A composite of four clinical photographs demonstrating the progression and classification of burn wound severity. Image A illustrates a first-degree (superficial) burn, showing blanching erythema of the skin with areas of peeling epidermis. Image B depicts a superficial second-degree (partial-thickness) burn on an extremity, characterized by large, tense, fluid-filled bullae with translucent amber-colored serous fluid and surrounding erythematous inflammation. Image C shows a deep second-degree burn on the hand, featuring mottled red and white tissue with ruptured blisters and areas of exposed dermis. Image D represents a third-degree (full-thickness) burn, displaying a dry, leathery, and inelastic eschar with a variegated color palette of white, grey, and brown, indicating destruction of the epidermis, dermis, and subcutaneous tissue. This comparison chart serves as a diagnostic educational tool for identifying burn depth based on visual morphological features such as blistering, tissue color, and texture.

This clinical series demonstrates the progression of burn severity categorized by depth and tissue involvement. (a) First-degree burn (superficial): Presents as erythema of the epidermis without blistering, consistent with a sunburn. (b) Second-degree superficial partial-thickness burn (SPTB): Displays clear blistering (bullae) and moist, red tissue, indicating damage to the papillary dermis. (c) Second-degree deep partial-thickness burn (DPTB): Shows a mottled appearance with ruptured blisters and more significant exudate, extending into the reticular dermis. (d) Third-degree burn (full-thickness): Characterized by a central area of leathery, dark eschar formation surrounded by a zone of stasis, indicating total destruction of the epidermis and dermis into the subcutaneous fat. (e) Fourth-degree burn: Illustrates the most severe injury, extending through all skin layers to involve underlying fascia, skeletal muscle (rectus abdominis), and potentially bone, appearing charred and necrotic. This visual comparison serves as an educational tool for clinical triage and the classification of thermal injuries.

Clinical photograph showing a mixed-depth thermal contact burn on the right perianal region. The lesion is vertically oriented and exhibits features consistent with second- and third-degree burns. The superior aspect of the wound presents as a bright red, moist, well-demarcated area of deep partial-thickness injury (second-degree), likely indicating exposed dermis. Moving inferiorly, the wound transitions into a full-thickness injury (third-degree) characterized by a tan, leathery appearance and a dark brown, opaque eschar at the lower pole. The surrounding skin displays significant blanching erythema and inflammation, with clear margins between the viable tissue and the necrotic burn site. The clinical context involves a paraplegic patient with sensory deficits, highlighting the risk of thermal injury from external sources like car seat heaters. This image serves as an educational example of thermal trauma morphology, burn staging, and the importance of pressure and temperature awareness in neurologically impaired patients.

This clinical dataset collage demonstrates the application of deep learning for burn wound segmentation. The image is structured in a 6x5 grid. The first column displays clinical photographs of various burn injuries on human limbs, showing a spectrum of wound morphology: superficial redness, blistering, deep partial-thickness wounds with white/pink slough, and full-thickness burns characterized by charred eschar and exposed subcutaneous tissue. The second and third columns present 'Ground truth mask' and 'Predicted mask' as binary blue-on-black segmentations, representing expert labels versus AI-generated area detections. The fourth and fifth columns, 'Ground truth visualization' and 'Predicted visualization,' show these masks overlaid as semi-transparent blue filters on the original clinical images to illustrate the accuracy of the automated segmentation. The examples highlight the challenges in defining burn boundaries, especially in cases with transitional zones between burned and healthy skin. This material is designed for medical informatics and dermatology education, focusing on automated diagnostic imaging and the assessment of burn surface area and depth.

This infographic depicts the user interface of a virtual medical simulation for burn care assessment. The central focus is a 3D-rendered male virtual patient lying on a cot, exhibiting visible erythematous skin changes consistent with thermal injury. The interface features several educational and diagnostic tools: 1) a 'Patient Diagram' for marking burned regions, 2) a 'Burn Chart' for recording data, 3) navigation controls ('Move to Patient Region' and 'Move Along Patient Region') to examine the body in detail, and 4) 'Manipulate Patient' icons to change body positioning for better visualization. Textual instructions at the top guide the user through the clinical workflow: identifying burn type and location, marking regions on a diagram, and calculating Total Body Surface Area (TBSA). The tool is designed for medical training to improve clinical accuracy in burn staging and surface area estimation using standard methodologies like the Lund-Browder chart. The background mimics a clinical environment with stocked medical supplies.

This composite of clinical photographs illustrates various burn injuries and skin lesions across multiple anatomical regions, paired with corresponding segmentation masks (B1-B5) used for Total Body Surface Area (TBSA) estimation. A1 displays a supinated left hand and palm used as an anatomical reference for sizing. A2 focuses on the abdomen, showing a large, irregularly shaped partial-thickness burn characterized by a pink-to-red erythematous base with areas of central pallor, suggesting varying burn depths. A3, A4, and A5 depict injuries on the left thigh, right leg/knee, and left lower leg, respectively. These areas exhibit extensive post-inflammatory hyperpigmentation, mottled tan-to-brown discoloration, and varying textures ranging from smooth to glossy or dry, characteristic of healing burn wounds or hypertrophic scarring. The series demonstrates the application of machine learning (Mask R-CNN) in dermatology and burn surgery to accurately delineate wound boundaries (red dashed lines in row B) for objective clinical assessment and treatment planning.

This clinical photograph displays a large, mixed-depth friction burn on the anterior abdomen of a patient following a motor vehicle accident. The injury covers a significant Total Burn Surface Area (TBSA), estimated at 13%. The wound exhibits heterogeneous characteristics: peripheral regions show intense erythema and mottled pink-to-red discoloration consistent with partial-thickness (second-degree) burns. Central and dependent areas of the wound demonstrate darker, grayish-black eschar and leathery texture, indicating full-thickness (third-degree) tissue damage covering approximately 4% of the TBSA. The burn follows a circular pattern around the umbilicus, which remains relatively spared. Visual signs of mechanical friction, such as linear abrasions and uneven texture, are evident throughout the injured area. This image serves as an educational example of high-energy friction trauma, illustrating the variable depths of thermal and mechanical skin injury and the clinical indicators used to differentiate between partial and full-thickness burns in a trauma setting.