Diagnosis of disease using laser-induced breakdown spectroscopy and machine learning

A system for screening patients with, diagnosing, or monitoring progress of a pathology using laser induced breakdown spectroscopy (LIBS) and biological fluids, using laser induced breakdown spectroscopy (LIBS) and biomedical fluids, the system comprising:

• a substrate configured to have a sample of a predetermined biological fluid deposited on the substrate;
• a laser light source;
• an optical subsystem configured to receive light from the laser light source and focus the received light on the sample deposited on the substrate, wherein the energy and pulse length of the laser light source are configured to cause ablation of the sample and substrate and formation of a plasma;
• a light collection optical subsystem configured to collect optical emission from the plasma;
• a spectroscopic acquisition component configured to receive collected optical emission from the light collection optical subsystem and to provide spectral data, the spectroscopic acquisition component including a spectrometer and a detector;
• a processing component including one or more processors and configured to receive the spectral data from the spectroscopic acquisition component, wherein the one or more processors are configured use a machine learning algorithm to screen a patient with, diagnose, or monitor progress of the pathology in the sample, wherein the machine learning algorithm is trained on a training set that includes spectral features in a difference spectrum derived from differences between a first LIBS optical emission spectrum collected from one or more samples of the predetermined biological fluid that have the pathology or known progress of the pathology and a second LIBS optical emission spectrum collected from one or more samples of the predetermined biological fluid that do not have the pathology or the known progress of the pathology.