PURPOSE To evaluate the impact of different supervision regimens on the training of artificial intelligence (AI) in the classification of chest radiographs as normal or abnormal in a moderately sized cohort of individuals more likely to be outpatients. MATERIALS AND METHODS In a retrospective study, 7000 consecutive two-view chest radiographs obtained from 2012 to 2015 were labeled as normal or abnormal based on clinical reports. A convolutional neural network (CNN) was trained on this dataset and then evaluated with an unseen subset of 500 radiographs. Five different training approaches were tested: (1) weak supervision and four hybrid approaches combining weak supervision and extra supervision with annotation in (2) an unbalanced set of normal and abnormal cases, (3) a set of only abnormal cases, (4) a set of only normal cases, and (5) a balanced set of normal and abnormal cases. Standard binary classification metrics were assessed. RESULTS The weakly supervised model achieved an accuracy of 82%, but yielded 75 false negative cases, at a sensitivity of 70.0% and a negative predictive value (NPV) of 75.5%. Extra supervision increased NPV at the expense of the false positive rate and overall accuracy. Extra supervision with training using a balance of abnormal and normal radiographs resulted in the greatest increase in NPV (87.2%), improved sensitivity (92.8%), and reduced the number of false negatives by more than fourfold (18 compared to 75 cases). CONCLUSION Extra supervision using a balance of annotated normal and abnormal cases applied to a weakly supervised model can minimize the number of false negative cases when classifying two-view chest radiographs. Further refinement of such hybrid training approaches for AI is warranted to refine models for practical clinical applications.

中文翻译：

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混合监督方法对胸部X光片分类的人工智能性能的影响。

目的评估在中等规模人群中更可能成为门诊患者的胸部X光片分类为正常或异常中，不同监督方案对人工智能（AI）训练的影响。材料与方法在一项回顾性研究中，根据临床报告，将2012年至2015年连续获得的7000张两视图胸部X光片标记为正常或异常。在该数据集上训练了卷积神经网络（CNN），然后用500幅X射线照片的未见子集进行了评估。测试了五种不同的培训方法：（1）弱监督和四种将弱监督和额外监督与注释结合在一起的混合方法，其中（2）一组不平衡的正常和异常案例，（3）一组仅异常案例，（4）一组仅有的正常情况，（5）平衡的正常和异常情况。评估标准二进制分类指标。结果弱监督模型的准确率达到82％，但产生了75例假阴性病例，敏感性为70.0％，阴性预测值（NPV）为75.5％。额外的监督会增加NPV，但会降低误报率和整体准确性。通过使用异常和正常的X射线照片进行训练的额外监督可以使NPV增幅最大（87.2％），灵敏度提高（92.8％），并将假阴性数减少四倍以上（与75例相比减少了18例）。结论在对弱视监护模型进行分类时，使用带注释的正常病例和异常病例之间的平衡进行额外的监督可以最大程度地减少假阴性病例的数量。