Automated evaluation of pulmonary lesion changes on chest radiograph during follow-up using semantic segmentation

To develop and validate a deep learning-based model utilizing lesion-specific segmentation to determine the changed/unchanged status of consolidation and pleural effusion in paired chest radiographs (CRs).

The model was trained using 5.178 CRs from a single institution for lesion segmentation. Paired CRs from the emergency department (ED) and intensive care unit (ICU) were used to determine the thresholds for change and temporal validation. Model performance was evaluated through the area under the receiver operating characteristic curve (AUC), and its accuracy was compared with that of a thoracic radiologist.

In the ED, the model achieved AUCs of 0.988 and 0.883 for consolidation and pleural effusion, respectively, with accuracies of 0.900 (36/40) and 0.825 (33/40). The radiologist showed accuracies of 0.975 (39/40) and 0.950 (38/40), respectively. In the ICU, model AUCs were 0.970 (consolidation) and 0.955 (pleural effusion), with accuracies of 0.875 (35/40) and 0.800 (32/40), respectively. Radiologist performance was 0.975 (39/40) for consolidation and 1.000 (40/40) for pleural effusion. No significant accuracy differences were observed between the model and radiologist for consolidation in the ICU or both targets in the ED (all P > 0.05), except for pleural effusion in the ICU (P = 0.01).

The lesion-specific deep learning model was feasible for identifying interval changes in consolidation and pleural effusion on follow-up CRs.

It could potentially be utilized for prioritizing interpretation, generating alerts, and extracting time-series data from multiple follow-up CRs.