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人工智能在骨质疏松症中的胸部 X 射线和 CT 诊断准确性:系统评价和荟萃分析。

Diagnostic accuracy of chest X-ray and CT using artificial intelligence for osteoporosis: systematic review and meta-analysis.

机构信息

Department of Epidemiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan.

Scientific Research WorkS Peer Support Group (SRWS-PSG), Osaka, Japan.

出版信息

J Bone Miner Metab. 2024 Sep;42(5):483-491. doi: 10.1007/s00774-024-01532-4. Epub 2024 Aug 21.

Abstract

INTRODUCTION

Artificial intelligence (AI)-based systems using chest images are potentially reliable for diagnosing osteoporosis.

METHODS

We performed a systematic review and meta-analysis to assess the diagnostic accuracy of chest X-ray and computed tomography (CT) scans using AI for osteoporosis in accordance with the diagnostic test accuracy guidelines. We included any type of study investigating the diagnostic accuracy of index test for osteoporosis. We searched MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials, and IEEE Xplore Digital Library on November 8, 2023. The main outcome measures were the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) for osteoporosis and osteopenia. We described forest plots for sensitivity, specificity, and AUC. The summary points were estimated from the bivariate random-effects models. We summarized the overall quality of evidence using the Grades of Recommendation, Assessment, Development, and Evaluation approach.

RESULTS

Nine studies with 11,369 participants were included in this review. The pooled sensitivity, specificity, and AUC of chest X-rays for the diagnosis of osteoporosis were 0.83 (95% confidence interval [CI] 0.75, 0.89), 0.76 (95% CI 0.71, 0.80), and 0.86 (95% CI 0.83, 0.89), respectively (certainty of the evidence, low). The pooled sensitivity and specificity of chest CT for the diagnosis of osteoporosis and osteopenia were 0.83 (95% CI 0.69, 0.92) and 0.70 (95% CI 0.61, 0.77), respectively (certainty of the evidence, low and very low).

CONCLUSIONS

This review suggests that chest X-ray with AI has a high sensitivity for the diagnosis of osteoporosis, highlighting its potential for opportunistic screening. However, the risk of bias of patient selection in most studies were high. More research with adequate participants' selection criteria for screening tool will be needed in the future.

摘要

简介

基于人工智能(AI)的胸部影像系统在诊断骨质疏松症方面具有较高的可靠性。

方法

我们按照诊断性测试准确性指南进行了系统综述和荟萃分析,以评估 AI 用于胸部 X 线和计算机断层扫描(CT)扫描诊断骨质疏松症的诊断准确性。我们纳入了任何类型的研究,这些研究调查了针对骨质疏松症的索引测试的诊断准确性。我们于 2023 年 11 月 8 日在 MEDLINE、EMBASE、Cochrane 对照试验中心注册库和 IEEE Xplore 数字图书馆进行了检索。主要结局指标为骨质疏松症和骨量减少的敏感性、特异性和接受者操作特征曲线(ROC)下面积(AUC)。我们描述了森林图,用于显示敏感性、特异性和 AUC。汇总点是从双变量随机效应模型中估计的。我们使用推荐、评估、制定和评价方法(Grades of Recommendation, Assessment, Development, and Evaluation,GRADE)评估证据的总体质量。

结果

本综述共纳入了 9 项研究,涉及 11369 名参与者。胸部 X 射线诊断骨质疏松症的汇总敏感性、特异性和 AUC 分别为 0.83(95%置信区间 [CI] 0.75,0.89)、0.76(95% CI 0.71,0.80)和 0.86(95% CI 0.83,0.89)(证据确定性为低)。胸部 CT 诊断骨质疏松症和骨量减少的汇总敏感性和特异性分别为 0.83(95% CI 0.69,0.92)和 0.70(95% CI 0.61,0.77)(证据确定性为低和极低)。

结论

本综述表明,AI 辅助的胸部 X 射线检查对骨质疏松症的诊断具有较高的敏感性,这突出了其在机会性筛查中的应用潜力。然而,大多数研究中患者选择的偏倚风险较高。未来需要进行更多研究,以确定更适合筛查工具的适当参与者选择标准。

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