Machine learning in orthopaedic surgery

Table 2 Summary of machine learning for orthopaedic surgery outcomes assessment

Ref.	Conclusion
Bongers et al[40]	ML algorithm overestimated ability to predict 5-year survival in patients with chondrosarcoma
Fontana et al[41]	Used ML to demonstrate fair-to-good ability in predicting 2-year postsurgical MCID following TJA
Greenstein et al[51]	Used EMR-integrated ANN to predict discharge disposition after TJA on small data set
Janssen et al[38]	Boosting ML algorithm far superior in training data sets to classic scoring system and nomogram in predicting survival in patients with long bone metastases at 30 days, 90 days, and 1 year
Karnuta et al[50]	Bayes ML algorithm demonstrated excellent accuracy in prediction of length of stay and cost of an episode of care for hip fracture
Menendez et al[44]	Used ML on patient-narrative analysis to show patient satisfaction after TSA is linked to hospital environment, nontechnical skills, and delays
Navarro et al[46]	Created a valid ML algorithm that predicted length of stay and costs before primary TKA
Pereira et al[55]	Boosting ML algorithm comparable to nomogram in its ability to predict survival in metastatic spine disease with testing data sets
Ramkumar et al[45]	Created a valid and reliable ML algorithm that predicted length of stay and payment prior to primary THA
Ramkumar et al[47]	Developed several ML based models for primary LEA that preoperatively predict cost, length of stay, and discharge disposition
Thio et al[39]	Created a high performing ML algorithm that could predict 5-year survival in patients with chondrosarcoma

ML: Machine learning; MCID: Minimally clinically important difference; TJA: Total joint arthroplasty; EMR: Electronic medical record; ANN: Artificial neural network; TSA: Total shoulder arthroplasty; TKA: Total knee arthroplasty; THA: Total hip arthroplasty; LEA: Lower extremity arthroplasty.