Reza Basiri

Research

Journal article, February 2026 Open access

Temporal machine learning framework for diabetic foot ulcer healing trajectory prediction

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

BioMedical Engineering OnLine, 25(1), 41. DOI 10.1186/s12938-026-01529-2

In brief

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.

Key findings

  • 78% accuracy and an average AUC of 0.90 for next-visit trajectory, validated at the patient level
  • A feature set cut by about 70% to 30 predictors, led by the wound's healing history
  • Offloading recommendations matched clinicians 88.7% of the time; dressing advice was strongest for acute wounds and, as expected, weakest for the most chronic ones

Read at the publisher

Related work

  1. 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

  2. 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

  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