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 expert clinician judgment. Using a porcine model of tangential burn excision, we investigated the effectiveness of an intraoperative multispectral imaging device combined with artificial 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 abilities 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.

Clinical Investigation of a Rapid Non-invasive Multispectral Imaging Device Utilizing an Artificial Intelligence Algorithm for Improved Burn Assessment.

Currently, the incorrect judgment of burn depth remains common even among experienced surgeons. Contributing to this problem are change in burn appearance throughout the first week requiring periodic evaluation until a confident diagnosis can be made. To overcome these issues, we investigated the feasibility of an artificial intelligence algorithm trained with multispectral images of burn injuries to predict burn depth rapidly and accurately, including burns of indeterminate depth. In a feasibility study, 406 multispectral images of burns were collected within 72 hours of injury and then serially for up to 7 days. Simultaneously, the subject's clinician indicated whether the burn was of indeterminate depth. The final depth of burned regions within images were agreed upon by a panel of burn practitioners using biopsies and 21-day healing assessments as reference standards. We compared three convolutional neural network architectures and an ensemble in their capability to automatically highlight areas of nonhealing burn regions within images. The top algorithm was the ensemble with 81% sensitivity, 100% specificity, and 97% positive predictive value (PPV). Its sensitivity and PPV were found to increase in a sigmoid shape during the first week postburn, with the inflection point at day 2.5. Additionally, when burns were labeled as indeterminate, the algorithm's sensitivity, specificity, PPV, and negative predictive value were: 70%, 100%, 97%, and 100%. These results suggest multispectral imaging combined with artificial intelligence is feasible for detecting nonhealing burn tissue and could play an important role in aiding the earlier diagnosis of indeterminate burns.

Assessment of a noninvasive optical photoplethysmography imaging device with dynamic tissue phantom models.

Noncontact photoplethysmography (PPG) has been studied as a method to provide low-cost, noninvasive, two-dimensional blood oxygenation measurements and medical imaging for a variety of near-surface pathologies. To evaluate this technology in a laboratory setting, dynamic tissue phantoms were developed with tunable parameters that mimic physiologic properties of the skin, including blood vessel volume change, pulse wave frequency, and tissue scattering and absorption. Tissue phantoms were generated using an elastic tubing to represent a blood vessel where the luminal volume could be modulated with a pulsatile fluid flow. The blood was mimicked with a scattering and absorbing motility standard, and the tissue with a gelatin-lipid emulsion hydrogel. A noncontact PPG imaging system was then evaluated using the phantoms. Noncontact PPG imaging accurately identified pulse frequency, and PPG signals from these phantoms suggest that the phantoms can be used to evaluate noncontact PPG imaging systems. Such information may be valuable to the development of future PPG imaging systems.

Multispectral and Photoplethysmography Optical Imaging Techniques Identify Important Tissue Characteristics in an Animal Model of Tangential Burn Excision.

Burn excision, a difficult technique owing to the training required to identify the extent and depth of injury, will benefit from a tool that can cue the surgeon as to where and how much to resect. We explored two rapid and noninvasive optical imaging techniques in their ability to identify burn tissue from the viable wound bed using an animal model of tangential burn excision. Photoplethysmography (PPG) imaging and multispectral imaging (MSI) were used to image the initial, intermediate, and final stages of burn excision of a deep partial-thickness burn. PPG imaging maps blood flow in the skin's microcirculation, and MSI collects the tissue reflectance spectrum in visible and infrared wavelengths of light to classify tissue based on a reference library. A porcine deep partial-thickness burn model was generated and serial tangential excision accomplished with an electric dermatome set to 1.0 mm depth. Excised eschar was stained with hematoxylin and eosin to determine the extent of burn remaining at each excision depth. We confirmed that the PPG imaging device showed significantly less blood flow where burn tissue was present, and the MSI method could delineate burn tissue in the wound bed from the viable wound bed. These results were confirmed independently by a histological analysis. We found these devices can identify the proper depth of excision, and their images could cue a surgeon as to the preparedness of the wound bed for grafting. These image outputs are expected to facilitate clinical judgment in the operating room.

Mesoporous silicon particles as a multistage delivery system for imaging and therapeutic applications.

Many nanosized particulate systems are being developed as intravascular carriers to increase the levels of therapeutic agents delivered to targets, with the fewest side effects. The surface of these carriers is often functionalized with biological recognition molecules for specific, targeted delivery. However, there are a series of biological barriers in the body that prevent these carriers from localizing at their targets at sufficiently high therapeutic concentrations. Here we show a multistage delivery system that can carry, release over time and deliver two types of nanoparticles into primary endothelial cells. The multistage delivery system is based on biodegradable and biocompatible mesoporous silicon particles that have well-controlled shapes, sizes and pores. The use of this system is envisioned to open new avenues for avoiding biological barriers and delivering more than one therapeutic agent to the target at a time, in a time-controlled fashion.