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自动化膝关节骨关节炎评估可提高医师的一致性和准确性:来自骨关节炎倡议的数据。

Automated Knee Osteoarthritis Assessment Increases Physicians' Agreement Rate and Accuracy: Data from the Osteoarthritis Initiative.

机构信息

Danube University Krems, Krems, Austria.

ImageBiopsy Lab, Vienna, Austria.

出版信息

Cartilage. 2021 Dec;13(1_suppl):957S-965S. doi: 10.1177/1947603519888793. Epub 2019 Nov 24.

Abstract

. To assess the impact of a computerized system on physicians' accuracy and agreement rate, as compared with unaided diagnosis. . A set of 124 unilateral knee radiographs from the Osteoarthritis Initiative (OAI) study were analyzed by a computerized method with regard to Kellgren-Lawrence (KL) grade, as well as joint space narrowing, osteophytes, and sclerosis Osteoarthritis Research Society International (OARSI) grades. Physicians scored all images, with regard to osteophytes, sclerosis, joint space narrowing OARSI grades and KL grade, in 2 modalities: through a plain radiograph () and a radiograph presented together with the report from the computer assisted detection system (). Intraclass correlation between the physicians was calculated for both modalities. Furthermore, physicians' performance was compared with the grading of the OAI study, and accuracy, sensitivity, and specificity were calculated in both modalities for each of the scored features. . Agreement rates for KL grade, sclerosis, and osteophyte OARSI grades, were statistically increased in the aided versus the unaided modality. Readings for joint space narrowing OARSI grade did not show a statistically difference between the 2 modalities. Readers' accuracy and specificity for KL grade >0, KL >1, sclerosis OARSI grade >0, and osteophyte OARSI grade >0 was significantly increased in the aided modality. Reader sensitivity was high in both modalities. . These results show that the use of an automated knee OA software increases consistency between physicians when grading radiographic features of OA. The use of the software also increased accuracy measures as compared with the OAI study, mostly through increases in specificity.

摘要

. 评估计算机系统对医生准确性和一致性的影响,与非辅助诊断相比。. 对来自骨关节炎倡议(OAI)研究的 124 张单侧膝关节 X 光片进行了计算机方法分析,涉及 Kellgren-Lawrence(KL)分级以及关节间隙狭窄、骨赘和硬化性骨关节炎研究协会国际(OARSI)分级。医生对所有图像进行了评分,涉及骨赘、硬化、关节间隙狭窄 OARSI 分级和 KL 分级,使用了两种模式:通过普通 X 光片()和与计算机辅助检测系统报告一起呈现的 X 光片()。计算了两种模式下医生之间的组内相关性。此外,还比较了医生的表现与 OAI 研究的分级,并计算了两种模式下每个评分特征的准确性、敏感性和特异性。. 在辅助模式下,KL 分级、硬化和骨赘 OARSI 分级的一致性在统计学上高于非辅助模式。OARSI 分级关节间隙狭窄的读数在两种模式之间没有统计学差异。在辅助模式下,读者对 KL 分级>0、KL>1、硬化 OARSI 分级>0 和骨赘 OARSI 分级>0 的准确性和特异性显著提高。两种模式下的读者敏感性都很高。. 这些结果表明,使用自动膝关节 OA 软件可提高医生在分级 OA 放射学特征时的一致性。与 OAI 研究相比,软件的使用还提高了准确性度量,主要是通过特异性的提高。

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