Digital wound monitoring with artificial intelligence to prioritise surgcial wounds in cardiac surgery: protocol for a randomised feasibility trial (WISDOM)

Tanner, Judith; Melissa, Rochon; Roy, Harris; Jacqueline, Beckhelling; James, Jurkiewicz; Lara, Mason; Janet, Boutell; Sarah, Bolton; Jon, Dummer; Kieth, Wilson; Luxmi, Dhoonmoon; Karen, Cariaga

Abstract

Introduction: Digital surgical wound monitoring for patients at home is becoming an increasingly common method of wound follow-up. This regular monitoring improves patient outcomes by detecting wound complications early and enabling treatment to start before complications worsen. However, reviewing the digital data creates a new and additional workload for staff. The aim of this study is to assess a surgical wound monitoring platform that uses artificial intelligence to assist clinicians to review patients’ wound images by prioritising concerning images for urgent review. This will manage staff time more effectively.

Methods and analysis: This is a feasibility study for a new artificial intelligence module with 120 cardiac surgery patients at two centres serving a range of patient ethnicities and urban, rural and coastal locations. Each patient will be randomly allocated using a 1:1 ratio with mixed block sizes to receive the platform with the new detection and prioritising module (for up to 30 days after surgery) plus standard postoperative wound care or standard postoperative wound care only. Assessment is through surveys, interviews, phone calls and platform review at 30 days and through medical notes review and patient phone calls at 60 days. Outcomes will assess safety, acceptability, feasibility and health economic endpoints. The decision to proceed to a definitive trial will be based on prespecified progression criteria.

Ethics and dissemination: Permission to conduct the study was granted by the North of Scotland Research Ethics Committee 1 (24/NS0005) and the MHRA (CI/2024/0004/GB). The results of this Wound Imaging Software Digital platfOrM (WISDOM) study will be reported in peer-reviewed open-access journals and shared with participants and stakeholders.

Citation

Tanner, J., Melissa, R., Roy, H., Jacqueline, B., James, J., Lara, M., Janet, B., Sarah, B., Jon, D., Kieth, W., Luxmi, D., & Karen, C. (2024). Digital wound monitoring with artificial intelligence to prioritise surgcial wounds in cardiac surgery: protocol for a randomised feasibility trial (WISDOM). BMJ Open, 14(9), Article e086486. https://doi.org/10.1136/bmjopen-2024-086486

• Journal Article Type: Article
• Acceptance Date: Jul 30, 2024
• Online Publication Date: Sep 17, 2024
• Publication Date: 2024-09
• Deposit Date: Sep 10, 2024
• Publicly Available Date: Sep 20, 2024
• Journal: BMJ Open
• Electronic ISSN: 2044-6055
• Publisher: BMJ Publishing Group
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Issue: 9
• Article Number: e086486
• DOI: https://doi.org/10.1136/bmjopen-2024-086486
• Public URL: https://nottingham-repository.worktribe.com/output/39453773
• Publisher URL: https://bmjopen.bmj.com/content/14/9/e086486