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Accepted for/Published in: Journal of Participatory Medicine

Date Submitted: Dec 1, 2024
Date Accepted: Apr 2, 2025

The final, peer-reviewed published version of this preprint can be found here:

Empowering Patients and Caregivers to Use Artificial Intelligence and Computer Vision for Wound Monitoring: Nonrandomized, Single-Arm Feasibility Study

Raizmam R, Ramírez-GarciaLuna JL, Newaz T, Wang SC, Berry GK, Kong LY, Mohammed HT, Fraser RDJ

Empowering Patients and Caregivers to Use Artificial Intelligence and Computer Vision for Wound Monitoring: Nonrandomized, Single-Arm Feasibility Study

J Particip Med 2025;17:e69470

DOI: 10.2196/69470

PMID: 40466054

PMCID: 12157955

Empowering Patients and Caregivers to Use AI and Computer Vision for Wound Monitoring: A Feasibility Study

  • Rose Raizmam; 
  • José Luis Ramírez-GarciaLuna; 
  • Tanmoy Newaz; 
  • Sheila C Wang; 
  • Gregory K Berry; 
  • Ling Yaun Kong; 
  • Heba Tallah Mohammed; 
  • Robert Douglas John Fraser

ABSTRACT

Background:

Chronic wounds affect 1-2% of the global population, and pose significant health and quality-of-life challenges for patients and caregivers. Advances in artificial intelligence (AI) and computer vision (CV) technologies present new opportunities for enhancing wound care, particularly through remote monitoring and patient engagement. A Digital Wound Care Solution (DWCS) that objective wound tracking using AI/CV was redesigned as a patient-facing mobile application to empower patients and caregivers to actively participate in wound monitoring and management.

Objective:

This study aimed to evaluate the feasibility, usability, and preliminary clinical outcomes of the Patient Connect application in enabling patients and caregivers to remotely capture and share wound data with healthcare providers.

Methods:

A feasibility study was conducted at two outpatient clinics in Canada between May 2020 and February 2021. Twenty-eight patients with chronic wounds were recruited and trained to use the Patient Connect app for wound imaging and secure data sharing with their care teams. Wound images and data were analyzed using AI/CV models integrated into the app. Clinicians reviewed the data to inform treatment decisions during follow-up visits or remotely. Key metrics included app usage frequency, patient engagement, and wound closure rates.

Results:

Participants captured a median of 13 wound images per wound, with images submitted every 8 days on average. The study cohort included patients with diabetic ulcers, venous ulcers, pressure injuries, and post-surgical wounds. A median wound closure rate of 80% was achieved across all patients, demonstrating the app’s clinical potential. Feedback from patients and clinicians highlighted the app’s usability, data security features, and ability to enhance remote monitoring.

Conclusions:

The Patient Connect application effectively engaged patients and caregivers in chronic wound care, demonstrating feasibility and promising clinical outcomes. By enabling secure, remote wound monitoring through AI/CV technology, the app has the potential to improve patient adherence, enhance care accessibility, and optimize clinical workflows. Future studies should focus on evaluating its scalability, cost-effectiveness, and broader applicability in diverse healthcare settings.


 Citation

Please cite as:

Raizmam R, Ramírez-GarciaLuna JL, Newaz T, Wang SC, Berry GK, Kong LY, Mohammed HT, Fraser RDJ

Empowering Patients and Caregivers to Use Artificial Intelligence and Computer Vision for Wound Monitoring: Nonrandomized, Single-Arm Feasibility Study

J Particip Med 2025;17:e69470

DOI: 10.2196/69470

PMID: 40466054

PMCID: 12157955

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