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May 6, 2026Endocrinology Diabetes & MetabolismOpen Access

Predicting Diabetic Foot Ulcer Outcomes: Machine Learning‐Based Refinement of IWGDF ‐Approved Classifications for Outpatient Services

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Authors

FMFarideh MostafaviMAMohammad Reza AminiYMYadollah Mehrabi

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Overview

Prospective cohort study improved prediction of outcomes in diabetic foot ulcers, suggesting refinements for classification systems.

Key Points

  • This research aims to validate and refine IWGDF-approved classification systems for diabetic foot ulcers.
  • Conducted a prospective cohort study involving 616 diabetic foot ulcers from 400 patients.
  • Assessed six wound classification systems: Wagner, UTWCS, PEDIS/IDSA, SINBAD, WIFI, DiaFORA.
  • Employed machine learning techniques for feature selection, including LASSO and random forest.
  • SINBAD and UTWCS were most effective in predicting poor outcomes.
  • Modifications to the WIFI system improved its predictive capabilities compared to others.

Cite This Study

Mostafavi et al. (2026) studied this question.

synapsesocial.com/papers/69faa2e204f884e66b5337fbhttps://doi.org/10.1002/edm2.70193
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