Department of Orthopaedic Surgery, Columbia University Irving Medical Center, New York, New York.
Department of Orthopaedic Surgery, Mayo Clinic, Rochester, Minnesota.
JBJS Rev. 2024 Aug 22;12(8). doi: e24.00087. eCollection 2024 Aug 1.
Hip arthroscopy has seen a significant surge in utilization, but complications remain, and optimal functional outcomes are not guaranteed. Artificial intelligence (AI) has emerged as an effective supportive decision-making tool for surgeons. The purpose of this systematic review was to characterize the outcomes, performance, and validity (generalizability) of AI-based prediction models for hip arthroscopy in current literature.
Two reviewers independently completed structured searches using PubMed/MEDLINE and Embase databases on August 10, 2022. The search query used the terms as follows: (artificial intelligence OR machine learning OR deep learning) AND (hip arthroscopy). Studies that investigated AI-based risk prediction models in hip arthroscopy were included. The primary outcomes of interest were the variable(s) predicted by the models, best model performance achieved (primarily based on area under the curve, but also accuracy, etc), and whether the model(s) had been externally validated (generalizable).
Seventy-seven studies were identified from the primary search. Thirteen studies were included in the final analysis. Six studies (n = 6,568) applied AI for predicting the achievement of minimal clinically important difference for various patient-reported outcome measures such as the visual analog scale and the International Hip Outcome Tool 12-Item Questionnaire, with area under a receiver-operating characteristic curve (AUC) values ranging from 0.572 to 0.94. Three studies used AI for predicting repeat hip surgery with AUC values between 0.67 and 0.848. Four studies focused on predicting other risks, such as prolonged postoperative opioid use, with AUC values ranging from 0.71 to 0.76. None of the 13 studies assessed the generalizability of their models through external validation.
AI is being investigated for predicting clinical outcomes after hip arthroscopy. However, the performance of AI models varies widely, with AUC values ranging from 0.572 to 0.94. Critically, none of the models have undergone external validation, limiting their clinical applicability. Further research is needed to improve model performance and ensure generalizability before these tools can be reliably integrated into patient care.
Level IV. See Instructions for Authors for a complete description of levels of evidence.
髋关节镜检查的应用显著增加，但仍存在并发症，且无法保证最佳的功能结果。人工智能（AI）已成为外科医生进行有效辅助决策的工具。本系统评价的目的是描述当前文献中基于 AI 的髋关节镜检查预测模型的结果、性能和有效性（可推广性）。
两位审查员于 2022 年 8 月 10 日独立使用 PubMed/MEDLINE 和 Embase 数据库完成了结构化检索。检索查询使用了以下术语：（人工智能或机器学习或深度学习）和（髋关节镜检查）。纳入研究为 AI 为髋关节镜检查中的风险预测模型。主要研究结果为模型预测的变量、模型达到的最佳性能（主要基于曲线下面积，也包括准确性等）以及模型是否经过外部验证（可推广）。
从初步检索中确定了 77 项研究。最终有 13 项研究被纳入分析。6 项研究（n=6568）应用 AI 预测了各种患者报告的结果测量（如视觉模拟量表和国际髋关节结果工具 12 项问卷）的最小临床重要差异的实现情况，其接受者操作特征曲线（AUC）值范围为 0.572 至 0.94。3 项研究使用 AI 预测髋关节翻修手术，AUC 值在 0.67 至 0.848 之间。4 项研究关注预测其他风险，如术后阿片类药物使用时间延长，AUC 值在 0.71 至 0.76 之间。13 项研究均未通过外部验证评估其模型的可推广性。
AI 正被用于预测髋关节镜检查后的临床结果。然而，AI 模型的性能差异很大，AUC 值范围为 0.572 至 0.94。重要的是，没有一个模型经过外部验证，限制了其临床适用性。在这些工具能够可靠地整合到患者护理中之前，需要进一步研究以提高模型性能并确保可推广性。
四级。请参阅作者说明，以获取完整的证据水平描述。
