Machine learning in orthopaedic surgery

Table 1 Summary of machine learning for orthopaedic surgery risk assessment

Ref.	Conclusion
Bevevino et al[26]	ANN capable of accurately estimating the likelihood of amputation
Gowd et al[25]	Supervised ML outperformed ASA classification models in predicting adverse events, transfusion, extended length of stay, surgical site infection, return to operating room, and readmission
Harris et al[24]	ML was moderately accurate in predicting 30-d mortality and cardiac complications after elective primary TJA
Kim et al[23]	ANN more accurate than ASA in predicting mortality, VTE, cardiac and wound complications following posterior lumbar spine fusion

ML: Machine learning; ANN: Artificial neural network; ASA: American Society of Anesthesiologists; TJA: Total joint arthroplasty; VTE: Venous thromboembolism.