Artificial Intelligence and the State of the Art of Orthopedic Surgery

Document Type : CURRENT CONCEPTS REVIEW

Authors

1 1 Orthopedic Research Center, Department of Orthopedic Surgery, Mashhad University of Medical Sciences, Mashhad, Iran 2 Bone and Joint Research laboratory, Ghaem Hospital, Mashhad University of Medical Sciences, Mashhad, Iran

2 1 Orthopedic Research Center, Department of Orthopedic Surgery, Mashhad University of Medical Sciences, Mashhad, Iran 2 Bone and Joint Research laboratory, Ghaem Hospital, Mashhad University of Medical Sciences, Mashhad, Iran 3 Department of Regenerative Medicine and Cell Therapy, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran

10.22038/abjs.2024.84231.3829

Abstract

Artificial Intelligence (AI) is rapidly transforming healthcare, particularly in orthopedics, by enhancing diagnostic accuracy, surgical planning, and personalized treatment. This review explores current applications of AI in orthopedics, focusing on its contributions to diagnostics and surgical procedures. Key methodologies such as artificial neural networks (ANNs), convolutional neural networks (CNNs), support vector machines (SVMs), and ensemble learning have significantly improved diagnostic precision and patient care. For instance, CNN-based models excel in tasks like fracture detection and osteoarthritis grading, achieving high sensitivity and specificity. In surgical contexts, AI enhances procedures through robotic assistance and optimized preoperative planning, aiding in prosthetic sizing and minimizing complications. Additionally, predictive analytics during postoperative care enable tailored rehabilitation programs that improve recovery times. Despite these advancements, challenges such as data standardization and algorithm transparency hinder widespread adoption. Addressing these issues is crucial for maximizing AI's potential in orthopedic practice. This review emphasizes the synergistic relationship between AI and clinical expertise, highlighting opportunities to enhance diagnostics and streamline surgical procedures, ultimately driving patient-centric care.
        Level of evidence: V

