Abstract
Functional near-infrared spectroscopy (fNIRS) and its interaction with machine learning (ML) is a popular research topic for the diagnostic classification of clinical disorders due to the lack of robust and objective biomarkers. This review provides an overview of research on psychiatric diseases by using fNIRS and ML. Article search was carried out and 45 studies were evaluated by considering their sample sizes, used features, ML methodology, and reported accuracy. To our best knowledge, this is the first review that reports diagnostic ML applications using fNIRS. We found that there has been an increasing trend to perform ML applications on fNIRS-based biomarker research since 2010. The most studied populations are schizophrenia (n = 12), attention deficit and hyperactivity disorder (n = 7), and autism spectrum disorder (n = 6) are the most studied populations. There is a significant negative correlation between sample size (>21) and accuracy values. Support vector machine (SVM) and deep learning (DL) approaches were the most popular classifier approaches (SVM = 20) (DL = 10). Eight of these studies recruited a number of participants more than 100 for classification. Concentration changes in oxy-hemoglobin (ΔHbO) based features were used more than concentration changes in deoxy-hemoglobin (ΔHb) based ones and the most popular ΔHbO-based features were mean ΔHbO (n = 11) and ΔHbO-based functional connections (n = 11). Using ML on fNIRS data might be a promising approach to reveal specific biomarkers for diagnostic classification.
Acknowledgments
We would like to thank Prof. Dr. Turgut Durduran from the Institute of Photonic Sciences (ICFO, Barcelona, Spain) for his valuable and constructive suggestions during the planning and development of this review.
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Research ethics: Not applicable.
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Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Competing interests: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
References
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Articles in the same Issue
- Frontmatter
- Studying the Alzheimer’s disease continuum using EEG and fMRI in single-modality and multi-modality settings
- Diversity of amyloid beta peptide actions
- Empowering brain cancer diagnosis: harnessing artificial intelligence for advanced imaging insights
- Diagnostic machine learning applications on clinical populations using functional near infrared spectroscopy: a review
- Exploring the latest findings on endovascular treatments for giant aneurysms: a review
- Evolving frontiers: endovascular strategies for the treatment of delayed cerebral ischemia
- Inflammation and oxidative stress in epileptic children: from molecular mechanisms to clinical application of ketogenic diet
Articles in the same Issue
- Frontmatter
- Studying the Alzheimer’s disease continuum using EEG and fMRI in single-modality and multi-modality settings
- Diversity of amyloid beta peptide actions
- Empowering brain cancer diagnosis: harnessing artificial intelligence for advanced imaging insights
- Diagnostic machine learning applications on clinical populations using functional near infrared spectroscopy: a review
- Exploring the latest findings on endovascular treatments for giant aneurysms: a review
- Evolving frontiers: endovascular strategies for the treatment of delayed cerebral ischemia
- Inflammation and oxidative stress in epileptic children: from molecular mechanisms to clinical application of ketogenic diet