ArticleBioengineering & translational medicine2024
Integrating routine blood biomarkers and artificial intelligence for supporting diagnosis of silicosis in engineered stone workers.
Article in Bioengineering & translational medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Multimodal Fusion of Chest X-Rays and Blood Biomarkers for Automated Silicosis Staging.Journal of clinical medicine · 2025Article
- Cytokine Profiles as Predictive Biomarkers of Disease Severity and Progression in Engineered Stone Silicosis: A Machine Learning Approach.Diagnostics (Basel, Switzerland) · 2025Article
- Machine learning-assisted point-of-care diagnostics for cardiovascular healthcare.Bioengineering & translational medicine · 2025Review
- TMEM175 activity in BK-deficient macrophages maintains lysosomal function and mediates silica-induced inflammatory response in macrophages.Inhalation toxicology · 2025Article
- Integrating CT radiomics and clinical data with machine learning to predict fibrosis progression in coalworker pneumoconiosis.Frontiers in medicine · 2025Article
- Internal and external silica dust exposure threshold as an early screening index for silicosis: a cross-sectional study.Frontiers in public health · 2025Article
- Coronary heart disease risk prediction based on GAIN imputation and interpretable machine learning.Frontiers in genetics · 2025Article
- Integrating routine blood biomarkers and artificial intelligence for supporting diagnosis of silicosis in engineered stone workers.Bioengineering & translational medicine · 2024Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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-
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