Department of Orthopedic Surgery, Sports & Orthopedics Research Centre, St. Anna Hospital, Geldrop, The Netherlands.
InteractiveStudios, Den Bosch, The Netherlands.
J Arthroplasty. 2024 May;39(5):1173-1177.e6. doi: 10.1016/j.arth.2023.11.022. Epub 2023 Nov 24.
Increasing numbers of patients suffering from hip osteoartritis will lead to increased orthopaedic health care consumption. Artificial intelligence might alleviate this problem, using Machine learning (ML) to optimize orthopaedic consultation workflow by predicting treatment strategy (non-operative or operative) prior to consultation. The purpose of this study was to assess ML accuracy in clinical practice, by comparing ML predictions to the outcome of clinical consultations.
In this prospective clinical cohort study, adult patients referred for hip complaints between January 20th to February 20th 2023 were included. Patients completed a computer-assisted history taking (CAHT) form and using these CAHT answers, a ML-algorithm predicted non-operative or operative treatment outcome prior to in-hospital consultation. During consultation, orthopaedic surgeons and physician assistants were blinded to the prediction in 90 and unblinded in 29 cases. Consultation outcome (non-operative or operative) was compared to ML treatment prediction for all cases, and for blinded and unblinded conditions separately. Analysis was done on 119 consultations.
Overall treatment strategy prediction was correct in 101 cases (accuracy 85%, P < .0001). Non-operative treatment prediction (n = 71) was 97% correct versus 67% for operative treatment prediction (n = 48). Results from unblinded consultations (86.2% correct predictions,) were not statistically different from blinded consultations (84.4% correct, P > .05).
Machine Learning algorithms can predict non-operative or operative treatment for patients with hip complaints with high accuracy. This could facilitate scheduling of non-operative patients with physician assistants, and operative patients with orthopaedic surgeons including direct access to pre-operative screening, thereby optimizing usage of health care resources.
越来越多的髋骨关节炎患者将导致骨科医疗保健消费的增加。人工智能可能通过在咨询前预测治疗策略（非手术或手术）来优化骨科咨询工作流程，从而缓解这一问题。本研究旨在通过将机器学习（ML）预测与临床咨询结果进行比较，评估其在临床实践中的准确性。
这是一项前瞻性临床队列研究，纳入了 2023 年 1 月 20 日至 2 月 20 日期间因髋部问题就诊的成年患者。患者完成了计算机辅助病史采集（CAHT）表格，使用这些 CAHT 答案，ML 算法在住院咨询前预测非手术或手术治疗结果。在咨询过程中，骨科医生和医师助理在 90 例中对预测结果设盲，在 29 例中不设盲。比较所有病例、设盲和不设盲条件下的咨询结果（非手术或手术）与 ML 治疗预测结果。共分析了 119 次咨询。
总体治疗策略预测正确的有 101 例（准确率 85%，P<0.0001）。非手术治疗预测（n=71）的准确率为 97%，而手术治疗预测（n=48）的准确率为 67%。不设盲的咨询结果（正确预测率为 86.2%）与设盲的咨询结果（正确预测率为 84.4%）没有统计学差异（P>0.05）。
机器学习算法可以高度准确地预测髋部疼痛患者的非手术或手术治疗。这可以方便地安排医师助理治疗非手术患者，以及骨科医生治疗手术患者，包括直接进行术前筛查，从而优化医疗资源的使用。
