Using Artificial Intelligence for High-Volume Identification of Silicosis and Tuberculosis: A Bio-Ethics Approach

Research progress on the pathogenesis and prediction of pneumoconiosis among coal miners

Pneumoconiosis is the most common occupational disease among coal miners, which is a lung disease caused by long-term inhalation of coal dust and retention in the lungs. The early stage of this disease is highly insidious, and pulmonary fibrosis may occur in the middle and late stages, leading to an increase in patient pain index and mortality rate. Currently, there is a lack of effective treatment methods. The pathogenesis of pneumoconiosis is complex and has many influencing factors. Although the characteristics of coal dust have been considered the main cause of different mechanisms of pneumoconiosis, the effects of coal dust composition, particle size and shape, and coal dust concentration on the pathogenesis of pneumoconiosis have not been systematically elucidated. Meanwhile, considering the irreversibility of pneumoconiosis progression, early prediction for pneumoconiosis patients is particularly important. However, there is no early prediction standard for pneumoconiosis among coal miners. This review summarizes the relevant research on the pathogenesis and prediction of pneumoconiosis in coal miners in recent years. Firstly, the pathogenesis of coal worker pneumoconiosis and silicosis was discussed, and the impact of coal dust characteristics on pneumoconiosis was analyzed. Then, the early diagnostic methods for pneumoconiosis have been systematically introduced, with a focus on image collaborative computer-aided diagnosis analysis and biomarker detection. Finally, the challenge of early screening technology for miners with pneumoconiosis was proposed.

A Systematic Review of Artificial Intelligence Applications in the Management of Lung Disorders

This systematic review examines the transformative impact of artificial intelligence (AI) in managing lung disorders through a comprehensive analysis of articles spanning 2014 to 2023. Evaluating AI's multifaceted roles in radiological imaging, disease burden prediction, detection, diagnosis, and molecular mechanisms, this review presents a critical synthesis of key insights from select articles. The findings underscore AI's significant strides in bolstering diagnostic accuracy, interpreting radiological imaging, predicting disease burdens, and deepening the understanding of tuberculosis (TB), chronic obstructive pulmonary disease (COPD), silicosis, pneumoconiosis, and lung fibrosis. The synthesis positions AI as a revolutionary tool within the healthcare system, offering vital implications for healthcare workers, policymakers, and researchers in comprehending and leveraging AI's pivotal role in lung disease management.

Accuracy of Computer-Aided Detection of Occupational Lung Disease: Silicosis and Pulmonary Tuberculosis in Ex-Miners from the South African Gold Mines

BACKGROUND

Computer-aided detection (CAD) of pulmonary tuberculosis (TB) and silicosis among ex-miners from the South African gold mines has the potential to ease the backlog of lung examinations in clinical screening and medical adjudication for miners' compensation. This study aimed to determine whether CAD systems developed to date primarily for TB were able to identify TB (without distinction between prior and active disease) and silicosis (or "other abnormality") in this population.

METHODS

A total of 501 chest X-rays (CXRs) from a screening programme were submitted to two commercial CAD systems for detection of "any abnormality", TB (any) and silicosis. The outcomes were tested against the readings of occupational medicine specialists with experience in reading miners' CXRs. Accuracy of CAD against the readers was calculated as the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Sensitivity and specificity were derived using a threshold requiring at least 90% sensitivity.

RESULTS

One system was able to detect silicosis and/or TB with high AUCs (>0.85) against both readers, and specificity > 70% in most of the comparisons. The other system was able to detect "any abnormality" and TB with high AUCs, but with specificity < 70%.

CONCLUSION

CAD systems have the potential to come close to expert readers in the identification of TB and silicosis in this population. The findings underscore the need for CAD systems to be developed and validated in specific use-case settings.

Reforming the workers' compensation process for occupational lung disease among miners in South Africa: an efficiency study of claims assessment

PURPOSE

The large burden of silicosis and tuberculosis (TB) in the South African mining industry, coupled with an under-resourcing of the compensation agencies responsible for certifying occupational lung disease, have resulted in serious backlogs. This work aimed to measure the efficiency gains from triaging occupational lung disease claims using claim type, years of mining exposure and computer aided detection (CAD) to save on scarce medical adjudicators.

METHODS

During 2020, the compensation authority started to triage claims for TB and those of miners with < 10 years of service to two-person panels instead of the four-person panel plus radiologist used previously. Efficiency gain was calculated in medical person-units saved and reduction in delays. Different service thresholds predictive of silicosis were simulated, as well as the impact of pre-classification of chest X-rays with CAD using different combinations of sensitivity and specificity.

RESULTS

The new triage system saved 20.3% in person-time units and reduced delays by 10-20 days. Without CAD the greatest efficiency gain (28%) was projected from dispensing with a mining service threshold and routing all non-TB claims to the small panels at the outset. Simulation of four different CAD sensitivity/specificity combinations yielded efficiency gains of 18.2-36.1%, with 31.1% judged the most realistic. Use of sensitivity of close to 100% would not be feasible because of the very low resulting specificity.

CONCLUSION

Pre-adjudication triage of claims at the compensation agency is capable of saving a substantial proportion of adjudicator time and reducing certification delays. Additional efficiency gains are achievable by referring all claims to small panels to begin with and improvement of CAD performance for this ex-miner population.