial intelligence in the diagnosis of diseases of the musculoskeletal system and shows how it can be useful for doctors and patients.

Conclusion

Conclusion. The introduction of artificial intelligence in orthopedics opens up new horizons for improving the quality of medical care. Specialized equipment and mobile applications not only facilitate the monitoring of patients' condition, but also make it more personalized, which contributes to rapid and effective rehabilitation. New technologies make it possible to minimize hospital treatment time and optimize resources. Artificial intelligence advances diagnosis and treatment to a new level by predicting complex clinical outcomes with high accuracy.

искусственный интеллектопорно-двигательный аппаратискусственные нейронные сетиArtificial IntelligenceMusculoskeletal SystemArtificial Neural NetworksReferencesЗаболевания опорно-двигательного аппарата // Всемирная организация здравоохранения. URL: https://www.who.int/ru/news-room/fact-sheets/detail/musculoskeletal-conditions (дата обращения: 29.07.2024).Musculoskeletal health. World Health Organization. URL: https://www.who.int/ru/news-room/fact-sheets/detail/musculoskeletal-conditions (date of application: 29.07.2024). (In Russ.).Bini S. A., Schilling P. L., Patel S. P., Kalore N. V., Ast M. P., Maratt J. D., Schuett D. J., Lawrie C. M., Chung C. C., Steele G. D. Digital Orthopaedics: A Glimpse Into the Future in the Midst of a Pandemic. J. Arthroplasty. 2020;35(7S):S68-S73. https://doi.org/10.1016/j.arth.2020.04.048Bini S. A., Schilling P. L., Patel S. P., Kalore N. V., Ast M. P., Maratt J. D., Schuett D. J., Lawrie C. M., Chung C. C., Steele G. D. Digital Orthopaedics: A Glimpse Into the Future in the Midst of a Pandemic. J. Arthroplasty. 2020;35(7S):S68-S73. https://doi.org/10.1016/j.arth.2020.04.048Bloomfield R. A., Williams H. A., Broberg J. S., Lanting B. A., McIsaac K. A., Teeter M. G. Machine Learning Groups Patients by Early Functional Improvement Likelihood Based on Wearable Sensor Instrumented Preoperative Timed-Up-and-Go Tests. J. Arthroplasty. 2019;34(10):2267-2271. https://doi.org/10.1016/j.arth.2019.05.061Bloomfield R. A., Williams H. A., Broberg J. S., Lanting B. A., McIsaac K. A., Teeter M. G. Machine Learning Groups Patients by Early Functional Improvement Likelihood Based on Wearable Sensor Instrumented Preoperative Timed-Up-and-Go Tests. J. Arthroplasty. 2019;34(10):2267-2271. https://doi.org/10.1016/j.arth.2019.05.061Cilla M., Borgiani E., Martínez J., Duda G. N., Checa S. Machine learning techniques for the optimization of joint replacements: Application to a short-stem hip implant. PLoS One. 2017;12(9): e0183755. https://doi.org/10.1371/journal.pone.0183755Cilla M., Borgiani E., Martínez J., Duda G. N., Checa S. Machine learning techniques for the optimization of joint replacements: Application to a short-stem hip implant. PLoS One. 2017;12(9): e0183755. https://doi.org/10.1371/journal.pone.0183755Dennis B. M., Stonko D. P., Callcut R. A., Sid well R. A., Stassen N. A., Cohen M. J., Cotton B. A., Guillamondegui O. D. Artificial neural networks can predict trauma volume and acuity regardless of center size and geography: A multicenter study. J. Trauma Acute Care Surg. 2019;87(1):181-187. https://doi.org/10.1097/TA.0000000000002320Dennis