Beyond diagnosis: deep-learning-based analysis of hospitalization using abdominal radiographs in the emergency department.

purposeAbdominal radiography (AXR) is routinely performed in emergency departments (ED) but has limited clinical utility. Thus, this study developed deep-learning models using AXR to predict hospitalization in ED patients with abdominal symptoms.

methodsThis retrospective study included 1,585 adult ED patients with abdominal symptoms who underwent AXR between August and December 2021. External validation included 112 patients. Three prediction models were developed using random forest classifiers: an image model, a clinical model, and a fusion model combining both image and clinical features. DenseNet201 extracted image features, and early clinical information obtainable by non-physician staff was incorporated. The performances of the two radiologists were compared with those of a deep-learning model.

resultsThe fusion model achieved an area under the receiver operating characteristic curve (AUROC) of 0.70 (95% confidence interval: 0.65-0.76), sensitivity of 0.82, and an F1-score of 0.75 in internal validation. Human readers showed high specificity (0.80-0.95) but low sensitivity (0.13-0.52) and F1-scores (0.23-0.55). Deep-learning models achieved substantially higher sensitivity and F1-scores than human readers, but with lower specificity (0.43), making them suitable for screening. In the external validation, the performance of the fusion model decreased (AUROC, 0.60), but image-based models maintained a higher sensitivity than human readers. Of all the radiographs, 11.1% showed abnormal findings, and 5.8% were specific to the final diagnosis.

conclusionDeep learning significantly improved the clinical utility of AXR in screening for hospitalization risk, achieving higher sensitivity and F1-scores than radiologists. While these models require further improvement for clinical implementation, they can potentially extract predictive patterns from traditionally limited imaging studies.