Artificial intelligence and orthopaedics
DOI:
https://doi.org/10.18203/issn.2455-4510.IntJResOrthop20243147Keywords:
Artificial intelligence, Orthopaedics, AIAbstract
Artificial intelligence is the next big thing in human history. It is set to affect large and small industries, entertainment, agriculture, literature, research, and healthcare. The field of Artificial intelligence includes machine learning and deep learning, which denote an increased level of specialization. These changes are also set to influence orthopedic trauma, from diagnostics, clinical assessment, surgical intervention, rehabilitation, and outcome prediction. Some of these fields will be affected more than others. We carried out a narrative review of artificial intelligence and orthopaedic surgery to provide an updated overview of the current and future applications. AI is set to revolutionize radiological diagnostics, outcome measurement, and rehabilitation in orthopedic trauma. However, concerns about clinical decision-making and intervention remain. Ethical concerns, regulation, and superiority over traditional methods of treatment also need to be assessed. Until then, the role of AI in orthopaedic trauma remains in the realm of possibilities.
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References
Farhadi F, Barnes MR, Sugito HR, Sin JM, Henderson ER, Levy JJ. Applications of artificial intelligence in orthopaedic surgery. Front Med Technol. 2022;4:995526.
Vaish A, Migliorini F, Vaishya R. Artificial intelligence in foot and ankle surgery: current concepts. Orthopadie (Heidelb). 2023;52(12):1011-6.
A proposal for the dartmouth summer research project on artificial intelligence. Front Med Technol. 1955;2:34.
Oettl FC, Pareek A, Winkler PW, Zsidai B, Pruneski JA, Senorski EH, et al. ESSKA Artificial Intelligence Working Group. A practical guide to the implementation of AI in orthopaedic research, Part 6: How to evaluate the performance of AI research? J Exp Orthop. 2024;11(3):12039.
Khan A, Laghari AA, Ahmed Awan S. Machine learning in computer vision: a review. Endorsed Scal Inf Syst. 2021;8(32):4.
Liu XS, Nie R, Duan AW, Yang L, Li X, Zhang LT, et al. YOLOX-SwinT algorithm improves the accuracy of AO/OTA classification of intertrochanteric fractures by orthopedic trauma surgeons. Chin J Traumatol. 2024;12(24):51-8.
Bhatnagar A, Kekatpure AL, Velagala VR, Kekatpure A. A Review on the use of artificial intelligence in fracture detection. Cureus. 2024;16(4):58364.
Dell'Aria A, Tack D, Saddiki N, Makdoud S, Alexiou J, De Hemptinne FX, et al. Radiographic detection of post-traumatic bone fractures: contribution of artificial intelligence software to the analysis of senior and junior radiologists. J Belg Soc Radiol. 2024;108(1):44.
Yuh WT, Khil EK, Yoon YS, Kim B, Yoon H, Lim J, et al. Deep learning-assisted quantitative measurement of thoracolumbar fracture features on lateral radiographs. Neurospine. 2024;21(1):30-43.
Wilson NA, Jehn M, York S, Davis CM. Revision total hip and knee arthroplasty implant identification: implications for use of Unique Device Identification 2012 AAHKS member survey results. J Arthroplasty. 2014;29(2):251-5.
Krogue JD, Cheng KV, Hwang KM, Toogood P, Meinberg EG, Geiger EJ, et al. Automatic hip fracture identification and functional subclassification with deep learning. Radiol Artif Intell. 2020;2(2):190023.
Zhao XL, Shen JJ, Feng KH, Chen ZW, Si YL, Zhang X, et al. Comparison of the efficacy of TiRobot orthopaedic robot assisted F screw technique and inverted triangle parallel nail internal fixation in the treatment of unstable femoral neck fractures. Zhongguo Gu Shang. 2024;37(2):129-34.
Jiang SH, Zhang CK, Jia FT, Chen Q, Xu M, Yang PL, et al. Orthopaedic robot assisted closed reduction and cannulated screw internal fixation for the treatment of femoral neck fractures. Zhongguo Gu Shang. 2024;37(2):119-23.
