سامانه مدیریت نشریات علمی دانشگاه علوم پزشکی مشهد

Ghouchani, Azadeh, Ebrahimzadeh, Mohammad. (1404). AI Revolution in Orthopedic Biomechanics: From Fracture Classification to Real-Time Simulations. , 13(10), 607-610. doi: 10.22038/abjs.2025.88346.4012
Azadeh Ghouchani; Mohammad H. Ebrahimzadeh. "AI Revolution in Orthopedic Biomechanics: From Fracture Classification to Real-Time Simulations". , 13, 10, 1404, 607-610. doi: 10.22038/abjs.2025.88346.4012
Ghouchani, Azadeh, Ebrahimzadeh, Mohammad. (1404). 'AI Revolution in Orthopedic Biomechanics: From Fracture Classification to Real-Time Simulations', , 13(10), pp. 607-610. doi: 10.22038/abjs.2025.88346.4012
Ghouchani, Azadeh, Ebrahimzadeh, Mohammad. AI Revolution in Orthopedic Biomechanics: From Fracture Classification to Real-Time Simulations. , 1404; 13(10): 607-610. doi: 10.22038/abjs.2025.88346.4012

AI Revolution in Orthopedic Biomechanics: From Fracture Classification to Real-Time Simulations

The Archives of Bone and Joint Surgery
مقاله 3، دوره 13، شماره 10، دی 2025، صفحه 607-610    اصل مقاله (561 K)
نوع مقاله: EDITORIAL
شناسه دیجیتال (DOI): 10.22038/abjs.2025.88346.4012
نویسندگان
Azadeh Ghouchani* 1؛ Mohammad H. Ebrahimzadeh2
1Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran
2Orthopedic Research Center, Department of Orthopedic Surgery, Mashhad University of Medical Science, Mashhad, Iran- Bone and Joint Research Laboratory, Ghaem Hospital, Mashhad University of Medical Sciences, Mashhad, Iran
چکیده
Over recent decades, orthopedic biomechanics has evolved from classical mechanics-based models to advanced computational simulations. Innovations in modeling and material analysis—such as finite element method (FEM), which has revolutionized the simulation of complex bone responses, and advanced imaging modalities like 3D CT and MRI, which offer detailed anatomical insights—have deepened our understanding of bone behavior. The incorporation of patient-specific data has further enabled personalized fracture analysis and treatment strategies, enhancing precision in orthopedic care.

Despite significant advancements, orthopedic biomechanics still faces challenges—particularly in comprehensively understanding complex fracture patterns and effectively applying real-time intraoperative solutions. This editorial explores how artificial intelligence (AI) driven innovations can overcome these persistent challenges and enhance diagnostic accuracy, support personalized treatment plans, and facilitate improved intraoperative decision-making from an engineering perspective.
کلیدواژه‌ها
ARTIFICIAL INTELLIGENCE؛ ORTHOPEDIC BIOMECHANICS؛ FRACTURE CLASSIFICATION؛ REAL TIME SIMULATION
مراجع
  1. Park J-W, Jo W-L, Park BK, et al. Reliability of the 2018 Revised Version of AO/OTA Classification for Femoral Shaft Fractures. Clin Orthop Surg. 2024;16(5):688-693. doi: 10.4055/cios23292.
  2. Marongiu G, Leinardi L, Congia S, Frigau L, Mola F, Capone A. Reliability and reproducibility of the new AO/OTA 2018 classification system for proximal humeral fractures: a comparison of three different classification systems. J Orthop Traumatol. 2020;21(1):4. doi: 10.1186/s10195-020-0543-1.
  3. Pflüger P, Harder F, Müller K, Biberthaler P, Crönlein M. Evaluation of ankle fracture classification systems in 193 trimalleolar ankle fractures. Eur J Trauma Emerg Surg. 2022;48(5):4181-4188. doi: 10.1007/s00068-022-01959-2.