Keywords

Main Subjects

References

  1. Hirani R, Noruzi K, Khuram H, et al. Artificial Intelligence and Healthcare: A Journey through History, Present Innovations, and Future Possibilities. Life (Basel). 2024; 14(5):557, doi: 10.3390/life14050557.
  2. Beyaz S. A brief history of artificial intelligence and robotic surgery in orthopedics & traumatology and future expectations. Jt Dis Relat Surg. 2020; 31(3):653-5, doi: 10.5606/ehc.2020.75300.
  3. 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.
  4. Lisacek-Kiosoglous AB, Powling AS, Fontalis A, Gabr A, Mazomenos E, Haddad FS. Artificial intelligence in orthopaedic surgery: exploring its applications, limitations, and future direction. Bone Joint Res. 2023; 12(7):447-54, doi: 10.1302/2046-3758.127.BJR-2023-0111.R1.
  5. Bini SA. Artificial Intelligence, Machine Learning, Deep Learning, and Cognitive Computing: What Do These Terms Mean and How Will They Impact Health Care? J Arthroplasty. 2018; 33(8):2358-61, doi: 10.1016/j.arth.2018.02.067.
  6. Haeberle HS, Helm JM, Navarro SM, et al. Artificial Intelligence and Machine Learning in Lower Extremity Arthroplasty: A Review. J Arthroplasty. 2019; 34(10):2201-3, doi: 10.1016/j.arth.2019.05.055.
  7. Kayani B, Konan S, Huq SS, Ibrahim MS, Ayuob A, Haddad FS. The learning curve of robotic-arm assisted acetabular cup positioning during total hip arthroplasty. Hip Int. 2021; 31(3):311-9, doi: 10.1177/1120700019889334.
  8. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019; 25(1):44-56, doi: 10.1038/s41591-018-0300-7.
  9. Collins GS, Moons KGM. Reporting of artificial intelligence prediction models. Lancet. 2019; 393(10181):1577-9. doi: 10.1016/S0140-6736(19)30037-6.
  10. Kutbi M. Artificial Intelligence-Based Applications for Bone Fracture Detection Using Medical Images: A Systematic Review. Diagnostics (Basel). 2024; 14(17):1879, doi: 10.3390/diagnostics14171879.
  11. Silver AE, Lungren MP, Johnson ME, O’Driscoll SW, An KN, Hughes RE. Using support vector machines to optimally classify rotator cuff strength data and quantify post-operative strength in rotator cuff tear patients. J Biomech. 2006; 39(5):973-9, doi: 10.1016/j.jbiomech.2005.01.011.
  12. Begg R, Kamruzzaman J. A machine learning approach for automated recognition of movement patterns using basic, kinetic and kinematic gait data. J Biomech. 2005; 38(3):401-8, doi: 10.1016/j.jbiomech.2004.05.002.
  13. Abedin J, Antony J, McGuinness K, et al. Predicting knee osteoarthritis severity: comparative modeling based on patient's data and plain X-ray images. Sci Rep. 2019; 9(1):5761, doi: 10.1038/s41598-019-42215-9.
  14. Kwon SB, Ro DH, Song MK, Han HS, Lee MC, Kim HC. Identifying key gait features associated with the radiological grade of knee osteoarthritis. Osteoarthritis Cartilage. 2019; 27(12):1755-60, doi: 10.1016/j.joca.2019.07.014.
  15. Kotti M, Duffell LD, Faisal AA, McGregor AH. Detecting knee osteoarthritis and its discriminating parameters using random forests. Med Eng Phys. 2017; 43:19-29, doi: 10.1016/j.medengphy.2017.02.004.
  16. Tian C, Gao Y, Rui C, Qin S, Shi L, Rui Y. Artificial intelligence in orthopaedic trauma. EngMedicine. 2024; 1(2):100020, doi: 10.1016/j.engmed.2024.100020.
  17. Olczak J, Fahlberg N, Maki A, et al. Artificial intelligence for analyzing orthopedic trauma radiographs. Acta Orthop. 2017; 88(6):581-6, doi: 10.1080/17453674.2017.1344459.
  18. Yang W, Gao T, Liu X, et al. Clinical application of artificial intelligence-assisted three-dimensional planning in direct anterior approach hip arthroplasty. Int Orthop.2024; 48(3):773-83, doi: 10.1007/s00264-023-06029-9.
  19. Katakam A, Karhade AV, Collins A, et al. Development of machine learning algorithms to predict achievement of minimal clinically important difference for the KOOS-PS following total knee arthroplasty. J Orthop Res. 2022; 40(4):808-15, doi: 10.1002/jor.25125.
  20. Anderson AB, Grazal CF, Balazs GC, Potter BK, Dickens JF, Forsberg JA. Can Predictive Modeling Tools Identify Patients at High Risk of Prolonged Opioid Use After ACL Reconstruction? Clin Orthop Relat Res. 2020; 478(7):0-1618, doi: 10.1097/CORR.0000000000001251.
  21. Huber M, Kurz C, Leidl R. Predicting patient-reported outcomes following hip and knee replacement surgery using supervised machine learning. BMC Med Inform Decis Mak. 2019; 19(1):3, doi: 10.1186/s12911-018-0731-6.
  22. Hetherington J, Lessoway V, Gunka V, Abolmaesumi P, Rohling R. SLIDE: automatic spine level identification system using a deep convolutional neural network. Int J Comput Assist Radiol Surg. 2017; 12(7):1189-98, doi: 10.1007/s11548-017-1575-8.
  23. Consalvo S, Hinterwimmer F, Neumann J, et al. Two-Phase Deep Learning Algorithm for Detection and Differentiation of Ewing Sarcoma and Acute Osteomyelitis in Paediatric Radiographs. Anticancer Res. 2022; 42(9):4371-80, doi: 10.21873/anticanres.15937.
  24. Li W, Zhang Y, Zhou X, et al. Ensemble learning-assisted prediction of prolonged hospital length of stay after spine correction surgery: a multi-center cohort study. J Orthop Surg Res. 2024; 19(1):112, doi: 10.1186/s13018-024-04576-4.
  25. Forsberg JA, Eberhardt J, Boland PJ, Wedin R, Healey JH. Estimating survival in patients with operable skeletal metastases: an application of a bayesian belief network. PLoS One. 2011; 6(5):e19956, doi: 10.1371/journal.pone.0019956.
  26. McLachlan S, Dube K, Hitman GA, Fenton NE, Kyrimi E. Bayesian networks in healthcare: Distribution by medical condition. Artif Intell Med. 2020; 107:101912, doi: 10.1016/j.artmed.2020.101912.
  27. Gan K, Xu D, Lin Y, et al. Artificial intelligence detection of distal radius fractures: a comparison between the convolutional neural network and professional assessments. Acta Orthop. 2019; 90(4):394-400, doi: 10.1080/17453674.2019.1600125.
  28. 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):468-73, doi: 10.1080/17453674.2018.1453714.
  29. Cheng CT, Ho TY, Lee TY, et al. Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs. Eur Radiol. 2019; 29(10):5469-77, doi: 10.1007/s00330-019-06167-y.
  30. Köktaş NŞ, Yalabik N, Yavuzer G, Duin RP. A multi-classifier for grading knee osteoarthritis using gait analysis. Pattern Recognition Letters. 2010; 31(9):898-904, doi: 10.1016/j.patrec.2010.01.003.
  31. Hussain D, Han SM. Computer-aided osteoporosis detection from DXA imaging. Comput Methods Programs Biomed. 2019; 173:87-107, doi: 10.1016/j.cmpb.2019.03.011.
  32. Yu X, Ye C, Xiang L. Application of artificial neural network in the diagnostic system of osteoporosis. Neurocomputing. 2016; 214:376-81, doi: 10.1016/j.neucom.2016.06.023.
  33. Zhao K, Zhang M, Xie Z, et al. Deep Learning Assisted Diagnosis of Musculoskeletal Tumors Based on Contrast-Enhanced Magnetic Resonance Imaging. J Magn Reson Imaging. 2022; 56(1):99-107, doi: 10.1002/jmri.28025.
  34. Wei C, Quan T, Wang KY, et al. Artificial neural network prediction of same-day discharge following primary total knee arthroplasty based on preoperative and intraoperative variables. Bone Joint J. 2021; 103-b (8):1358-66, doi: 10.1302/0301-620X.103B8.BJJ-2020-1013.R2.
  35. Huo J, Huang G, Han D, et al. Value of 3D preoperative planning for primary total hip arthroplasty based on artificial intelligence technology. J Orthop Surg Res. 2021; 16(1):156, doi: 10.1186/s13018-021-02294-9.
  36. Lan Q, Li S, Zhang J, Guo H, Yan L, Tang F. Reliable prediction of implant size and axial alignment in AI-based 3D preoperative planning for total knee arthroplasty. Sci Rep. 2024; 14(1):16971, doi: 10.1038/s41598-024-67276-3.
  37. Al Zoubi F, Kashanian K, Beaule P, Fallavollita P. First deployment of artificial intelligence recommendations in orthopedic surgery. Front Artif Intell. 2024; 7:1342234, doi: 10.3389/frai.2024.1342234.
  38. Karhade AV, Ogink PT, Thio Q, et al. Development of machine learning algorithms for prediction of prolonged opioid prescription after surgery for lumbar disc herniation. Spine J.

