基于深度学习的新型人工智能计算机辅助诊断（AI-CAD）系统有望支持筛查阅读。为了测试深度学习人工智能计算机辅助诊断系统与人类阅读在连续乳腺X线照片筛查中的比较效果，本回顾性研究采用17,884个连续非实名的筛查乳腺X线照片数据，这些照片双重阅读于2018年1月至11月期间，并通过深度学习人工智能计算机辅助诊断系统进行处理。AI-CAD阅读被认为是阳性，如果AI-CAD病例分数超过30（范围为1-100），且恶性病变被正确标记。同样，被正确识别和呼叫恶性病变，则人类阅读（R1或R2）被认为是阳性。进行了受试者工作特性（ROC）分析，并计算了准确性数据。良性病变的标准为：经过癌症登记数据库匹配后无恶性病变（2022年）；恶性病变的标准为：组织病理学证明；评估是以患者为基础进行的。 总共发现了114个通过筛查发现的乳腺癌和17个间隔期乳腺癌病例。筛查发现的乳腺癌的ROC分析结果显示，AI-CAD的曲线下面积为89%。AI-CAD的敏感性/特异性为81.7%/80.2%，R1的敏感性/特异性为77.1%/91.7%，R2的敏感性/特异性为78.6%/91.6%。将每个人类阅读与AI-CAD相结合的敏感性与人类双重阅读（均约为88%）相当，但特异性较低（约为75%），与人类双重阅读（约为87%）相比。这些AI-CAD组合需要对两倍数量的病例进行共识阅读，而人类组合则不需要。在17个间隔期乳腺癌病例中，有4个超过了病例分数30；其中有两个CAD正确标记了后续乳腺癌的象限。 包括间隔期乳腺癌病例，该AI-CAD在较低特异性下实现了与人类阅读相当的敏感性。将人类阅读与AI-CAD相结合可以提高敏感性，相比单独阅读。

Novel artificial intelligence computer-assisted detection (AI-CAD) systems based on deep learning (DL) promise to support screen reading.To test a DL-AI-CAD system compared to human reading on consecutive screening mammograms.In this retrospective study, 17,884 consecutive anonymized screening mammograms, double-read from January to November 2018, were processed by the DL-AI-CAD system. AI-CAD reading was considered positive if the AI-CAD case scores exceeded 30 (range = 1-100) and the lesion was correctly marked. Likewise, human reading (R1 or R2, respectively) was considered positive if the lesion was correctly identified and called. Receiver operating characteristic (ROC) analysis was performed and accuracy data were calculated. Ground truth for benign lesions: absence of malignancy after cancer registry matching (2022); for malignancy: histopathologic proof; evaluation was patient-based.In total, 114 screen-detected and 17 interval cancers (ICA) occurred. ROC analysis of screen-detected cancers yielded an AUC of 89% for AI-CAD. Sensitivity/specificity was 81.7%/80.2% for AI-CAD; 77.1%/91.7% for R1; 78.6/91.6% for R2. Combining each human reading with AI-CAD was as sensitive as human double-reading (all approximately 88%), but less specific (approximately 75%) compared to human double-reading (approximately 87%). These AI-CAD combinations required consensus readings for twice as many cases as the human combination. Four of 17 ICA exceeded a case score of 30; two of four CAD correctly marked the quadrant of the subsequent ICA.Including ICA cases, this AI-CAD achieved comparable sensitivity to human reading at lower specificity. Combining human reading and AI-CAD allows increasing sensitivity compared to single-reading.