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.

中文翻译：

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整合常规血液生物标志物和人工智能，以支持人造石工人的矽肺病诊断


工程石矽肺病 （ESS） 主要由吸入可吸入结晶二氧化硅引起，在全球范围内构成重大的职业健康风险。ESS 没有有效的治疗方法，并且表现为从单纯性矽肺病 （SS） 迅速进展为进行性大块纤维化 （PMF），伴有呼吸衰竭和死亡。尽管使用了胸部 X 光检查和高分辨率计算机断层扫描等诊断方法，但矽肺病的早期检测仍然具有挑战性。由于常规血液测试在检测与疾病相关的炎症标志物方面显示出前景，本研究旨在评估常规血液生物标志物结合机器学习技术是否可以有效区分健康个体、SS 受试者和 PMF。为此，招募了 107 名被诊断患有矽肺病的男性、人造石 （ES） 行业的前工人和 22 名健康的男性志愿者作为未暴露于 ES 粉尘的对照。从临床医院记录中回顾性获得来自外周血提取的 21 个主要生化标志物。应用 Relief-F 特征选择技术，并使用 11 个生物标志物的结果子集构建 5 个机器学习模型，在最佳情况下表现出高性能，灵敏度和特异性分别大于 82% 和 89%。淋巴细胞的百分比、血管紧张素转换酶和乳酸脱氢酶指数等被揭示为血液生物标志物，对机器学习模型具有显着的累积重要性。我们的研究表明，这些生物标志物可以检测慢性炎症状态，并可能作为诊断、监测和早期检测矽肺病进展的支持工具。