论文标题
胸部X射线中用于肺结核检测的基于AI的软件 - 第二读者方法的时间？
AI-based software for lung nodule detection in chest X-rays -- Time for a second reader approach?
论文作者
论文摘要
目的：将人工智能（AI）作为第二读者比较胸部X射线（CXR）与两个双层机构的放射科医生的第二读者，并在使用两种不同模式时评估AI的性能：自动化辅助相比（附加远程放射科医生审查）。 方法：分析了日本放射科学学会的CXR公共数据库（n = 247），具有各种类型和大小的肺结节。八位放射科医生评估了CXR图像，就肺结节和结节象征的存在而言。在放射科医生审查之后，AI软件以最高的遗漏结节的可能性处理并标记了CXR。计算出的精度指标是曲线下的面积（AUC），灵敏度，特异性，F1分数，假阴性病例数（FN）以及不同AI模式（自动/辅助）对结节检测准确性的影响。 结果：对于放射科医生而言，平均AUC值为0.77 $ \ pm $ 0.07，而平均FN为52.63 $ \ pm $ 17.53（所有研究）和32 $ \ pm $ $ \ pm $ 11.59（研究包含一个恶性病因的结节= 32％的恶性肿瘤率）。自动化和辅助的AI模式均可平均提高灵敏度（提高14％和12％），F1得分（5％和6％）和特异性降低（分别为10％和3％）。 结论：两种AI模式都标志着放射科医生在大量病例中错过的肺结核。 AI作为第二读者具有提高诊断准确性和放射学工作流程的高潜力。 AI可能比放射科医生早期检测到某些肺结核，对患者的预后有潜在的显着影响。
Objectives: To compare artificial intelligence (AI) as a second reader in detecting lung nodules on chest X-rays (CXR) versus radiologists of two binational institutions, and to evaluate AI performance when using two different modes: automated versus assisted (additional remote radiologist review). Methods: The CXR public database (n = 247) of the Japanese Society of Radiological Technology with various types and sizes of lung nodules was analyzed. Eight radiologists evaluated the CXR images with regard to the presence of lung nodules and nodule conspicuity. After radiologist review, the AI software processed and flagged the CXR with the highest probability of missed nodules. The calculated accuracy metrics were the area under the curve (AUC), sensitivity, specificity, F1 score, false negative case number (FN), and the effect of different AI modes (automated/assisted) on the accuracy of nodule detection. Results: For radiologists, the average AUC value was 0.77 $\pm$ 0.07, while the average FN was 52.63 $\pm$ 17.53 (all studies) and 32 $\pm$ 11.59 (studies containing a nodule of malignant etiology = 32% rate of missed malignant nodules). Both AI modes -- automated and assisted -- produced an average increase in sensitivity (by 14% and 12%) and of F1-score (5% and 6%) and a decrease in specificity (by 10% and 3%, respectively). Conclusions: Both AI modes flagged the pulmonary nodules missed by radiologists in a significant number of cases. AI as a second reader has a high potential to improve diagnostic accuracy and radiology workflow. AI might detect certain pulmonary nodules earlier than radiologists, with a potentially significant impact on patient outcomes.
