Pneumoconiosis screening and classification using deep learning models

To develop and evaluate different deep learning (DL) models for clinically relevant screening and classification of pneumoconiosis based on chest radiographs.

This retrospective study used B-reader labelled pneumoconiosis lung images from US workers. After pre-processing the images, DL models were trained and evaluated for four classification tasks: (1) parenchymal abnormalities present versus absent, (2) parenchymal and/or pleural abnormalities present versus absent, (3) small parenchymal opacities grade 1 versus grade 0 and (4) progressive massive fibrosis versus simple pneumoconiosis versus no pneumoconiosis.

A total of 2968 lung images were divided as follows: 2294 for training, 287 for validation, 287 for independent in-distribution testing and 100 for out-of-distribution testing from a separate scanner manufacturer dataset. On the in-distribution testing data set, the DL models achieved an accuracy of 0.93 (SD=0.01), 0.92 (SD=0.02) and 0.91 (SD=0.02) for task 1, 2 and 3, respectively. DL had comparable or better performance compared with certified B-readers who had average accuracies of 0.9 (SD=0.11), 0.91 (SD=0.10) and 0.77 (SD=0.21). For task 4, the DL model achieved an accuracy of 0.83 (SD=0.02), which is similar to B-readers' performance (average accuracy of 0.86 (SD=0.12)). Performance was markedly lower on the out-of-distribution test set, with reduced accuracy and AUC.

On independent, in-distribution test images, DL models performed comparably to certified B-readers for tasks 1-4, supporting their potential to augment pneumoconiosis screening and classification. Reduced performance on challenging out-of-distribution cases warrants further study to improve generalisation.