论文标题
准确且可解释的深度学习系统改善了观察者的一致性在胸部X光片解释中
An Accurate and Explainable Deep Learning System Improves Interobserver Agreement in the Interpretation of Chest Radiograph
论文作者
论文摘要
最近的人工智能（AI）算法已经在各种医学分类任务上实现了放射科医生级的性能。但是，只有少数研究解决了CXR扫描异常发现的定位，这对于向放射学家解释图像级分类至关重要。我们在本文中介绍了一个称为Vindr-CXR的可解释的深度学习系统，该系统可以将CXR扫描分类为多种胸部疾病，同时将大多数类型的关键发现本地定位在图像上。 Vindr-CXR接受了51,485 CXR扫描的培训，并通过放射科医生提供的边界盒注释进行了培训。它表现出与经验丰富的放射科医生相当的性能，可以在3,000 CXR扫描的回顾性验证集上对6种常见的胸部疾病进行分类，而在接收器操作特征曲线（AUROC）下的平均面积为0.967（95％置信区间[CI]：0.958-0.975）。 VINDR-CXR在独立患者队列中也得到了外部验证，并显示出其稳健性。对于具有14种类型病变的本地化任务，我们的自由响应接收器操作特征（FROC）分析表明，VINDR-CXR以每扫描确定的1.0假阳性病变的速率达到80.2％的灵敏度。还进行了一项前瞻性研究，以衡量VINDR-CXR在协助六位经验丰富的放射科医生方面的临床影响。结果表明，当用作诊断工具时，提出的系统显着改善了放射科医生本身之间的一致性，平均Fleiss的Kappa的同意增加了1.5％。我们还观察到，在放射科医生咨询了Vindr-CXR的建议之后，在平均Cohen的Kappa中，它们与系统之间的一致性显着增加了3.3％。
Recent artificial intelligence (AI) algorithms have achieved radiologist-level performance on various medical classification tasks. However, only a few studies addressed the localization of abnormal findings from CXR scans, which is essential in explaining the image-level classification to radiologists. We introduce in this paper an explainable deep learning system called VinDr-CXR that can classify a CXR scan into multiple thoracic diseases and, at the same time, localize most types of critical findings on the image. VinDr-CXR was trained on 51,485 CXR scans with radiologist-provided bounding box annotations. It demonstrated a comparable performance to experienced radiologists in classifying 6 common thoracic diseases on a retrospective validation set of 3,000 CXR scans, with a mean area under the receiver operating characteristic curve (AUROC) of 0.967 (95% confidence interval [CI]: 0.958-0.975). The VinDr-CXR was also externally validated in independent patient cohorts and showed its robustness. For the localization task with 14 types of lesions, our free-response receiver operating characteristic (FROC) analysis showed that the VinDr-CXR achieved a sensitivity of 80.2% at the rate of 1.0 false-positive lesion identified per scan. A prospective study was also conducted to measure the clinical impact of the VinDr-CXR in assisting six experienced radiologists. The results indicated that the proposed system, when used as a diagnosis supporting tool, significantly improved the agreement between radiologists themselves with an increase of 1.5% in mean Fleiss' Kappa. We also observed that, after the radiologists consulted VinDr-CXR's suggestions, the agreement between each of them and the system was remarkably increased by 3.3% in mean Cohen's Kappa.
