Development of Artificial Intelligence (AI) based algorithms to classify the Pneumoconioses

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Project Summary Pneumoconiosis is a major occupational lung disease. Medical screening programs of workers exposed to asbestos, coal and silica and the compensation program for Black Lung Benefits require the use of the International Labor Organization (ILO) guidelines to classify radiographs for pneumoconiosis. NIOSH has developed a certification program to standardize the classification. Despite the use of certified B readers in screening and compensation programs, the small number of certified B readers, inter- and intra-reader variability, and potential financial conflict of interest have remained important challenges. There is a pressing need for a system to improve the objective and consistent classification of the pneumoconioses. Artificial intelligence (AI)- based models have demonstrated value for other lung diseases such as lung lesions, edema, and pneumonia. Our study aims to develop AI-based models to assist in the classification of radiographs for the pneumoconioses according to the ILO guidelines. Aim 1 will curate a novel set of expert classified chest radiographs with and without pneumoconiosis for training AI models. Aim 2 will develop machine learned methods including pre- trained Convolutional Neural Network (CNN) methods and hybrid CNN methods combined with handpicked features to distinguish parenchymal abnormalities and pleural abnormalities from normal radiographs. Aim 3 will further classify pneumoconiosis radiographs based on the ILO classification guideline by four major categories of small opacities; affected zones of the lung and shape; three sizes of large opacities and three subtypes of pleural abnormalities. In this aim, Deep Learning (DL) algorithms including Bayesian deep learning and Category-wise residual attention learning (CRAL) algorithm will be developed for uncertainty estimation and higher prediction accuracy in multi-class and multi-label classification problem. Our project will be the first study in the US to develop AI algorithms to classify pneumoconiosis based on the ILO guidelines. Particular attention in algorithm development will be given to the classification of borderline radiographs (i.e. profusion, 0/1 vs. 1/0) with estimated uncertainties. The AI algorithms developed in this study will be tested using a new set of radiographs with expectation of classifying pneumoconiosis based on ILO guideline with high accuracy especially for individuals with an early stage of pneumoconiosis. The project aligns with the NIOSH Research to Practice (r2p) approach, as the results of the proposed algorithms will be shared with NIOSH for dissemination to B readers. Computer-aided algorithms developed in this study will provide an objective and consistent classification that will assist in addressing the problems of small number of certified B readers, inter- and intra- reader variability, and potential financial conflict of interest.