目的:青少年特发性脊柱侧凸(AIS)是一种常见的脊柱畸形,具有不同的进展,复杂的治疗决定。人工智能(AI)和机器学习(ML)在骨科护理中日益突出,协助诊断,风险分层,和治疗指导。此范围审查概述了AIS中的AI应用。
方法:本研究遵循PRISMA-ScR指南,包括报道进展的文章,使用,或验证用于治疗的AI模型,诊断,或预测AIS的临床结果。
结果:包括40篇全文文章,大多数研究发表在过去5年(77.5%)。常见的机器学习技术是卷积神经网络(55%),决策树和随机森林(15%),和人工神经网络(15%)。AIS中的大多数AI应用是用于成像分析(25/40;62.5%),专注于Cobb角的自动测量,和轴向椎体旋转(13/25;52%)和弯曲分类/严重程度(13/25;52%)。预测是第二个最常见的应用(15/40;37.5%),预测曲线进展的研究(9/15;60%),和Cobb角(9/15;60%)。只有15项研究(37.5%)报告了AIS管理中AI的临床实施指南。52.5%的研究报告了模型的准确性,平均为85.4%。
结论:这篇综述强调了人工智能在AIS护理中的应用,特别是包括自动射线照相分析,曲线类型分类,曲线进展的预测,和AIS诊断。然而,目前缺乏明确的临床实施指南,模型透明度,研究模型的外部验证限制了临床医生的信任以及AI在AIS管理中的普适性和适用性。
OBJECTIVE: Adolescent idiopathic scoliosis (AIS) is a common spinal deformity with varying progression, complicating treatment decisions. Artificial intelligence (AI) and machine learning (ML) are increasingly prominent in orthopedic care, aiding in diagnosis, risk-stratification, and treatment guidance. This scoping review outlines AI applications in AIS.
METHODS: This study followed PRISMA-ScR guidelines and included articles that reported the development, use, or validation of AI models for treating, diagnosing, or predicting clinical outcomes in AIS.
RESULTS: 40 full-text articles were included, with most studies published in the last 5 years (77.5%). Common ML techniques were convolutional neural networks (55%), decision trees and random forests (15%), and artificial neural networks (15%). Most AI applications in AIS were for imaging analysis (25/40; 62.5%), focusing on automatic measurement of Cobb angle, and axial vertebral rotation (13/25; 52%) and curve classification/severity (13/25; 52%). Prediction was the second most common application (15/40; 37.5%), with studies predicting curve progression (9/15; 60%), and Cobb angles (9/15; 60%). Only 15 studies (37.5%) reported clinical implementation guidelines for AI in AIS management. 52.5% of studies reported model accuracy, with an average of 85.4%.
CONCLUSIONS: This review highlights the applications of AI in AIS care, notably including automatic radiographic analysis, curve type classification, prediction of curve progression, and AIS diagnosis. However, the current lack of clear clinical implementation guidelines, model transparency, and external validation of studied models limits clinician trust and the generalizability and applicability of AI in AIS management.