Anatomy-specific Progression Classification in Chest Radiographs via Weakly Supervised Learning.

Purpose To develop a machine learning approach for classifying disease progression in chest radiographs using weak labels automatically derived from radiology reports. Materials and Methods In this retrospective study, a twin neural network was developed to classify anatomy-specific disease progression into four categories: improved, unchanged, worsened, and new. A two-step weakly supervised learning approach was employed, pretraining the model on 243 008 frontal chest radiographs from 63 877 patients (mean age, 51.7 years ± 17.0 [SD]; 34 813 [55%] female) included in the MIMIC-CXR database and fine-tuning it on the subset with progression labels derived from consecutive studies. Model performance was evaluated for six pathologic observations on test datasets of unseen patients from the MIMIC-CXR database. Area under the receiver operating characteristic (AUC) analysis was used to evaluate classification performance. The algorithm is also capable of generating bounding-box predictions to localize areas of new progression. Recall, precision, and mean average precision were used to evaluate the new progression localization. One-tailed paired tests were used to assess statistical significance. Results The model outperformed most baselines in progression classification, achieving macro AUC scores of 0.72 ± 0.004 for atelectasis, 0.75 ± 0.007 for consolidation, 0.76 ± 0.017 for edema, 0.81 ± 0.006 for effusion, 0.7 ± 0.032 for pneumonia, and 0.69 ± 0.01 for pneumothorax. For new observation localization, the model achieved mean average precision scores of 0.25 ± 0.03 for atelectasis, 0.34 ± 0.03 for consolidation, 0.33 ± 0.03 for edema, and 0.31 ± 0.03 for pneumothorax. Conclusion Disease progression classification models were developed on a large chest radiograph dataset, which can be used to monitor interval changes and detect new pathologic conditions on chest radiographs. Prognosis, Unsupervised Learning, Transfer Learning, Convolutional Neural Network (CNN), Emergency Radiology, Named Entity Recognition © RSNA, 2024 See also commentary by Alves and Venkadesh in this issue.