OA10.1. Deep Learning for Hiatal Hernia Detection on Barium Swallow Images Using a Limited Dataset
TL;DR
Deep learning models can support detection and classification of hiatal hernias using focused AP barium swallow images, even when trained on a limited dataset, and this training approach may enable automated identification and classification while potentially reducing radiation exposure and barium ingestion during diagnostic studies.
Abstract
Benign Disease: New Technologies Hiatal hernia diagnosis using barium swallow studies often requires multiple image acquisitions to visualize the esophagogastric junction adequately. Repeated acquisitions may increase radiation exposure and barium ingestion. Interpretation is observer-dependent. The aim of this study was to train an artificial intelligence model for detecting and classifying hiatal hernias. A limited dataset of 70 anonymized barium swallow images centered on the esophagogastric junction was retrospectively analyzed and classified as hiatal hernia (n=45) or normal (n=25). Images were standardized using region-of-interest cropping, autocontrast adjustment, resizing to 512×512 pixels, and data augmentation to mitigate small-sample limitations. The dataset was divided into training (70%), validation (15%), and testing (15%) subsets. Three pretrained convolutional neural networks (ResNet18, DenseNet121, EfficientNetB0) were fine-tuned using transfer learning. A structured grid search (72 trials) optimized learning rate, dropout, weight decay, and training epochs under identical cross-validation conditions. The primary evaluation metric was mean area under the curve (AUC). Secondary metrics included F1-score, accuracy, sensitivity, and specificity. Grad-CAM visualization was applied to assess anatomical regions influencing predictions. Despite the limited dataset and use of focused images, all architectures demonstrated strong internal performance. DenseNet121 achieved mean AUC and F1 values of 1.00 in cross-validation, while EfficientNetB0 showed the strongest out-of-fold performance (AUC 0.9733; F1 0.9556). ResNet18 achieved mean cross-validation AUC 0.9956 and F1 0.9882. Under a unified grid-search comparison framework, ResNet18 demonstrated the most consistent global performance (mean AUC 0.7860; F1 0.7662; accuracy 0.7571; sensitivity 0.6215; specificity 1.0000), supporting its selection as the final model. Grad-CAM confirmed consistent attention to the esophagogastric junction region. The prototype web application generated automated binary classification with confidence scoring. Deep learning models can support detection and classification of hiatal hernias using focused AP barium swallow images, even when trained on a limited dataset. This training approach may enable automated identification and classification while potentially reducing radiation exposure and barium ingestion during diagnostic studies.