RT journal article
T1 Integrating routine blood biomarkers and artificial intelligencefor supporting diagnosis of silicosis in engineered stone workers
A1 Sánchez Morillo, Daniel
A1 León Jiménez, Antonio
A1 Guerrero Chanivet, María
A1 Jiménez-Gómez, Gema
A1 Hidalgo Molina, Antonio
A1 Campos Caro, Antonio
A2 Ingeniería en AutomáticaElectrónica, Arquitectura y Redes de Computadores
K1 artificial stone
K1 blood biomarkers
K1 engineered stone
K1 machine learning
K1 silica agglomerate
K1 silicosis
AB Engineered stone silicosis (ESS), primarily caused by inhaling respirable crystalline silica, poses a significant occupational health risk globally. ESS has no effective treatment and presents a rapid progression from simple silicosis (SS) to progressive massive fibrosis (PMF), with respiratory failure and death. Despite the use of diagnostic methods like chest x-rays and high-resolution computed tomography, early detection of silicosis remains challenging. Since routine blood tests have shown promise in detecting inflammatory markers associated with the disease, this study aims to assess whether routine blood biomarkers, coupled with machine learning techniques, can effectively differentiate between healthy individuals, subjects with SS, and PMF. To this end, 107 men diagnosed with silicosis, ex-workers in the engineered stone (ES) sector, and 22 healthy male volunteers as controls not exposed to ES dust were recruited. Twenty-one primary biochemical markers derived from peripheral blood extraction were obtained retrospectively from clinical hospital records. Relief-F features selection technique was applied, and the resulting subset of 11 biomarkers was used to build five machine learning models, demonstrating high performance with sensitivities and specificities in the best case greater than 82% and 89%, respectively. The percentage of lymphocytes, the angiotensin-converting enzyme, and lactate dehydrogenase indexes were revealed, among others, as blood biomarkers with significant cumulative importance for the machine learning models. Our study reveals that these biomarkers could detect a chronic inflammatory status and potentially serve as a supportive tool for the diagnosis, monitoring, and early detection of the progression of silicosis.
PB Wiley Periodicals LLC
SN 2380-6761
YR 2024
FD 2024-06-28
LK http://hdl.handle.net/10498/32829
UL http://hdl.handle.net/10498/32829
LA eng
NO This work was funded in the “Convocatoria 2021 de Ayudas a Proyectos de Excelencia, en régimen de concurrencia competitiva, destinadas a entidades calificadas como Agentes del Sistema Andaluz del Conocimiento, en el ámbito del Plan Andaluz de Investigación, Desarrollo e Innovación (PAIDI 2020). Consejería de Universidad, Investigación e Innovación de la Junta de Andalucía,” under grant number ProyExcel_00942 and by the Programa Estatal de Generación de Conocimiento y Fortalecimiento del Sistema Español de I + D + i, funded by the Instituto de Salud Carlos III and Fondo Europeo de Desarrollo Regional (FEDER) 2014–2020 (grant number PI19/01064) and 2021-2027 (grant number PI23/01475).
DS Repositorio Institucional de la Universidad de Cádiz
RD 09-nov-2026