Diagnostic Machine Learning Applications on Clinical Populations using Functional Near Infrared Spectroscopy: A Review

Abstract

Objective : There is a growing interest in functional near-infrared spectroscopy (fNIRS) among researchers due to its remarkable advantages such as ease of use, being inexpensive and less tolerance to motion artifacts compared to other neuroimaging modalities. Also, its interaction with machine learning (ML) approaches was inevitable like other neuroimaging modalities for different purposes such as diagnostic classification of diseases or prediction of disease severity due to the lack of robust and objective biomarkers. A review of literature is carried out to understand the evolution of biomarker research on clinical populations and clinical states by combining fNIRS and ML. Approach : In this review, article search was carried out in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) standard and 62 studies were evaluated and we provided a general overview by using fNIRS, particularly in clinical populations and some clinically relevant conditions on healthy populations. Also, potential biomarkers that were found in these studies and some popular ML algorithms that had been used for the prediction or classification of fNIRS data were discussed. Main Results : There is an increasing trend to perform ML applications on fNIRS data on biomarker research related to different clinical fields. Among these studies, few were able to have a notable number of participants for classification and clinical state prediction. Oxy-hemoglobin was used more than deoxy-hemoglobin in ML-based studies as a potential feature source. Significance : Using ML on fNIRS data might be a promising approach to revealing specific biomarkers for either diagnostic classification of diseases or prediction of clinical conditions.