Artificial Intelligence-Enhanced Remote Monitoring After Cardiac Surgery: A Prospective Single-Centre Feasibility Study with Retrospective Wound Image Classification

Background/Objectives: Unplanned readmission within 30 days affects 12–20% of patients after cardiac surgery, largely through complications developing at home. We evaluated the feasibility, acceptability, and adherence of a 30-day post-discharge remote monitoring programme. In a separate retrospective analysis that did not inform patient care, we also assessed how accurately an investigational AI-based classifier triages the programme’s wound photographs. Methods: In this prospective single-centre study, 76 of 85 screened patients were monitored for 30 days with twice-daily blood pressure and pulse, daily weight, electrocardiography (ECG), and alternate-day wound photographs, all reviewed daily by a study clinician. Retrospectively, 735 photographs of 88 wounds from 71 patients, labelled by two blinded clinicians, were used to train an ensemble of five convolutional neural networks, tested on a patient-disjoint test set of 147 images from 15 patients. Results: All 76 eligible patients enrolled and 75 (99%) completed the programme, with 82% adherence to the core measurements and an acceptability score of 6.3 (SD 0.5) of 7. Daily review produced 494 teleconsultations, 10 emergency referrals, and 6 admissions (7.9%), and atrial fibrillation was confirmed in 18 episodes in 11 patients. Agreement between the two wound labelling clinicians was κ 0.89. On the test set, the classifier achieved an area under the precision–recall curve of 0.747 (95% CI 0.402–1.000), image-level sensitivity 73.3% (11/15; 95% CI 33.3–100%), precision 73.3%, and wound-level sensitivity 5/6; the one confirmed wound complication in the test set was flagged. Conclusions: Intensive remote monitoring with daily clinician review was feasible after cardiac surgery and was sustained by patients. Daily review identified arrhythmias and wound problems, most of which were managed remotely or in the outpatient clinic. The classifier is a proof of concept for automated triage of such photographs by reproducing clinicians’ visual judgment; given the wide confidence intervals, larger multicentre validation is needed.