AI-enabled digital wound monitoring after cardiac surgery: a randomised controlled feasibility, safety and acceptability trial

BACKGROUND: Surgical site infection after cardiac surgery is a common cause of morbidity and unplanned healthcare use, with most infections developing after hospital discharge. Remote wound monitoring using smartphone technology and artificial intelligence (AI) may support earlier identification of complications. AIM: To evaluate the feasibility, acceptability, and safety of an AI-enabled digital wound monitoring platform plus usual care (Isla-AI) compared with usual care (UC) alone. DESIGN, SETTING, AND PARTICIPANTS: This multi-centre, two-arm randomised controlled feasibility trial was conducted at two UK hospitals between August 2024 and January 2025. Adults undergoing cardiac surgery were randomised to receive Isla-AI or UC. The study was not powered to assess effectiveness. RESULTS: 120 patients were randomised and participated (Isla-AI n=62; UC n=58). Feasibility targets were exceeded: 60% of eligible patients approached consented, 95% of Isla-AI participants submitted at least one image, and 92% completed the study. 98% of images were suitable for clinical assessment. Clinician agreement with AI priority flags was 87%. AI prioritisation performance was slightly better for patients with darker skin tones. More than half of participants required assistance to capture or submit wound images. Patient and staff acceptability of AI was largely favourable. Adverse and serious adverse event rates were similar across both groups. The proportion of patients accessing NHS resources for wound-related problems and antibiotics was lower in the Isla-AI group. CONCLUSIONS: These findings support progression to a large, definitive multi-centre effectiveness trial, with further attention to equity, usability, and workflow integration. TRIAL REGISTRATION: IRAS 338141; local project UHDB/2022/024. ISRCTN16900119 CLINICALTRIALS.GOV: NCT06475703 Date registered: 20/06/2024.

Authors

Institutions

Publication Details

Journal
Journal of Hospital Infection
Published
2026-05-01
DOI
https://doi.org/10.1016/j.jhin.2026.04.020
Primary Topic
Surgical site infection prevention
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AI-enabled digital wound monitoring after cardiac surgery: a randomised controlled feasibility, safety and acceptability trial

Rosalie Magboo, F. Oezalp, Jacqueline Beckhelling, Melissa Rochon et al.
Journal of Hospital Infection
Surgical site infection prevention
article

AI-enabled digital wound monitoring after cardiac surgery: a randomised controlled feasibility, safety and acceptability trial

Rosalie Magboo, F. Oezalp, Jacqueline Beckhelling, Melissa Rochon, Karen Cariaga, James Jurkiewicz, Keith Wilson, Roy Harris, Mr Alex Shipolini, Daniel Davis, Luxmi Dhoonmoon, Janet Bouttell, Sarah Bolton, Victoria Chester, Judith Tanner
article en

Abstract

BACKGROUND: Surgical site infection after cardiac surgery is a common cause of morbidity and unplanned healthcare use, with most infections developing after hospital discharge. Remote wound monitoring using smartphone technology and artificial intelligence (AI) may support earlier identification of complications. AIM: To evaluate the feasibility, acceptability, and safety of an AI-enabled digital wound monitoring platform plus usual care (Isla-AI) compared with usual care (UC) alone. DESIGN, SETTING, AND PARTICIPANTS: This multi-centre, two-arm randomised controlled feasibility trial was conducted at two UK hospitals between August 2024 and January 2025. Adults undergoing cardiac surgery were randomised to receive Isla-AI or UC. The study was not powered to assess effectiveness. RESULTS: 120 patients were randomised and participated (Isla-AI n=62; UC n=58). Feasibility targets were exceeded: 60% of eligible patients approached consented, 95% of Isla-AI participants submitted at least one image, and 92% completed the study. 98% of images were suitable for clinical assessment. Clinician agreement with AI priority flags was 87%. AI prioritisation performance was slightly better for patients with darker skin tones. More than half of participants required assistance to capture or submit wound images. Patient and staff acceptability of AI was largely favourable. Adverse and serious adverse event rates were similar across both groups. The proportion of patients accessing NHS resources for wound-related problems and antibiotics was lower in the Isla-AI group. CONCLUSIONS: These findings support progression to a large, definitive multi-centre effectiveness trial, with further attention to equity, usability, and workflow integration. TRIAL REGISTRATION: IRAS 338141; local project UHDB/2022/024. ISRCTN16900119 CLINICALTRIALS.GOV: NCT06475703 Date registered: 20/06/2024.

Journal of Hospital Infection
Nottingham University Hospitals NHS Trust (GB), University of Nottingham (GB), University of Liverpool (GB), Guy's and St Thomas' NHS Foundation Trust (GB), University of Derby (GB), Barts Health NHS Trust (GB), Liverpool Heart and Chest Hospital NHS Trust (GB), Newcastle upon Tyne Hospitals NHS Foundation Trust (GB), Liverpool Heart and Chest Hospital (GB), Centre for Innovation in Regulatory Science (GB), Derby College (GB), Nottingham Biomedical Research Centre (GB), Office of Diversity and Inclusion (US)
National Institute for Health and Care Research
Openalex Percentile: Top 12%
Surgical site infection prevention
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.