A Review of Explainable Machine Learning in Medical Thermography for Interpretable Thermal Feature Analysis and Biomarker Discovery

초록

Medical thermography is a noninvasive, contactless imaging technique that captures spatial temperature distributions across the human body, providing insights into vascular function, inflammation, metabolism, physiological regulation, and aging. Recently, machine learning has been increasingly utilized to analyze thermographic data for disease screening, functional assessment, and biomarker identification. However, the existing literature is fragmented, with varied clinical applications, feature-engineering strategies, and predictive modeling frameworks, often lacking a focus on interpretability and the reliable identification of clinically relevant thermal markers. This review offers a structured overview of explainable machine learning in medical thermography, emphasizing thermal feature representation, model interpretability, and biomarker discovery. It categorizes thermographic features into pixel-based representations, region-wise statistical descriptors, texture measures, and deep latent features. Additionally, it evaluates conventional machine learning and deep learning methods for classification, regression, and risk assessment tasks. The review pays special attention to interpretable learning strategies, such as feature importance analysis, surrogate explanation models, saliency-based visualization, and Shapley-value-based methods, which can enhance transparency and confidence in model outputs. Key challenges are critically discussed, including imaging variability, limited dataset sizes, weak protocol standardization, class imbalance, generalizability, and the gap between predictive performance and clinical trust. Overall, this review synthesizes current advancements, identifies major research gaps, and outlines future directions for developing trustworthy machine learning frameworks in medical thermography and enhancing interpretable thermal biomarker discovery.

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