  4. Cueto E, Chinesta F. Real time simulation for computational surgery: a review. Advanced Modeling and Simulation in Engineering Sciences. 2014;1(1):11.
  5. Zhang J-y, Yang J-m, Wang X-m, et al. Application and Prospects of Deep Learning Technology in Fracture Diagnosis. Curr Med Sci. 2024;44(6):1132-1140. doi: 10.1007/s11596-024-2928-5.
  6. Wang Y, Li Y, Lin G, et al. Lower-extremity fatigue fracture detection and grading based on deep learning models of radiographs. Eur Radiol. 2023;33(1):555-565. doi: 10.1007/s00330-022-08950-w.
  7. Oude Nijhuis KD, Dankelman LH, Wiersma JP, et al. AI for detection, classification and prediction of loss of alignment of distal radius fractures; a systematic review. Eur J Trauma Emerg Surg. 2024;50(6):2819-2831. doi: 10.1007/s00068-024-02557-0.
  8. Tanzi L, Vezzetti E, Moreno R, Aprato A, Audisio A, Massè A. Hierarchical fracture classification of proximal femur X-Ray images using a multistage Deep Learning approach. Eur J Radiol. 2020:133:109373. doi: 10.1016/j.ejrad.2020.109373.
  9. Chung SW, Han SS, Lee JW, et al. Automated detection and classification of the proximal humerus fracture by using deep learning algorithm. Acta Orthop. 2018;89(4):468-473. doi: 10.1080/17453674.2018.1453714.
  10. Yao X, Zhou K, Lv B, Wang L, Xie J, Fu X, Yuan J, Zhang Y. 3D mapping and classification of tibial plateau fractures. Bone Joint Res. 2020 Jul 23;9(6):258-267. doi: 10.1302/2046-3758.96.BJR-2019-0382.R2.
  11. McGonagle L, Cordier T, Link BC, Rickman MS, Solomon LB. Tibia plateau fracture mapping and its influence on fracture fixation. J Orthop Traumatol. 2019;20(1):12. doi: 10.1186/s10195-019-0519-1.
  12. Li J, Tang S, Zhang H, et al. Clustering of morphological fracture lines for identifying intertrochanteric fracture classification with Hausdorff distance–based K-means approach. Injury. 2019;50(4):939-949. doi: 10.1016/j.injury.2019.03.032.
  13. Pingili R. Transforming Surgical Planning with AI, Hyper-Automation, and RPA. International Research Journal of Modernization in Engineering Technology and Science. 2024;6.
  14. Zhang J, Zhao Y, Shone F, et al. Physics-informed deep learning for musculoskeletal modeling: Predicting muscle forces and joint kinematics from surface EMG. IEEE Trans Neural Syst Rehabil Eng. 2023:31:484-493. doi: 10.1109/TNSRE.2022.3226860.
  15. Zha Q, Shen S, Ma Z, Yu M, Bi H, Yang H. AI-based biplane X-ray image-guided method for distal radius fracture reduction. Front Bioeng Biotechnol. 2025:13:1502669. doi: 10.3389/fbioe.2025.1502669.
  16. Stanev D, Filip K, Bitzas D, et al. Real-time musculoskeletal kinematics and dynamics analysis using marker-and IMU-based solutions in rehabilitation. Sensors (Basel). 2021;21(5):1804. doi: 10.3390/s21051804.
  17. Weber J, Stetter BJ. Effects of different wearable sensors and locomotion tasks on machine learning-based joint moment prediction. Current Issues in Sport Science (CISS). 2024;9(4):016-.
  18. Tejedor P, Denost Q. The integration of artificial intelligence with robotic instruments in surgical practice. British Journal of Surgery. 2024;111(10):znae248.
  19. Wah JNK. Revolutionizing surgery: AI and robotics for precision, risk reduction, and innovation. J Robot Surg. 2025;19(1):47. doi: 10.1007/s11701-024-02205-0.
