No abstract

No abstract

No abstract

Assessment of burn extent and depth are critical and require very specialized diagnosis. Automated image-based algorithms could assist in performing wound detection and classification. We aimed to develop two deep-learning algorithms that respectively identify burns, and classify whether they require surgery. An additional aim assessed the performances in different Fitzpatrick skin types. Annotated burn (n = 1105) and background (n = 536) images were collected. Using a commercially available platform for deep learning algorithms, two models were trained and validated on 70% of the images and tested on the remaining 30%. Accuracy was measured for each image using the percentage of wound area correctly identified and F1 scores for the wound identifier; and area under the receiver operating characteristic (AUC) curve, sensitivity, and specificity for the wound classifier. The wound identifier algorithm detected an average of 87.2% of the wound areas accurately in the test set. For the wound classifier algorithm, the AUC was 0.885. The wound identifier algorithm was more accurate in patients with darker skin types; the wound classifier was more accurate in patients with lighter skin types. To conclude, image-based algorithms can support the assessment of acute burns with relatively good accuracy although larger and different datasets are needed.

Over 95% of fire-related burns occur in low- and middle-income countries (LMICs), an important and frequently overlooked global health disparity, yet research is limited from LMICs on how survivors and their caregivers recover and successfully return to their pre-burn lives. This study examines the lived experiences of burn patients and caregivers, the most challenging aspects of their recoveries, and factors that have assisted in recovery. This qualitative study was conducted in KwaZulu-Natal, South Africa at a 900-bed district hospital. Participants (n = 35) included burn patients (n = 13) and caregivers (n = 22) after discharge. In-depth interviews addressed the recovery process after a burn injury. Data were coded using NVivo 12. Analysis revealed three major thematic categories. Coded data were triangulated to analyze caregiver and patient perspectives jointly. The participants’ lived experiences fell into three main categories: (1) psychological impacts of the burn, (2) enduring the transition into daily life, and (3) reflections on difficulties survivors face in returning for aftercare. The most notable discussions regarded stigma, difficulty accepting self-image, loss of relationships, returning to work, and barriers in receiving long-term aftercare at the hospital outpatient clinic. Patients and caregivers face significant adversities integrating into society. This study highlights areas in which burn survivors may benefit from assistance to inform future interventions and international health policy.