Reza Basiri

Research

PhD thesis, October 2025 Open access

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

Reza Basiri

University of Toronto

In brief

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.

Key findings

  • Zivot: about 10,000 annotated images with clinical metadata from 268 patients, the largest multimodal diabetic foot ulcer collection reported
  • 22 clinical indicators that classify healing phase without laboratory tests
  • A Generative Adaptive Multimodal Attention Network that classifies healing phase correctly in more than 70% of cases, ahead of any single modality

Read the full text

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. Applications of Generative AI in Diabetic Foot Ulcer Treatment

    Reza Basiri

    Book chapter. IntechOpen, 2025 Open access

    A single-author review of how generative AI is changing diabetic foot ulcer care: synthetic images where real data is scarce, automated clinical documentation, treatment planning and healing forecasts. It also covers what clinical validation and the new regulatory guidance for AI-enabled devices mean for getting these tools into practice.

    SummaryPublisher

  3. Accessible healing phase classification of diabetic foot ulcer

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

    Journal article. Computers in Biology and Medicine, 197, 111066, 2025

    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.

    SummaryPublisher