  20. Monge J, Ribeiro G, Raimundo A, Postolache O, Santos J. AI-based smart sensing and AR for gait rehabilitation assessment. Information. 2023;14(7):355.
  21. Zhang M, Yao N, He Y, Cao K, Chen Q. Real-time processing and intelligent analysis of biomechanical data based on 5G and artificial intelligence. Molecular & Cellular Biomechanics. 2025;22(5):1094. doi: 10.62617/mcb1094.
  22. Monge J, Ribeiro G, Raimundo A, Postolache O, Santos J. AI-based smart sensing and AR for gait rehabilitation assessment. Information. 2023;14(7):355. doi: 10.3390/info14070355.
  23. Ukai K, Rahman R, Yagi N, et al. Detecting pelvic fracture on 3D-CT using deep convolutional neural networks with multi-orientated slab images. Sci Rep. 2021;11(1):11716. doi: 10.1038/s41598-021-91144-z.
  24. Khojastehnezhad MA, Youseflee P, Moradi A, Jirofti N, Ebrahimzadeh MH. Artificial Intelligence and the State of the Art of Orthopedic Surgery. Arch Bone Jt Surg. 2025;13(1):17-22. doi: 10.22038/ABJS.2024.84231.3829.
  25. Yao JJ, Lopez RD, Rizk AA, Aggarwal M, Namdari S. Evaluation of a Popular Large Language Model in Orthopedic Literature Review: Comparison to Previously Published Reviews. Arch Bone Jt Surg. 2025;13(8):460-469. doi: 10.22038/ABJS.2025.84896.3874.
  26. Ibrahim M, Al Khalil Y, Amirrajab S, et al. Generative AI for synthetic data across multiple medical modalities: A systematic review of recent developments and challenges. Comput Biol Med. 2025:189:109834. doi: 10.1016/j.compbiomed.2025.109834.
  27. Gupta N, Khatri K, Malik Y, et al. Exploring prospects, hurdles, and road ahead for generative artificial intelligence in orthopedic education and training. BMC Med Educ. 2024;24(1):1544. doi: 10.1186/s12909-024-06592-8.
  28. Jung J, Dai J, Liu B, Wu Q. Artificial intelligence in fracture detection with different image modalities and data types: A systematic review and meta-analysis. PLOS Digit Health. 2024;3(1):e0000438. doi: 10.1371/journal.pdig.0000438.
  29. Gao Y, Zhao Y, Liu Y, et al. A new distal radius fracture classification depending on the specific fragments through machine learning clustering method. BMC Musculoskelet Disord. 2024;25(1):1085. doi: 10.1186/s12891-024-08215-1.
  30. Deng Z, Xiang N, Pan J. State of the art in immersive interactive technologies for surgery simulation: a review and prospective. Bioengineering (Basel).2023;10(12):1346. doi: 10.3390/bioengineering10121346.
  31. Bicer EK, Fangerau H, Sur H. Artifical intelligence use in orthopedics: an ethical point of view. EFORT Open Rev. 2023;8(8):592-596. doi: 10.1530/EOR-23-0083.
  32. Weiner EB, Dankwa-Mullan I, Nelson WA, Hassanpour S. Ethical challenges and evolving strategies in the integration of artificial intelligence into clinical practice. PLOS Digit Health. 2025;4(4):e0000810. doi: 10.1371/journal.pdig.0000810.
  33. Goktas P, Grzybowski A. Shaping the future of healthcare: Ethical clinical challenges and pathways to trustworthy AI. J Clin Med. 2025;14(5):1605. doi: 10.3390/jcm14051605.
  34. Bruey K, Kachooei A. Applications, Implications, and Drawbacks of Artificial Intelligence in Medical Publications. Arch Bone Jt Surg. 2025;13(1):1-3. doi: 10.22038/ABJS.2024.82343.3751.
آمار
تعداد مشاهده مقاله: 201
تعداد دریافت فایل اصل مقاله: 197