Machine Learning Applications to Diffuse Reflectance Spectroscopy in Optical Diagnosis; A Systematic Review
This review synthesizes existing research for clinicians and researchers, highlighting gaps and future directions, but it is incremental as it does not introduce new methods or data.
This systematic review analyzed 77 studies on machine learning applications for diffuse reflectance spectroscopy in optical diagnosis, finding that these methods show strong potential for tissue differentiation in clinical settings but require more rigorous validation and explainability.
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.