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<p>Diabetic foot ulcers (DFUs) are among the most serious complications of diabetes mellitus, often resulting in infection, amputation, and increased mortality. Early detection is essential but remains difficult due to the complex interaction of neuropathy, vascular disease, and immune dysfunction. This review examines the effectiveness of thermal imaging, including approaches supported by artificial intelligence (AI), as a non-invasive tool for identifying early signs of DFUs. A total of 49 studies published between 1991 and 2024 were analysed, focusing on adult patients and primary research only. Findings show that thermal imaging can detect abnormal skin temperature patterns and early inflammation, key indicators of DFU development. AI techniques, such as machine learning and neural networks, further enhance diagnostic accuracy by identifying subtle patterns and predicting ulcer risk. Despite promising results, several limitations were noted: lack of standardised imaging protocols, inconsistent equipment quality, and small sample sizes in many studies. To improve clinical reliability, future work should focus on developing standard procedures, integrating AI with high-resolution thermal cameras, and validating these systems in real-world hospital and home-care settings. Overall, thermal imaging, especially when combined with AI, shows strong potential as a practical, non-invasive method for early DFU detection and monitoring.</p>

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Use of thermal imaging in the detection of the diabetic foot- review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月14日

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  • Aleksandra Mrowiec,
  • Teresa Kasprzyk-Kucewicz,
  • Daria Wziątek-Kuczmik,
  • Agata Stanek,
  • Armand Cholewka

摘要

Diabetic foot ulcers (DFUs) are among the most serious complications of diabetes mellitus, often resulting in infection, amputation, and increased mortality. Early detection is essential but remains difficult due to the complex interaction of neuropathy, vascular disease, and immune dysfunction. This review examines the effectiveness of thermal imaging, including approaches supported by artificial intelligence (AI), as a non-invasive tool for identifying early signs of DFUs. A total of 49 studies published between 1991 and 2024 were analysed, focusing on adult patients and primary research only. Findings show that thermal imaging can detect abnormal skin temperature patterns and early inflammation, key indicators of DFU development. AI techniques, such as machine learning and neural networks, further enhance diagnostic accuracy by identifying subtle patterns and predicting ulcer risk. Despite promising results, several limitations were noted: lack of standardised imaging protocols, inconsistent equipment quality, and small sample sizes in many studies. To improve clinical reliability, future work should focus on developing standard procedures, integrating AI with high-resolution thermal cameras, and validating these systems in real-world hospital and home-care settings. Overall, thermal imaging, especially when combined with AI, shows strong potential as a practical, non-invasive method for early DFU detection and monitoring.