Burn care management includes assessing the severity of burns accurately, especially distinguishing superficial partial-thickness burns from deep partial-thickness burns, in the context of providing definitive, downstream treatment. Moreover, the healing of the wound in the subacute care setting requires continuous tracking to avoid complications. Artificial intelligence (AI) and computer vision (CV) provide a unique opportunity to build low-cost and accessible tools to classify burn severity and track changes in wound parameters, both in the clinic by physicians and nurses and asynchronously in the remote setting by the patient themselves. Wound assessments can be achieved by AI–CV using the principles of image-guided therapy using high-quality 2D color images. Wound parameters can include wound 2D spatial dimension and the characterization of wound color changes, which demonstrates physiological changes such as the presentation of eschar/necrotic tissue, pustulence, granulation tissue, and scabbing. Here we present the development of AI–CV-based Skin Abnormality Tracking Algorithm pipeline. Additionally, we provide the results on a single localized burn tracked for a 6-week period in the clinic and an additional 2-week period of home monitoring.

Appropriate identification of burn depth and size is paramount. Despite the development of burn depth assessment aids [eg, laser Doppler imaging (LDI)], clinical assessment, which assesses partial-thickness burn depth with 67% accuracy, currently remains the most consistent standard of practice. We sought to develop an image-based artificial intelligence system that predicts burn severity and wound margins for use as a triaging tool in thermal injury management. Modified EfficientNet architecture trained by 1684 mobile-device-captured images of different burn depths was previously used to create a convoluted neural network (CNN). The CNN was modified to a novel boundary attention mapping (BAM) algorithm using elements of saliency mapping, which was used to recognize the boundaries of burns. For validation, 144 patient charts that included clinical assessment, burn location, total body surface area, and LDI assessment were retrieved for a retrospective study. The clinical images underwent CNN-BAM assessment and were directly compared with the LDI assessment. CNN using a 4-level burn severity classification achieved an accuracy of 85% (micro/macro-averaged receiver operating characteristic scores). The CNN-BAM system can successfully highlight burns from surrounding tissue with high confidence. CNN-BAM burn area segmentations attained a 91.6% accuracy, 78.2% sensitivity, and 93.4% specificity, when compared to LDI methodology. Results comparing the CNN-BAM outputs to clinical and LDI assessments have shown a high degree of correlation between the CNN-BAM burn severity predictions to those extrapolated from LDI healing potential (66% agreement). CNN-BAM algorithm gives equivalent burn-depth detection accuracy as LDI with a more economical and accessible application when embedded in a mobile device.

Burn injuries, resulting from thermal, chemical, and electrical mechanisms, require prompt and accurate assessment for effective treatment. The primary method, relying on visual and tactile evaluations, offers 50%–80% accuracy, while noninvasive methods such as laser Doppler imaging (LDI) reach up to 97% accuracy. This article presents a machine learning (ML) pipeline for assessing burn severity and segmenting affected skin regions. We trained a convolutional neural network (CNN) to classify four burn severities: superficial (SPF), superficial partial thickness (SPT), deep partial thickness (DPT), and full thickness (FT). In addition, we introduced boundary attention mapping (BAM), a saliency mapping method that leverages the trained CNN to accurately segment burn regions. Our pipeline was validated using two datasets: a Burn Injury Image dataset with 1385 images and an LDI dataset with 184 images. The CNN achieved 80% accuracy, a 79.5% average F1-score, and 95% ROC in classifying burn severities. Comparing BAM with LDI, our method achieved 91.39% accuracy, 78.12% sensitivity, and 95.07% specificity in segmenting burn regions. These findings demonstrate the robustness of our AI model and its potential clinical application.

Purpose: Despite the advances of modern operating rooms, surgeons often experience work environments that rival those of industrial workers with regard to the risk of musculoskeletal (MSK) injuries or disorders. Such injuries may result in loss of hours, decreased surgical volume, or premature retirement. This study aimed to investigate the prevalence and impact of MSK injuries among Canadian plastic surgeons and trainees. Methods: A cross-sectional, online survey was disseminated among Canadian plastic surgeons, defined as those registered as members of the Canadian Society of Plastic Surgeons, the Royal College of Physicians and Surgeons of Canada, or Plastic Surgery Residents. Results: This survey was disseminated to 604 Canadian plastic surgeons, fellows, and residents, of whom 139 responded (response rate 23.0%). Of the responders, 49.6% were male, 23.0% were >35 years of age, and 46.1% had been in practice for >10 years. The majority (72.7%) of respondents endorsed experiencing MSK symptoms after operating. Moreover, 18.7% of respondents felt their MSK symptoms had direct consequences on their performance as a surgeon. When MSK symptoms were reported to department heads, system change was only seen 44.4% of the time. Unsurprisingly, neck (76.2%), back (72.2%), and shoulders (48.5%) were the areas of pain most reported. Exercise was not shown to significantly reduce the impact of MSK symptoms resulting from operating ( P = .06). Conclusions: Musculoskeletal symptoms are common among plastic surgeons and directly impact the performance of a large proportion of surgeons. Besides traditional efforts to reinforce good posture while operating, best practice policies and operating room optimization with regard to ergonomics are warranted.