Department of Radiology, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan.
Fujifilm Corporation, Nishiazabu 2-Chome, Minato-ku, Tokyo, 26-30, Japan.
Sci Rep. 2022 May 19;12(1):8363. doi: 10.1038/s41598-022-12453-5.
Artificial intelligence algorithms utilizing deep learning are helpful tools for diagnostic imaging. A deep learning-based automatic detection algorithm was developed for rib fractures on computed tomography (CT) images of high-energy trauma patients. In this study, the clinical effectiveness of this algorithm was evaluated. A total of 56 cases were retrospectively examined, including 46 rib fractures and 10 control cases from our hospital, between January and June 2019. Two radiologists annotated the fracture lesions (complete or incomplete) for each CT image, which is considered the "ground truth." Thereafter, the algorithm's diagnostic results for all cases were compared with the ground truth, and the sensitivity and number of false positive (FP) results per case were assessed. The radiologists identified 199 images with a fracture. The sensitivity of the algorithm was 89.8%, and the number of FPs per case was 2.5. After additional learning, the sensitivity increased to 93.5%, and the number of FPs was 1.9 per case. FP results were found in the trabecular bone with the appearance of fracture, vascular grooves, and artifacts. The sensitivity of the algorithm used in this study was sufficient to aid the rapid detection of rib fractures within the evaluated validation set of CT images.
利用深度学习的人工智能算法是诊断成像的有用工具。已经开发出一种基于深度学习的自动检测算法，用于高能创伤患者 CT 图像上的肋骨骨折。在这项研究中，评估了该算法的临床效果。回顾性检查了 2019 年 1 月至 6 月期间来自我院的 56 例病例，包括 46 例肋骨骨折和 10 例对照病例。两位放射科医生对每张 CT 图像上的骨折病变（完全或不完全）进行注释，这被认为是“真实情况”。然后，将算法对所有病例的诊断结果与真实情况进行比较，并评估每个病例的敏感性和假阳性（FP）结果数量。放射科医生共识别出 199 张有骨折的图像。该算法的灵敏度为 89.8%，每个病例的 FP 数量为 2.5。经过进一步学习，灵敏度提高到 93.5%，每个病例的 FP 数量为 1.9。FP 结果出现在外观上有骨折的小梁骨、血管沟和伪影中。在评估的 CT 图像验证集中，本研究中使用的算法的灵敏度足以辅助快速检测肋骨骨折。
