Diagnostic evaluation of a deep learning model for optical diagnosis of colorectal cancer
Diagnostic evaluation of a deep learning model for optical diagnosis of colorectal cancer
复制标题
结直肠癌光学诊断深度学习模型的诊断评价
DOI：
10.1038/s41467-020-16777-6
复制
发表时间：
2020-06-11
影响因子：
16.6
通讯作者：
Li, Xiangchun
中科院分区：
文献类型：
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作者：
Zhou, Dejun;Tian, Fei;Li, Xiangchun
结肠镜检查通常用于筛查结直肠癌（CRC）。我们开发了一个名为CRCNet的深度学习模型，用于CRC的光学诊断，通过对来自12，179名患者的464，105张图像进行训练，并在来自三个独立数据集的2263名患者上测试其性能。在患者水平，CRCNet的精确-召回曲线下面积（AUPRC）分别为0.882（95% CI：0.828-0.931）、0.874（0.820-0.926）和0.867（0.795-0.923）。CRCNet在两个测试集的召回率方面超过了内镜医师的平均表现（91.3% vs 83.8%;双侧t检验，p
Colonoscopy is commonly used to screen for colorectal cancer (CRC). We develop a deep learning model called CRCNet for optical diagnosis of CRC by training on 464,105 images from 12,179 patients and test its performance on 2263 patients from three independent datasets. At the patient-level, CRCNet achieves an area under the precision-recall curve (AUPRC) of 0.882 (95% CI: 0.828-0.931), 0.874 (0.820-0.926) and 0.867 (0.795-0.923). CRCNet exceeds average endoscopists performance on recall rate across two test sets (91.3% versus 83.8%; two-sided t-test, p