[Diagnostic prediction of early silicosis patients using neural network and MALDI-TOF-MS in serum]

Serum of 79 workers exposed to silica and 25 healthy controls cases were determined by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). 7 protein peaks were selected and used by artificial neural network (ANN) to establish a diagnostic model. A blinded test showed that accuracy, sensitivity and specificity were 91.35%, 93.69%, and 84.52%, respectively. The diagnostic pattern was also established to distinguish each stage of silica-exposed population. The diagnostic pattern worked excellently with 89.23%, 94.20% and 92.37% of accurate rate for classifying phase 0, phase 0+, and phase I of silicosis, respectively.