# Using Near Infrared Spectroscopy and Machine Learning to diagnose Systemic Sclerosis

## TL;DR

This study explores using low-cost Near Infrared Spectroscopy combined with machine learning to improve the diagnosis of systemic sclerosis, identifying key wavelengths and optimal hand regions for accurate, non-invasive detection.

## Contribution

It introduces a novel application of NIRS with machine learning for systemic sclerosis diagnosis, highlighting important wavelength bands and effective regions for clinical use.

## Key findings

- Wavelength at 1270 nm is most relevant for diagnosis.
- Proximal Interphalangeal Joints region yields better accuracy.
- Low-cost optical spectrometers and open-source algorithms facilitate clinical adoption.

## Abstract

The motivation of this work is the use of non-invasive and low cost techniques to obtain a faster and more accurate diagnosis of systemic sclerosis (SSc), rheumatic, autoimmune, chronic and rare disease. The technique in question is Near Infrared Spectroscopy (NIRS). Spectra were acquired from three different regions of hand's volunteers. Machine learning algorithms are used to classify and search for the best optical wavelength. The results demonstrate that it is easy to obtain wavelength bands more important for the diagnosis. We use the algorithm RFECV and SVC. The results suggests that the most important wavelength band is at 1270 nm, referring to the luminescence of Singlet Oxygen. The results indicates that the Proximal Interphalangeal Joints region returns better accuracy's scores. Optical spectrometers can be found at low prices and can be easily used in clinical evaluations, while the algorithms used are completely diffused on open source platforms.