Rakhshankhah N, Abbaszadeh M, Kazemi A, Rezaei SS, Roozpeykar S, Arabfard M. Deep learning approach to femoral AVN detection in digital radiography: differentiating patients and pre-collapse stages. BMC Musculoskelet Disord. 2024;25(1):547.
Paik S, Park J, Hong JY, Han SW. Deep learning application of vertebral compression fracture detection using mask R-CNN. Sci Rep. 2024;14(1):16308.
Lambrechts A, Wirix-Speetjens R, Maes F, Van Huffel S. Artificial intelligence-based patient-specific preoperative planning algorithm for total knee arthroplasty. Front Robot AI. 2022;8(9):840282.
Fontana MA, Lyman S, Sarker GK, Padgett DE, MacLean CH. Can machine learning algorithms predict which patients will achieve minimally clinically important differences from total joint arthroplasty? Clin Orthop Relat Res. 2019;477(6):1267-79.
Oude Nijhuis KD, Dankelman LHM, Wiersma JP, Barvelink B, IJpma FFA, Verhofstad MHJ, et al. Machine learning consortium. AI for detection, classification and prediction of loss of alignment of distal radius fractures; a systematic review. Eur J Trauma Emerg Surg. 2024;68:24-57.
Knee CJ, Campbell RJ, Graham DJ, Handford C, Symes M, Sivakumar BS. Examining the role of ChatGPT in the management of distal radius fractures: insights into its accuracy and consistency. ANZ J Surg. 2024;19:143.
Silva G, Ashford R. Using Artificial Intelligence to predict outcomes of operatively managed neck of femur fractures. Br J Hosp Med. 2024;85(6):1-12.
Yao W, Wang Y, Zhao X, He M, Wang Q, Liu H, Zhao J. Automatic diagnosis of pediatric supracondylar humerus fractures using radiomics-based machine learning. Medicine (Baltimore). 2024;103(23):38503.
Kasapovic A, Ali T, Babasiz M, Bojko J, Gathen M, Kaczmarczyk R, Roos J. Does the information quality of ChatGPT meet the requirements of orthopedics and trauma surgery? Cureus. 2024;16(5):60318.
Kaarre J, Feldt R, Zsidai B, Senorski EH, Rydberg EM, Wolf O, et al. ChatGPT can yield valuable responses in the context of orthopaedic trauma surgery. J Exp Orthop. 2024;11(3):12047.
Dai A, Liu H, Shen P, Feng Y, Zhong Y, Ma M, et al. Incorporating preoperative frailty to assist in early prediction of postoperative pneumonia in elderly patients with hip fractures: an externally validated online interpretable machine learning model. BMC Geriatr. 2024;24(1):472.
Whiteside LA, Roy ME. Use of an artificial intelligence device for evaluating blood loss in complex major orthopaedic surgery procedures. J Arthroplasty. 2024;8(1):53-8.
Gutierrez-Naranjo JM, Moreira A, Valero-Moreno E, Bullock TS, Ogden LA, Zelle BA. A machine learning model to predict surgical site infection after surgery of lower extremity fractures. Int Orthop. 2024;48(7):1887-96.
Lai CH, Mok PK, Chau WW, Law SW. Application of machine learning models on predicting the length of hospital stay in fragility fracture patients. BMC Med Inform Decis Mak. 2024;24(1):26.
Youssef Y, De Wet D, Back DA, Scherer J. Digitalization in orthopaedics: a narrative review. Front Surg. 2024;10:1325423.
Shariatzadeh H, Dashtbozorg A, Gorjizadeh N. Association of distal radial fracture with comorbidities: model development and validation. Injury. 2024;55(7):111607.
Chalhoub R, Mouawad A, Aoun M, Daher M, El-Sett P, Kreichati G, Kharrat K, et al. Will ChatGPT be able to replace a spine surgeon in the clinical setting? World Neurosurg. 2024;185:648-52.
Lex JR, Di Michele J, Koucheki R, Pincus D, Whyne C, Ravi B. Artificial intelligence for hip fracture detection and outcome prediction: A systematic review and meta-analysis. JAMA Netw Open. 2023;6(3):233-391.