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

ABSTRACT:

Purpose

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).

Methods

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.

Results

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

SUBMITTER: Kim Y 

PROVIDER: S-EPMC12766351 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature