Generalized Radiographic View Identification with Deep Learning

To explore the feasibility of an automatic machine-learning algorithm-based quality control system for the practice of diagnostic radiography, performance of a convolutional neural networks (CNN)-based algorithm for identifying radiographic (X-ray) views at different levels was examined with a retrospective, HIPAA-compliant, and IRB-approved study performed on 15,046 radiographic images acquired between 2013 and 2018 from nine clinical sites affiliated with our institution. Images were labeled according to four classification levels: level 1 (anatomy level, 25 classes), level 2 (laterality level, 41 classes), level 3 (projection level, 108 classes), and level 4 (detailed level, 143 classes). An Inception V3 model pre-trained with ImageNet dataset was trained with transfer learning to classify the image at all levels. Sensitivity and positive predictive value were reported for each class, and overall accuracy was reported for each level. Accuracy was also reported when we allowed for "reasonable errors". The overall accuracy was 0.96, 0.93, 0.90, and 0.86 at levels 1, 2, 3, and 4, respectively. Overall accuracy increased to 0.99, 0.97, 0.94, and 0.88 when "reasonable errors" were allowed. Machine learning algorithms resulted in reasonable model performance for identifying radiographic views with acceptable accuracy when "reasonable errors" were allowed. Our findings demonstrate the feasibility of building a quality-control program based on machine-learning algorithms to identify radiographic views with acceptable accuracy at lower levels, which could be applied in a clinical setting.