Analysis of foot thermography images of diabetic patients using artificial intelligence: a scoping review.

Abstract

Artificial intelligence (AI) enhances thermal image analysis by providing advanced pattern recognition and improving the accuracy of diabetic foot condition detection. AI-driven thermography systems support clinicians, but research on AI for diabetic foot thermography is fragmented, with diverse algorithms and existing reviews focusing mainly on statistical performance. This review aimed to provide a comprehensive review of AI-based diabetic foot thermography, with a focus on condition detection, performance metrics, clinical implications, and existing research gaps. A scoping review was conducted using PubMed, MEDLINE, CINAHL, ScienceDirect, Scopus, and Google Scholar, with keywords "diabetic foot temperature," "thermal imaging," and "artificial intelligence," including related MeSH terms. Eligible studies included original research and conference proceedings on AI-based foot thermography for diagnosing or monitoring diabetic adults. Literature reviews and meta-analyses were excluded. Sixty articles were reviewed. Most studies addressed increased temperature, followed by decreased temperature, and DFU severity classification pattern. AI performance ranged from 61% to 100%. Study environments were 46.67% controlled, 6.67% uncontrolled, and 46.67% unreported. AI applications included clinical decision support, remote monitoring, and reducing clinician workload. AI has advanced diabetic foot detection; however, additional studies in uncontrolled environments are needed to improve accuracy and enhance generalizability under real-world conditions.