使用人工智能对骨质疏松症进行胸部 X 光和 CT 诊断的准确性:系统回顾和荟萃分析。

IF 2.4 3区 医学 Q3 ENDOCRINOLOGY & METABOLISM
Journal of Bone and Mineral Metabolism Pub Date : 2024-09-01 Epub Date: 2024-08-21 DOI:10.1007/s00774-024-01532-4
Norio Yamamoto, Akihiro Shiroshita, Ryota Kimura, Tomohiko Kamo, Hirofumi Ogihara, Takahiro Tsuge
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引用次数: 0

摘要

导言基于人工智能(AI)的系统使用胸部图像诊断骨质疏松症具有潜在的可靠性:我们进行了一项系统性回顾和荟萃分析,以评估根据诊断测试准确性指南使用人工智能对胸部 X 光和计算机断层扫描(CT)扫描进行骨质疏松症诊断的准确性。我们纳入了调查骨质疏松症指标检测诊断准确性的任何类型的研究。我们于 2023 年 11 月 8 日检索了 MEDLINE、EMBASE、Cochrane Central Register of Controlled Trials 和 IEEE Xplore Digital Library。主要结果指标是骨质疏松症和骨质疏松症的敏感性、特异性和接收者工作特征曲线下面积(AUC)。我们描述了灵敏度、特异性和 AUC 的森林图。总结点由双变量随机效应模型估算得出。我们采用推荐、评估、发展和评价分级法总结了证据的总体质量:本综述共纳入了九项研究,共有 11,369 名参与者。胸部 X 射线诊断骨质疏松症的汇总灵敏度、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,低)。胸部 CT 诊断骨质疏松症和骨质疏松症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,低和极低):本综述表明,胸部 X 光与 AI 对骨质疏松症的诊断具有很高的灵敏度,突出了其作为机会性筛查的潜力。然而,大多数研究中患者选择的偏倚风险较高。未来需要进行更多的研究,为筛查工具制定适当的参与者选择标准。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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

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.

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来源期刊
Journal of Bone and Mineral Metabolism
Journal of Bone and Mineral Metabolism 医学-内分泌学与代谢
CiteScore
6.30
自引率
3.00%
发文量
89
审稿时长
6-12 weeks
期刊介绍: The Journal of Bone and Mineral Metabolism (JBMM) provides an international forum for researchers and clinicians to present and discuss topics relevant to bone, teeth, and mineral metabolism, as well as joint and musculoskeletal disorders. The journal welcomes the submission of manuscripts from any country. Membership in the society is not a prerequisite for submission. Acceptance is based on the originality, significance, and validity of the material presented. The journal is aimed at researchers and clinicians dedicated to improvements in research, development, and patient-care in the fields of bone and mineral metabolism.
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