Machine Learning Applications to Diffuse Reflectance Spectroscopy in Optical Diagnosis; A Systematic Review
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910859086790656 |
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| author | Rossberg, Nicola Li, Celina L. Innocente, Simone Andersson-Engels, Stefan Komolibus, Katarzyna O'Sullivan, Barry Visentin, Andrea |
| author_facet | Rossberg, Nicola Li, Celina L. Innocente, Simone Andersson-Engels, Stefan Komolibus, Katarzyna O'Sullivan, Barry Visentin, Andrea |
| contents | Diffuse Reflectance Spectroscopy has demonstrated a strong aptitude for identifying and differentiating biological tissues. However, the broadband and smooth nature of these signals require algorithmic processing, as they are often difficult for the human eye to distinguish. The implementation of machine learning models for this task has demonstrated high levels of diagnostic accuracies and led to a wide range of proposed methodologies for applications in various illnesses and conditions. In this systematic review, we summarise the state of the art of these applications, highlight current gaps in research and identify future directions. This review was conducted in accordance with the PRISMA guidelines. 77 studies were retrieved and in-depth analysis was conducted. It is concluded that diffuse reflectance spectroscopy and machine learning have strong potential for tissue differentiation in clinical applications, but more rigorous sample stratification in tandem with in-vivo validation and explainable algorithm development is required going forward. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_02905 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning Applications to Diffuse Reflectance Spectroscopy in Optical Diagnosis; A Systematic Review Rossberg, Nicola Li, Celina L. Innocente, Simone Andersson-Engels, Stefan Komolibus, Katarzyna O'Sullivan, Barry Visentin, Andrea Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition 68Txx J.3 Diffuse Reflectance Spectroscopy has demonstrated a strong aptitude for identifying and differentiating biological tissues. However, the broadband and smooth nature of these signals require algorithmic processing, as they are often difficult for the human eye to distinguish. The implementation of machine learning models for this task has demonstrated high levels of diagnostic accuracies and led to a wide range of proposed methodologies for applications in various illnesses and conditions. In this systematic review, we summarise the state of the art of these applications, highlight current gaps in research and identify future directions. This review was conducted in accordance with the PRISMA guidelines. 77 studies were retrieved and in-depth analysis was conducted. It is concluded that diffuse reflectance spectroscopy and machine learning have strong potential for tissue differentiation in clinical applications, but more rigorous sample stratification in tandem with in-vivo validation and explainable algorithm development is required going forward. |
| title | Machine Learning Applications to Diffuse Reflectance Spectroscopy in Optical Diagnosis; A Systematic Review |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition 68Txx J.3 |
| url | https://arxiv.org/abs/2503.02905 |