IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089, Rozzano, Milan, Italy.
Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20072, Pieve Emanuele, Milan, Italy.
Osteoporos Int. 2024 Oct;35(10):1681-1692. doi: 10.1007/s00198-024-07179-1. Epub 2024 Jul 10.
This scoping review aimed to assess the current research on artificial intelligence (AI)--enhanced opportunistic screening approaches for stratifying osteoporosis and osteopenia risk by evaluating vertebral trabecular bone structure in CT scans.
PubMed, Scopus, and Web of Science databases were systematically searched for studies published between 2018 and December 2023. Inclusion criteria encompassed articles focusing on AI techniques for classifying osteoporosis/osteopenia or determining bone mineral density using CT scans of vertebral bodies. Data extraction included study characteristics, methodologies, and key findings.
Fourteen studies met the inclusion criteria. Three main approaches were identified: fully automated deep learning solutions, hybrid approaches combining deep learning and conventional machine learning, and non-automated solutions using manual segmentation followed by AI analysis. Studies demonstrated high accuracy in bone mineral density prediction (86-96%) and classification of normal versus osteoporotic subjects (AUC 0.927-0.984). However, significant heterogeneity was observed in methodologies, workflows, and ground truth selection.
The review highlights AI's promising potential in enhancing opportunistic screening for osteoporosis using CT scans. While the field is still in its early stages, with most solutions at the proof-of-concept phase, the evidence supports increased efforts to incorporate AI into radiologic workflows. Addressing knowledge gaps, such as standardizing benchmarks and increasing external validation, will be crucial for advancing the clinical application of these AI-enhanced screening methods. Integration of such technologies could lead to improved early detection of osteoporotic conditions at a low economic cost.
本范围综述旨在评估当前人工智能（AI）增强的机会性筛选方法，通过评估 CT 扫描中椎骨小梁骨结构来分层骨质疏松症和低骨量风险。
系统检索了 2018 年至 2023 年 12 月期间发表的来自 PubMed、Scopus 和 Web of Science 数据库的研究。纳入标准包括专注于 AI 技术的文章，这些技术用于通过 CT 扫描椎体对骨质疏松症/低骨量进行分类或确定骨矿物质密度。数据提取包括研究特征、方法和主要发现。
符合纳入标准的研究有 14 项。确定了三种主要方法：完全自动化的深度学习解决方案、将深度学习和传统机器学习相结合的混合方法，以及使用手动分割后进行 AI 分析的非自动化方法。研究表明，骨矿物质密度预测（86-96%）和正常与骨质疏松受试者分类（AUC 0.927-0.984）的准确性很高。然而，在方法学、工作流程和真实值选择方面存在显著的异质性。
综述强调了 AI 在使用 CT 扫描增强骨质疏松症机会性筛查方面的有前景的潜力。虽然该领域仍处于早期阶段，大多数解决方案处于概念验证阶段，但证据支持加大努力将 AI 纳入放射学工作流程。解决知识差距，例如标准化基准和增加外部验证，对于推进这些 AI 增强的筛查方法的临床应用至关重要。此类技术的集成可能会导致以较低的经济成本实现骨质疏松症的早期检测。