B. M., Stonko D. P., Callcut R. A., Sid well R. A., Stassen N. A., Cohen M. J., Cotton B. A., Guillamondegui O. D. Artificial neural networks can predict trauma volume and acuity regardless of center size and geography: A multicenter study. J. Trauma Acute Care Surg. 2019;87(1):181-187. https://doi.org/10.1097/TA.0000000000002320Dooley J. H., Ozdenerol E., Sharpe J. P., Magnotti L. J., Croce M. A., Fischer P. E. Location, location, location: Utilizing Needs-Based Assessment of Trauma Systems-2 in trauma system planning. J. Trauma Acute Care Surg. 2020;88(1):94-100. https://doi.org/10.1097/TA.0000000000002463Dooley J. H., Ozdenerol E., Sharpe J. P., Magnotti L. J., Croce M. A., Fischer P. E. Location, location, location: Utilizing Needs-Based Assessment of Trauma Systems-2 in trauma system planning. J. Trauma Acute Care Surg. 2020;88(1):94-100. https://doi.org/10.1097/TA.0000000000002463Galbusera F., Casaroli G., Bassani T. Artificial intelligence and machine lear ning in spine research. JOR Spine. 2019;2(1): e1044. https://doi.org/10.1002/jsp2.1044Galbusera F., Casaroli G., Bassani T. Artificial intelligence and machine lear ning in spine research. JOR Spine. 2019;2(1): e1044. https://doi.org/10.1002/jsp2.1044Ghaffar Nia N., Kaplanoglu E., Nasab A. Evaluation of artificial intelligence techniques in disease diagnosis and prediction. J. Discov Artif Intell. 2023;3(5). https://doi.org/10.1007/s44163-023-00049-5Ghaffar Nia N., Kaplanoglu E., Nasab A. Evaluation of artificial intelligence techniques in disease diagnosis and prediction. J. Discov Artif Intell. 2023;3(5). https://doi.org/10.1007/s44163-023-00049-5Hosny A., Parmar C., Quackenbush J., Schwartz L. H., Aerts H. J. W. L. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18(8):500-510. https://doi.org/10.1038/s41568-018-0016-5Hosny A., Parmar C., Quackenbush J., Schwartz L. H., Aerts H. J. W. L. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18(8):500-510. https://doi.org/10.1038/s41568-018-0016-5Jodeiri A., Zoroofi R. A., Hiasa Y., Takao M., Sugano N., Sato Y., Otake Y. Fully automatic estimation of pelvic sagittal in clination from anterior-posterior radio graphy image using deep learning frame work. Comput Methods Programs Biomed. 2020;184:105282. https://doi.org/10.1016/j.cmpb.2019.105282Jodeiri A., Zoroofi R. A., Hiasa Y., Takao M., Sugano N., Sato Y., Otake Y. Fully automatic estimation of pelvic sagittal in clination from anterior-posterior radio graphy image using deep learning frame work. Comput Methods Programs Biomed. 2020;184:105282. https://doi.org/10.1016/j.cmpb.2019.105282Karnuta J. M., Haeberle H. S., Luu B. C., Roth A. L., Molloy R. M., Nystrom L. M., Piuzzi N. S., Schaffer J. L., Chen A. F., Iorio R., Krebs V. E., Ramkumar P. N. Artificial Intelligence to Identify Arthroplasty Implants From Radiographs of the Hip. J. Arthroplasty. 2021;36(7S):S290-S294.e1. https://doi.org/10.1016/j.arth.2020.11.015Karnuta J. M., Haeberle H. S., Luu B. C., Roth A. L., Molloy R. M., Nystrom L. M., Piuzzi N. S., Schaffer J. L., Chen A. F., Iorio R., Krebs V. E., Ramkumar P. N. Artificial Intelligence to Identify Arthroplasty Implants From Radiographs of the Hip. J. Arthroplasty. 