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Published on: December 19, 2020
Clinical Validation of an AI System for Pneumoconiosis Detection Using Chest X-rays
Eduardo R Ruiz1, Carolina A Arellano, Carmen A Archila
1From the Convicción Digital, Talca, Región del Maule, Chile (E.R.R., C.A.A., G.C., F.P.); and Mutual de Seguridad, Santiago, Región Metropolitana, Chile (C.A.A., C.L.).
Objective:
The aims of the study were 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 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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