Machine Learning Applications to Diffuse Reflectance Spectroscopy in Optical Diagnosis; A Systematic Review

2025-03-03

AI Summary

This study addresses the limited efficacy of machine learning (ML) in diffuse reflectance spectroscopy (DRS)-based optical diagnosis for biological tissue identification and differentiation. Following the PRISMA guidelines, a systematic review of 77 studies identifies three critical bottlenecks: inadequate sample stratification, absence of in-vivo validation, and lack of interpretable algorithms. To overcome these, we propose an integrated ML-DRS analytical framework incorporating supervised learning (SVM, random forests, deep learning), spectral preprocessing, and advanced feature engineering. Experimental evaluation demonstrates high classification accuracy across multiple disease categories. Key contributions include: (1) the first comprehensive evidence map for DRS-based ML diagnostics; (2) identification of standardized acquisition protocols, rigorous in-vivo validation, and explainable AI (XAI) integration as essential translational pathways; and (3) a methodological foundation supporting clinical implementation of DRS-ML systems.

Technology Category

• Machine Learning: Transparent, Interpretable, Explainable ML
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Computer Vision: Medical and Biological Imaging

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Machine learning and data science for the Web

Abstract

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.

Problem

• Machine learning enhances diffuse reflectance spectroscopy for tissue differentiation.
• Current research lacks rigorous sample stratification and in-vivo validation.
• Explainable algorithm development is needed for clinical applications.

Innovation

• Machine learning enhances tissue differentiation accuracy
• Algorithmic processing of diffuse reflectance spectroscopy signals
• In-vivo validation and explainable algorithms needed