Reza Basiri

Research

Journal article, September 2025

Accessible healing phase classification of diabetic foot ulcer

Reza Basiri, Charles de Mestral, Milos R. Popovic, Shehroz S. Khan

Computers in Biology and Medicine, 197, 111066. DOI 10.1016/j.compbiomed.2025.111066

In brief

Knowing whether a wound is in the inflammatory, proliferative or remodelling phase steers treatment, but judging it takes specialist experience. This study shows a machine learning model can make that call from routine patient and wound information, and uses explainable AI to show which clinical features drive the decision.

Key findings

  • Trained on 890 visits from 268 patients in the Zivot dataset, with 80 candidate features
  • 22 essential features match the accuracy of a much larger feature set
  • SHAP analysis identifies the clinical signals that track healing, useful for triage where specialists are scarce

Read at the publisher

Related work

  1. Temporal machine learning framework for diabetic foot ulcer healing trajectory prediction

    Reza Basiri, Asem Saleh, Shehroz S. Khan, Milos R. Popovic

    Journal article. BioMedical Engineering OnLine, 25(1), 41, 2026 Open access

    Wound care is mostly reactive: treatment changes after a wound has already stalled. This framework looks one appointment ahead. Using only the clinical notes clinics already record, with no imaging, it predicts whether a diabetic foot ulcer will move in a favourable, acceptable or unfavourable direction by the next visit, and pairs that forecast with treatment suggestions.

    SummaryPublisher

  2. Multimodal Artificial Intelligence System to Enhance the Classification and Analysis of Diabetic Foot Ulcer Healing Conditions

    Reza Basiri

    PhD thesis. University of Toronto, 2025 Open access

    The doctoral work that ties the research programme together. It builds the Zivot dataset, ranks the clinical signs that matter most for healing, measures how colour, thermal and depth imaging complement each other, and introduces an attention-based network that fuses all of them to classify a wound's healing phase.

    SummaryFull text

  3. Enhancing Diabetic Foot Ulcer Assessment Through Fine-Tuned Vision-Language Models

    Reza Basiri, Asad Ghaffar, Donya Ghiasi, Meaka Tesfaye Mekonnen, Milos R. Popovic, Shehroz S. Khan

    Conference paper. IJCAI 2025 workshops, Communications in Computer and Information Science (Springer), 117-131, 2025

    General-purpose vision-language models can describe a photograph, but not the way a wound-care clinician would. This work fine-tunes vision-language models on diabetic foot ulcer images so they assess and describe wounds in clinical terms, moving from the off-the-shelf approach of UlcerGPT to models adapted to the domain.

    SummaryPublisher