Deep Learning Models for Radiography Body-part Classification and Chest Radiograph Projection/Orientation Classification: A Multi-institutional Study

BackgroundLarge-scale radiographic datasets often include errors in labels such as body-part or projection, which can undermine automated image analysis. PurposeTo develop and externally validate two deep learning models--one for categorizing radiographs by body-part, and another for identifying projection and rotation of chest radiographs--using large, diverse datasets. MethodsWe retrospectively collected radiographs from multiple institutions and public repositories. For the first model (Xp-Bodypart-Checker), we included seven categories (Head, Neck, Chest, Incomplete Chest, Abdomen, Pelvis, Extremities). For the second model (CXp-Projection-Rotation-Checker), we classified chest radiographs by projection (anterior-posterior, posterior-anterior, lateral) and rotation (upright, inverted, left rotation, right rotation). Both models were trained, tuned, and internally tested on separate data, then externally tested on radiographs from different institutions. Model performance was assessed using overall accuracy (micro, macro, and weighted) as well as one-vs-all area under the receiver operating characteristic curve (AUC). ResultsIn the Xp-Bodypart-Checker development phase, we included 429,341 radiographs obtained from Institutions A, B, and MURA. In the CXp-Projection-Rotation-Checker development phase, we included 463,728 chest radiographs from CheXpert, PadChest, and Institution A. The Xp-Bodypart-Checker achieved AUC values of 1.00 (99% CI: 1.00-1.00) for all classes other than Incomplete Chest, which had an AUC value of 0.99 (99% CI: 0.98- 1.00). The CXp-Projection-Rotation-Checker demonstrated AUC values of 1.00 (99% CI: 1.00-1.00) across all projection and rotation classifications. ConclusionThese models help automatically verify image labels in large radiographic databases, improving quality control across multiple institutions.