IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
Dipartimento Di Scienze Biomediche Per La Salute, Università Degli Studi Di Milano, Milan, Italy.
Radiol Med. 2024 Sep;129(9):1405-1411. doi: 10.1007/s11547-024-01856-1. Epub 2024 Jul 13.
To systematically review the use of artificial intelligence (AI) in musculoskeletal (MSK) ultrasound (US) with an emphasis on AI algorithm categories and validation strategies.
An electronic literature search was conducted for articles published up to January 2024. Inclusion criteria were the use of AI in MSK US, involvement of humans, English language, and ethics committee approval.
Out of 269 identified papers, 16 studies published between 2020 and 2023 were included. The research was aimed at predicting diagnosis and/or segmentation in a total of 11 (69%) out of 16 studies. A total of 11 (69%) studies used deep learning (DL)-based algorithms, three (19%) studies employed conventional machine learning (ML)-based algorithms, and two (12%) studies employed both conventional ML- and DL-based algorithms. Six (38%) studies used cross-validation techniques with K-fold cross-validation being the most frequently employed (n = 4, 25%). Clinical validation with separate internal test datasets was reported in nine (56%) papers. No external clinical validation was reported.
AI is a topic of increasing interest in MSK US research. In future studies, attention should be paid to the use of validation strategies, particularly regarding independent clinical validation performed on external datasets.
系统地回顾人工智能（AI）在肌肉骨骼（MSK）超声（US）中的应用，重点关注 AI 算法类别和验证策略。
对截至 2024 年 1 月发表的文章进行了电子文献检索。纳入标准为 AI 在 MSK-US 中的应用、涉及人类、英语语言和伦理委员会批准。
在 269 篇已确定的论文中，纳入了 2020 年至 2023 年期间发表的 16 项研究。这些研究旨在预测总共 11 项（69%）研究中的诊断和/或分割。共有 11 项（69%）研究使用基于深度学习（DL）的算法，3 项（19%）研究采用基于传统机器学习（ML）的算法，2 项（12%）研究采用基于传统 ML 和 DL 的算法。有 6 项（38%）研究使用了交叉验证技术，其中 K 折交叉验证最常用（n=4，25%）。9 项（56%）论文报告了临床验证，使用了单独的内部测试数据集。没有报告外部临床验证。
AI 是 MSK-US 研究中越来越受关注的课题。在未来的研究中，应注意验证策略的使用，特别是关于在外部数据集上进行独立临床验证的问题。
