Clinical Validation of an AI System for Pneumoconiosis Detection Using Chest X-rays.

Journal of occupational and environmental medicine Pub Date : 2025-01-28 DOI:10.1097/JOM.0000000000003329

Eduardo R Ruiz, Carolina A Arellano, Carmen A Archila, Carolina Llobet, Gonzalo Carrasco, Francisca Pinochet

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Abstract

Abstract: Objective: To develop and evaluate "eTóraxLaboral," an intelligent platform for detecting signs of pneumoconiosis in chest radiographs, and to assess its predictive capacity. Methods: A retrospective analysis of 2300 randomly selected chest radiographs was performed. Sensitivity, specificity, false positive/negative rates, predictive values, likelihood ratios, efficiency, error rate, and area under the receiver operating characteristic curve (AUC) were evaluated. A Fagan nomogram and ROC curve analysis were included. Results: "eTóraxLaboral" demonstrated high sensitivity to signs of pneumoconiosis (LR+ 23, LR- 0.2). A slight tendency toward a higher number of false positives was observed, possibly due to the superposition of anatomical elements and increased lung markings. False negatives were less common, often misinterpreting pneumoconiotic opacities as consolidation-type findings. Conclusions: "eTóraxLaboral" facilitates early pneumoconiosis detection, providing crucial diagnostic support for healthcare workers in Chile and other developed or developing nations.

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