Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou, 310009, Zhejiang, China.
Hithink RoyalFlush Information Network Co., Ltd, No. 18 Tongshun Street, Yuhang District, Hangzhou, 310012, Zhejiang, China.
Sci Rep. 2021 Dec 6;11(1):23513. doi: 10.1038/s41598-021-03002-7.
Rib fracture detection is time-consuming and demanding work for radiologists. This study aimed to introduce a novel rib fracture detection system based on deep learning which can help radiologists to diagnose rib fractures in chest computer tomography (CT) images conveniently and accurately. A total of 1707 patients were included in this study from a single center. We developed a novel rib fracture detection system on chest CT using a three-step algorithm. According to the examination time, 1507, 100 and 100 patients were allocated to the training set, the validation set and the testing set, respectively. Free Response ROC analysis was performed to evaluate the sensitivity and false positivity of the deep learning algorithm. Precision, recall, F1-score, negative predictive value (NPV) and detection and diagnosis were selected as evaluation metrics to compare the diagnostic efficiency of this system with radiologists. The radiologist-only study was used as a benchmark and the radiologist-model collaboration study was evaluated to assess the model's clinical applicability. A total of 50,170,399 blocks (fracture blocks, 91,574; normal blocks, 50,078,825) were labelled for training. The F1-score of the Rib Fracture Detection System was 0.890 and the precision, recall and NPV values were 0.869, 0.913 and 0.969, respectively. By interacting with this detection system, the F1-score of the junior and the experienced radiologists had improved from 0.796 to 0.925 and 0.889 to 0.970, respectively; the recall scores had increased from 0.693 to 0.920 and 0.853 to 0.972, respectively. On average, the diagnosis time of radiologist assisted with this detection system was reduced by 65.3 s. The constructed Rib Fracture Detection System has a comparable performance with the experienced radiologist and is readily available to automatically detect rib fracture in the clinical setting with high efficacy, which could reduce diagnosis time and radiologists' workload in the clinical practice.
肋骨骨折检测对放射科医生来说是一项耗时且要求很高的工作。本研究旨在引入一种基于深度学习的新型肋骨骨折检测系统，该系统可以帮助放射科医生方便、准确地诊断胸部计算机断层扫描（CT）图像中的肋骨骨折。本研究共纳入了来自单一中心的 1707 名患者。我们开发了一种基于胸部 CT 的新型肋骨骨折检测系统，采用三步算法。根据检查时间，将 1507 例、100 例和 100 例患者分别分配到训练集、验证集和测试集中。采用自由响应 ROC 分析评估深度学习算法的灵敏度和假阳性率。选择精度、召回率、F1 评分、阴性预测值（NPV）和检测诊断作为评价指标，比较该系统与放射科医生的诊断效率。仅放射科医生研究作为基准，评估放射科医生-模型合作研究以评估模型的临床适用性。共标记了 50170399 个块（骨折块 91574 个，正常块 50078825 个）用于训练。肋骨骨折检测系统的 F1 得分为 0.890，精度、召回率和 NPV 值分别为 0.869、0.913 和 0.969。通过与该检测系统交互，初级和经验丰富的放射科医生的 F1 评分从 0.796 提高到 0.925 和 0.889 提高到 0.970；召回率从 0.693 提高到 0.920 和 0.853 提高到 0.972。平均而言，使用该检测系统辅助诊断的放射科医生的诊断时间减少了 65.3 秒。构建的肋骨骨折检测系统与经验丰富的放射科医生具有相当的性能，并且可以随时在临床环境中自动检测肋骨骨折，具有高效性，可以减少诊断时间和放射科医生的工作量。
