Reza Basiri

Research

Journal article, January 2024 Open access

Protocol for metadata and image collection at diabetic foot ulcer clinics: enabling research in wound analytics and deep learning

Reza Basiri, Karim Manji, Philip M. LeLievre, John Toole, Faith Kim, Shehroz S. Khan, Milos R. Popovic

BioMedical Engineering OnLine, 23(1), 12. DOI 10.1186/s12938-024-01210-6

In brief

Good wound AI starts with good data. This paper sets out a standard way to collect diabetic foot ulcer data at the clinic, including a dedicated case report form, and introduces the resulting Zivot dataset: colour photos with matching thermal and depth maps and full clinical metadata. A baseline detector trained on it shows the data is clean enough to learn from.

Key findings

  • Zivot: 269 patients, about 3,700 RGB images with paired thermal and depth maps
  • A standardized case report form for wound clinics
  • Baseline EfficientNet and UNet detector reaches an F1 of 0.79 and an mAP of 0.86

Read at the publisherPDFPreprint

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. 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