Background: Composition of tissue types within a wound is a useful indicator of its healing progression. Tissue composition is clinically used in wound healing tools (eg, Bates-Jensen Wound Assessment Tool) to assess risk and recommend treatment. However, wound tissue identification and the estimation of their relative composition is highly subjective. Consequently, incorrect assessments could be reported, leading to downstream impacts including inappropriate dressing selection, failure to identify wounds at risk of not healing, or failure to make appropriate referrals to specialists.Objective: This study aimed to measure inter- and intrarater variability in manual tissue segmentation and quantification among a cohort of wound care clinicians and determine if an objective assessment of tissue types (ie, size and amount) can be achieved using deep neural networks.Methods: A data set of 58 anonymized wound images of various types of chronic wounds from Swift Medical’s Wound Database was used to conduct the inter- and intrarater agreement study. The data set was split into 3 subsets with 50% overlap between subsets to measure intrarater agreement. In this study, 4 different tissue types (epithelial, granulation, slough, and eschar) within the wound bed were independently labeled by the 5 wound clinicians at 1-week intervals using a browser-based image annotation tool. In addition, 2 deep convolutional neural network architectures were developed for wound segmentation and tissue segmentation and were used in sequence in the workflow. These models were trained using 465,187 and 17,000 image-label pairs, respectively. This is the largest and most diverse reported data set used for training deep learning models for wound and wound tissue segmentation. The resulting models offer robust performance in diverse imaging conditions, are unbiased toward skin tones, and could execute in near real time on mobile devices.Results: A poor to moderate interrater agreement in identifying tissue types in chronic wound images was reported. A very poor Krippendorff α value of .014 for interrater variability when identifying epithelization was observed, whereas granulation was most consistently identified by the clinicians. The intrarater intraclass correlation (3,1), however, indicates that raters were relatively consistent when labeling the same image multiple times over a period. Our deep learning models achieved a mean intersection over union of 0.8644 and 0.7192 for wound and tissue segmentation, respectively. A cohort of wound clinicians, by consensus, rated 91% (53/58) of the tissue segmentation results to be between fair and good in terms of tissue identification and segmentation quality.Conclusions: The interrater agreement study validates that clinicians exhibit considerable variability when identifying and visually estimating wound tissue proportion. The proposed deep learning technique provides objective tissue identification and measurements to assist clinicians in documenting the wound more accurately and could have a significant impact on wound care when deployed at scale.

Objectives To compare teledermatology and face-to-face (F2F) agreement in primary diagnoses of dermatological conditions. Design Systematic review and meta-analysis Methods MEDLINE, Embase, Cochrane Library (Wiley), CINAHL and medRxiv were searched between January 2010 and May 2022. Observational studies and randomised clinical trials that reported percentage agreement or kappa concordance for primary diagnoses between teledermatology and F2F physicians were included. Titles, abstracts and full-text articles were screened in duplicate. From 7173 citations, 44 articles were included. A random-effects meta-analysis was conducted to estimate pooled estimates. Primary outcome measures were mean percentage and kappa concordance for assessing diagnostic matches between teledermatology and F2F physicians. Secondary outcome measures included the agreement between teledermatologists, F2F dermatologists, and teledermatology and histopathology results. Results 44 studies were extracted and reviewed. The pooled agreement rate was 68.9%, and kappa concordance was 0.67. When dermatologists conducted F2F and teledermatology consults, the overall diagnostic agreement was significantly higher at 71% compared with 44% for non-specialists. Kappa concordance was 0.69 for teledermatologist versus specialist and 0.52 for non-specialists. Higher diagnostic agreements were also noted with image acquisition training and digital photography. The agreement rate was 76.4% between teledermatologists, 82.4% between F2F physicians and 55.7% between teledermatology and histopathology. Conclusions and relevance Teledermatology can be an attractive option particularly in resource-poor settings. Future efforts should be placed on incorporating image acquisition training and access to high-quality imaging technologies. Trial registration number 10.17605/OSF.IO/FJDVG

The clinical evaluation of a burn wound alone may not be adequate to predict the severity of the injury nor to guide clinical decision making. Infrared thermography provides information about soft tissue viability and has previously been used to assess burn depth. The objective of this study was to determine if temperature differences in burns assessed by infrared thermography could be used predict the treatment modality of either healing by re-epithelization, requiring skin grafts, or requiring amputations, and to validate the clinical predication algorithm in an independent cohort. Temperature difference (ΔT) between injured and healthy skin were recorded within the first three days after injury in previously healthy burn patients. After discharge, the treatment modality was categorized as re-epithelization, skin graft or amputation. Potential confounding factors were assessed through multiple linear regression models, and a prediction algorithm based on the ΔT was developed using a predictive model using a recursive partitioning Random Forest machine learning algorithm. Finally, the prediction accuracy of the algorithm was compared in the development cohort and an independent validation cohort. Significant differences were found in the ΔT between treatment modality groups. The developed algorithm correctly predicts into which treatment category the patient will fall with 85.35% accuracy. Agreement between predicted and actual treatment for both cohorts was weighted kappa 90%. Infrared thermograms obtained at first contact with a wounded patient can be used to accurately predict the definitive treatment modality for burn patients. This method can be used to rationalize treatment and streamline early wound closure.

Introduction: Despite being widely used as a screening tool, a rigorous scientific evaluation of infrared thermography for the diagnosis of minimally symptomatic patients suspected of having COVID‐19 infection has not been performed.Methods: A consecutive sample of 60 adult individuals with a history of close contact with COVID‐19 infected individuals and mild respiratory symptoms for less than 7 days and 20 confirmed COVID‐19 negative healthy volunteers were enrolled in the study. Infrared thermograms of the face were obtained with a mobile camera, and RT‐PCR was used as the reference standard test to diagnose COVID‐19 infection. Temperature values and distribution of the face of healthy volunteers and patients with and without COVID‐19 infection were then compared.Results: Thirty‐four patients had an RT‐PCR confirmed diagnosis of COVID‐19 and 26 had negative test results. The temperature asymmetry between the lacrimal caruncles and the forehead was significantly higher in COVID‐19 positive individuals. Through a random forest analysis, a cut‐off value of 0.55°C was found to discriminate with an 82% accuracy between patients with and without COVID‐19 confirmed infection.Conclusions: Among adults with a history of COVID‐19 exposure and mild respiratory symptoms, a temperature asymmetry of ≥ 0.55°C between the lacrimal caruncle and the forehead is highly suggestive of COVID‐19 infection. This finding questions the widespread use of the measurement of absolute temperature values of the forehead as a COVID‐19 screening tool.