 

       2019;19(11):1764-71, doi: 10.1016/j.spinee.2019.06.002.

  1. Jo C, Ko S, Shin WC, et al. Transfusion after total knee arthroplasty can be predicted using the machine learning algorithm. Knee Surg Sports Traumatol Arthrosc. 2020;28(6):1757-64, doi: 10.1007/s00167-019-05602-3.
  2. Bini SA, Shah RF, Bendich I, Patterson JT, Hwang KM, Zaid MB. Machine Learning Algorithms Can Use Wearable Sensor Data to Accurately Predict Six-Week Patient-Reported Outcome Scores Following Joint Replacement in a Prospective Trial. J Arthroplasty. 2019;34(10):2242-7, doi: 10.1016/j.arth.2019.07.024.
  3. Hernigou P, Barbier O, Chenaie P. Hip arthroplasty dislocation risk calculator: evaluation of one million primary implants and twenty-five thousand dislocations with deep learning artificial intelligence in a systematic review of reviews. Int Orthop. 2023;47(2):557-71, doi: 10.1007/s00264-022-05644-2.
  4. Rouzrokh P, Ramazanian T, Wyles CC, et al. Deep Learning Artificial Intelligence Model for Assessment of Hip Dislocation Risk Following Primary Total Hip Arthroplasty From Postoperative Radiographs. J Arthroplasty. 2021;36(6):2197-203.e3, doi: 10.1016/j.arth.2021.02.028.
  5. Khosravi B, Rouzrokh P, Maradit Kremers H, et al. Patient-specific Hip Arthroplasty Dislocation Risk Calculator: An Explainable Multimodal Machine Learning–based Approach. Radiol Artif Intell. 2022;4(6):e220067, doi: 10.1148/ryai.220067.
  6. Daliri M, Rajabi M, Rastaghi S, et al. Calculation of the Forearm and Hand Three-Dimensional Anthropometry Based on Two-Dimensional Image Feature Extraction: An Approach for Cock-up Splint Design. Arch Bone Jt Surg. 2024;12(9):622-30, doi: 10.22038/ABJS.2024.73439.3435.
  7. Shahbaz Y, Daliri M, Ebrahimzadeh MH, Sayyed Hosseinian SH. Factors Associated with the Clinical Outcomes of Ankle Instability Surgery. Arch Bone Jt Surg. 2024.doi: 10.22038/abjs.2024.74008.3427.
The Archives of Bone and Joint Surgery
Volume 13, Issue 1
January 2025
Pages 17-22

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History

  • Receive Date: 21 November 2024
  • Revise Date: 28 December 2024
  • Accept Date: 09 December 2024

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