2021;36(7S):S290-S294.e1. https://doi.org/10.1016/j.arth.2020.11.015Karnuta J. M., Navarro S. M., Haeberle H. S., Billow D. G., Krebs V. E., Ramkumar P. N. Bundled Care for Hip Fractures: A Machine-Learning Approach to an Untenable Patient-Specific Payment Model. J. Orthop Trauma. 2019;33(7):324-330. https://doi.org/10.1097/BOT.0000000000001454Karnuta J. M., Navarro S. M., Haeberle H. S., Billow D. G., Krebs V. E., Ramkumar P. N. Bundled Care for Hip Fractures: A Machine-Learning Approach to an Untenable Patient-Specific Payment Model. J. Orthop Trauma. 2019;33(7):324-330. https://doi.org/10.1097/BOT.0000000000001454Kim Y. J., Ganbold B., Kim K. G. Web-Based Spine Segmentation Using Deep Learning in Computed Tomogra phy Images. Healthc Inform Res. 2020;26(1):61-67. https://doi.org/10.4258/hir.2020.26.1.61Kim Y. J., Ganbold B., Kim K. G. Web-Based Spine Segmentation Using Deep Learning in Computed Tomogra phy Images. Healthc Inform Res. 2020;26(1):61-67. https://doi.org/10.4258/hir.2020.26.1.61Lee Han-soo. Ministry approves Korea’s first AI-based medical device. Korea Bio­ medical Review. URL: https://www.koreabiomed.com/news/articleView.html?idxno=3294 (date of application: 29.07.2024).Lee Han-soo. Ministry approves Korea’s first AI-based medical device. Korea Bio­ medical Review. URL: https://www.koreabiomed.com/news/articleView.html?idxno=3294 (date of application: 29.07.2024).Lee K. C., Hsu C. C., Lin T. C., Chiang H. F., Horng G. J., Chen K. T. Prediction of Prognosis in Patients with Trauma by Using Machine Learning. Medicina (Kaunas). 2022;58(10):1379. https://doi.org/10.3390/medicina58101379Lee K. C., Hsu C. C., Lin T. C., Chiang H. F., Horng G. J., Chen K. T. Prediction of Prognosis in Patients with Trauma by Using Machine Learning. Medicina (Kaunas). 2022;58(10):1379. https://doi.org/10.3390/medicina58101379Li Y. Y., Wang J. J., Huang S. H., Kuo C. L., Chen J. Y., Liu C. F., Chu C. C. Implementation of a machine learning application in preoperative risk assessment for hip repair surgery. BMC Anes­thesiol. 2022;22(1):116. https://doi.org/10.1186/s12871-022-01648-yLi Y. Y., Wang J. J., Huang S. H., Kuo C. L., Chen J. Y., Liu C. F., Chu C. C. Implementation of a machine learning application in preoperative risk assessment for hip repair surgery. BMC Anes­thesiol. 2022;22(1):116. https://doi.org/10.1186/s12871-022-01648-yLiu F., Zhou Z., Jang H., Samsonov A., Zhao G., Kijowski R. Deep convolutional neu ral network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magn Reson Med. 2018;79(4):2379-2391. https://doi.org/10.1002/mrm.26841Liu F., Zhou Z., Jang H., Samsonov A., Zhao G., Kijowski R. Deep convolutional neu ral network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magn Reson Med. 2018;79(4):2379-2391. https://doi.org/10.1002/mrm.26841Momtazmanesh S., Nowroozi A., Rezaei N. Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Per spectives: A State-of-the-Art Review. Rheu matol Ther. 2022;9(5):1249-1304. https://doi.org/10.1007/s40744-022-00475-4Momtazmanesh S., Nowroozi A., Rezaei N. Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Per spectives: A State-of-the-Art Review. Rheu matol Ther. 2022;9(5):1249-1304. https://doi.org/10.1007/s40744-022-00475-4Myers T. G., Ramkumar P. N., Ricciardi B. F., Urish K. L., Kipper J., Ketonis C. Artificial Intelligence and Orthopaedics: An Introduction for Clinicians. J. Bone Joint Surg Am. 2020;102(9):830-840. https://doi.org/10.2106/JBJS.19.01128Myers T. G., Ramkumar P. N., Ricciardi B. F., Urish K. L., Kipper J., Ketonis C. Artificial Intelligence and Orthopaedics: An Introduction for Clinicians. J. Bone Joint Surg Am. 2020;102(9):830-840. https://doi.org/10.2106/JBJS.19.01128Olczak J., Fahlberg N., Maki A., Razavian A. S., Jilert A., Stark A., Sköl denberg O., Gordon M. Artificial intelligence for analyzing orthopedic trauma radiographs. Acta Orthop. 2017;88(6): 581-586. https://doi.org/10.1080/17453674.2017.1344459Olczak J., Fahlberg N., Maki A., Razavian A. S., Jilert A., Stark A., Sköl denberg O., Gordon M. Artificial intelligence for analyzing orthopedic trauma radiographs. Acta Orthop. 2017;88(6): 581-586. https://doi.org/10.1080/17453674.2017.1344459Pranata Y. D., Wang K. C., Wang J. C., Idram I., Lai J. Y., Liu J. W., Hsieh I. H. Deep learning and SURF for automated classification and detection of calcaneus fractures in CT ima ges. Comput Methods Programs Biomed. 2019;171:27-37. https://doi.org/10.1016/j.cmpb.2019.02.006Pranata Y. D., Wang K. C., Wang J. C., Idram I., Lai J. Y., Liu J. W., Hsieh I. H. Deep learning and SURF for automated classification and detection of calcaneus fractures in CT ima ges. Comput Methods Programs Biomed. 2019;171:27-37. https://doi.org/10.1016/j.cmpb.2019.02.006Ramkumar P. N., Haeberle H. S., Bloomfield M. R., Schaffer J. L., Kamath A. F., Patterson B. M., Krebs V. E. Artificial Intelligence and Arthroplasty at a Single Institution: Real-World Applications of Machine Learning to Big Data, Value-Ba sed Care, Mobile Health, and Remote Patient Monitoring. J. Arthroplasty. 2019;34(10): 2204-2209. https://doi.org/10.1016/j.arth.2019.06.018Ramkumar P. N., Haeberle H. S., Bloomfield M. R., Schaffer J. L., Kamath A. F., Patterson B. M., Krebs V. E. Artificial Intelligence and Arthroplasty at a Single Institution: Real-World Applications of Machine Learning to Big Data, Value-Ba sed Care, Mobile Health, and Remote Patient Monitoring. J. Arthroplasty. 2019;34(10): 2204-2209. https://doi.org/10.1016/j.arth.2019.06.018Shah R. F., Martinez A. M., Pedoia V., Majumdar S., Vail T. P., Bini S. A. Variation in the Thickness of Knee Cartilage. The Use of a Novel Machine Learning Algo rithm for Cartilage Segmentation of Magnetic Resonance Images. J. Arthroplasty. 2019; 34(10):2210-2215. https://doi.org/10.1016/j.arth.2019.07.022Shah R. F., Martinez A. M., Pedoia V., Majumdar S., Vail T. P., Bini S. A. Variation in the Thickness of Knee Cartilage. The Use of a Novel Machine Learning Algo rithm for Cartilage Segmentation of Magnetic Resonance Images. J. Arthroplasty. 2019; 34(10):2210-2215. https://doi.org/10.1016/j.arth.2019.07.022Urakawa T., Tanaka Y., Goto S., Matsuzawa H., Watanabe K., Endo N. Detecting intertrochanteric hip fractures with orthopedist-level accuracy using a deep convolutional neural network. Skeletal Radiol. 2019;48(2):239-244. https://doi.org/10.1007/s00256-018-3016-3Urakawa T., Tanaka Y., Goto S., Matsuzawa H., Watanabe K., Endo N. Detecting intertrochanteric hip fractures with orthopedist-level accuracy using a deep convolutional neural network. Skeletal Radiol. 2019;48(2):239-244. https://doi.org/10.1007/s00256-018-3016-3

The authors declare that there are no conflicts of interest present.