Feasibility of intra-operative image guidance in burn excision surgery with multispectral imaging and deep learning

Background: Exposing a healthy wound bed for skin grafting is an important step during burn surgery to ensure graft take and maintain good functional outcomes. Currently, the removal of non-viable tissue in the burn wound bed during excision is determined by ex-pert clinician judgment. Using a porcine model of tangential burn excision, we investigated the effectiveness of an intraoperative multispectral imaging device combined with artifi-cial intelligence to aid clinician judgment for the excision of non-viable tissue. Methods: Multispectral imaging data was obtained from serial tangential excisions of thermal burn injuries and used to train a deep learning algorithm to identify the presence and location of non-viable tissue in the wound bed. Following algorithm development, we studied the ability of two surgeons to estimate wound bed viability, both unaided and aided by the imaging device. Results: The deep learning algorithm was 87% accurate in identifying the viability of a burn wound bed. When paired with the surgeons, this device significantly improved their abil-ities to determine the viability of the wound bed by 25% (p = 0.03). Each time a surgeon changed their decision after seeing the AI model output, it was always a change from an incorrect decision to excise more tissue to a correct decision to stop excision.Conclusion: This study provides insight into the feasibility of image-guided burn excision, its effect on surgeon decision making, and suggests further investigation of a real-time imaging system for burn surgery could reduce over-excision of burn wounds.(c) 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).