Vision-language foundation model-driven efficient recognition and home-based management of surgical incisions.

OBJECTIVE

Accurate assessment of surgical incision recovery is crucial for home-based care and rehabilitation of patients. This study aims to develop and evaluate a novel surgical incision recognition method driven by a vision-language foundation model (VLFM) to improve incision recognition accuracy and optimize home-based management.

METHODS

A total of 1008 surgical incision images from 865 postoperative patients between May 2022 and August 2023 from Center 1 were retrospectively included as the primary study cohort; 252 surgical incision images from 199 patients between September 2023 and December 2023 from Center 1 were included as the temporal validation cohort; and 183 surgical incision images from 130 patients between October 2023 and December 2023 from Center 2 were included as an independent external validation cohort. Seven categories of surgical incisions were defined and annotated using image processing software by wound care specialists. Our surgical incision recognition system (named DeepIncision) was developed based on Grounded Language-Image Pre-training (GLIP) VLFM. We compared the performance of DeepIncision with five traditional object detection deep learning methods and with non-medical personnel. The incision recognition performance was evaluated using average precision (AP), average recall (AR), F1-score, and the area under the receiver operator characteristic curve.

RESULTS

DeepIncision can efficiently recognize seven categories of surgical incisions: no abnormality, redness, suppuration, scab, tension blisters, ecchymosis around the incision, and dehiscence. The AP for the temporal validation cohort was 68.50% and that for the external validation cohort was 57.85%, both significantly outperforming other deep learning methods and non-expert manual recognition ( P < 0.01). Compared to the average performance of non-medical personnel (AP = 3.80%, AR = 15.30%) and non-wound specialist medical staff (AP = 41.00%, AR = 52.50%), DeepIncision (AP = 68.50%, AR = 96.91%) achieved absolute AP improvements of 64.7% and 27.5% and absolute AR improvements of 81.61% and 44.41%, respectively.

CONCLUSIONS

DeepIncision yields automatic and accurate detection and recognition of surgical incisions, assisting patients with home-based incision management and offering real-time feedback on incisions, which enhances patient self-management and promotes effective home care and rehabilitation of surgical incisions.