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

> **NIH ALLCDC R21** · MICHIGAN STATE UNIVERSITY · 2022 · $216,959

## Abstract

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.

## Key facts

- **NIH application ID:** 10428946
- **Project number:** 1R21OH012384-01
- **Recipient organization:** MICHIGAN STATE UNIVERSITY
- **Principal Investigator:** Adam M Alessio
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** ALLCDC
- **Fiscal year:** 2022
- **Award amount:** $216,959
- **Award type:** 1
- **Project period:** 2022-09-30 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10428946

## Citation

> US National Institutes of Health, RePORTER application 10428946, Development of  Artificial Intelligence (AI) based algorithms to classify the Pneumoconioses (1R21OH012384-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10428946. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*