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<p>Diabetic foot Ulceration (DFU) is a severe complication of diabetic foot syndrome, often leading to amputation. In patients with neuropathy, ulcer formation is facilitated by elevated plantar tissue stress under insensate feet. This study presents a plantar pressure distribution analysis method to predict diabetic peripheral neuropathy. The Win-Track platform was used to gather clinical and plantar pressure data from 86 diabetic patients with different degrees of neuropathy. An automated image processing algorithm segmented plantar pressure images into forefoot and hindfoot regions for precise pressure distribution measurement. Comparative analysis of static and dynamic assessment showed that static analysis consistently outperformed dynamic methods. Gradient Boosting achieved the highest accuracy (88% dynamic, 100% static), with Random Forest and Decision Tree also performing well. Explainable AI techniques (SHAP, Eli5, Anchor Explanations) provided insights into feature importance, enhancing model interpretability. Additionally, a foot classification system based on the forefoot-hindfoot pressure ratio categorized feet as flat, regular, or arched. These findings support the development of improved diagnostic tools for early neuropathy detection, aiding risk stratification and prevention strategies. Enhanced screening can help reduce DFU incidence, lower amputation rates, and ultimately decrease diabetes-related mortality.</p>

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Predicting diabetic peripheral neuropathy through advanced plantar pressure analysis: a machine learning approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月1日

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  • Mehewish Musheer Sheikh,
  • Mamatha Balachandra,
  • Narendra V. G.,
  • Arun G. Maiya

摘要

Diabetic foot Ulceration (DFU) is a severe complication of diabetic foot syndrome, often leading to amputation. In patients with neuropathy, ulcer formation is facilitated by elevated plantar tissue stress under insensate feet. This study presents a plantar pressure distribution analysis method to predict diabetic peripheral neuropathy. The Win-Track platform was used to gather clinical and plantar pressure data from 86 diabetic patients with different degrees of neuropathy. An automated image processing algorithm segmented plantar pressure images into forefoot and hindfoot regions for precise pressure distribution measurement. Comparative analysis of static and dynamic assessment showed that static analysis consistently outperformed dynamic methods. Gradient Boosting achieved the highest accuracy (88% dynamic, 100% static), with Random Forest and Decision Tree also performing well. Explainable AI techniques (SHAP, Eli5, Anchor Explanations) provided insights into feature importance, enhancing model interpretability. Additionally, a foot classification system based on the forefoot-hindfoot pressure ratio categorized feet as flat, regular, or arched. These findings support the development of improved diagnostic tools for early neuropathy detection, aiding risk stratification and prevention strategies. Enhanced screening can help reduce DFU incidence, lower amputation rates, and ultimately decrease diabetes-related mortality.