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.

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.

Background: Wounds with an avascular component represent a significant challenge in medical care due to impaired blood flow. Synthetic matrices, such as poly-lactic acid (PLA), have demonstrated promising results in promoting wound healing in complex wounds, including those with restricted blood supply, such as diabetic foot and venous leg ulcers. Objective: This case series presents the outcomes of five patients with wounds containing exposure of avascular components, of various etiologies successfully treated with PLA matrices. Case description: Five patients presented complex wounds involving exposure of bone, tendon, fascia, or osteosynthetic material. Wound bed preparation included debridement followed by PLA application covered with additional layers (non-adherent dressing, absorbent dressing, and compression bandage) as needed. Weekly assessments were conducted until full wound closure was achieved. Results: All cases showed successful outcomes, with PLA promoting granulation tissue formation and re-epithelialization, contributing to wound closure. One patient required skin grafts for complete healing. No local infections were reported before or after PLA application. Conclusions: PLA matrices are a practical and effective option for managing complex wounds, promoting tissue regeneration and optimizing wound bed quality for skin grafts or flaps. While these findings are promising, further studies are needed to confirm the broader applicability and efficacy of PLA in the management of wounds containing exposure of avascular structures.

Background: Scores and prediction models such as the mangled extremity severity score (MESS) for trauma and the Wound, Ischemia, and foot Infection (WIfI) classification for diabetic foot ulcers help in the decision-making process for amputation. However, these tools can be subjective as they depend on the experience of the medical staff applying them. Objective: This study aimed to assess the impact of temperature measurement using infrared thermal imaging in extremity salvage in patients at risk of limb amputation. Methods: We included 29 patients who sought a second opinion after an amputation recommendation. Infrared thermographic images were acquired to measure the temperature difference (ΔT) between the injured and uninjured limbs. For patients with salvaged limbs, we provided clinical follow-up for up to 12 weeks. Results: Of the 29 patients enrolled in the study, 27 limbs were salvaged. Thermographic imaging allowed the distinction into two groups: the first group of 18 patients with mean ΔT value of −3.6 °C (SD 1.99 °C) , and the second group of 9 patients with mean ΔT of 3.36 °C (SD 2.71 °C). None of the patients in either group showed progression in ΔT values within the first 5 days; at the twelfth week, ΔT values approached 0 °C at wound closure. Of the two patients who required amputation, one showed an initial ΔT of −4.3 °C, which worsened to −5 °C by the fifth day, and the other patient showed an initial ΔT of −4.5 °C, which worsened to −5.8 °C by the fifth day. Conclusions: Digital infrared thermography is a tool that may help guide limb salvage in patients with uncertain clinical diagnoses. This imaging modality allows visualization of thermal differences and patterns derived from thermal changes in patients at risk of limb amputation.