Improving burn depth assessment for pediatric scalds by AI based on semantic segmentation of polarized light photography images

Progress in the application of artificial intelligence in skin wound assessment and prediction of healing time

Since the 1970s, artificial intelligence (AI) has played an increasingly pivotal role in the medical field, enhancing the efficiency of disease diagnosis and treatment. Amidst an aging population and the proliferation of chronic disease, the prevalence of complex surgeries for high-risk multimorbid patients and hard-to-heal wounds has escalated. Healthcare professionals face the challenge of delivering safe and effective care to all patients concurrently. Inadequate management of skin wounds exacerbates the risk of infection and complications, which can obstruct the healing process and diminish patients' quality of life. AI shows substantial promise in revolutionizing wound care and management, thus enhancing the treatment of hospitalized patients and enabling healthcare workers to allocate their time more effectively. This review details the advancements in applying AI for skin wound assessment and the prediction of healing timelines. It emphasizes the use of diverse algorithms to automate and streamline the measurement, classification, and identification of chronic wound healing stages, and to predict wound healing times. Moreover, the review addresses existing limitations and explores future directions.

Artificial intelligence in wound care: diagnosis, assessment and treatment of hard-to-heal wounds: a narrative review

OBJECTIVE

The effective assessment of wounds, both acute and hard-to-heal, is an important component in the delivery by wound care practitioners of efficacious wound care for patients. Improved wound diagnosis, optimising wound treatment regimens, and enhanced prevention of wounds aid in providing patients with a better quality of life (QoL). There is significant potential for the use of artificial intelligence (AI) in health-related areas such as wound care. However, AI-based systems remain to be developed to a point where they can be used clinically to deliver high-quality wound care. We have carried out a narrative review of the development and use of AI in the diagnosis, assessment and treatment of hard-to-heal wounds. We retrieved 145 articles from several online databases and other online resources, and 81 of them were included in this narrative review. Our review shows that AI application in wound care offers benefits in the assessment/diagnosis, monitoring and treatment of acute and hard-to-heal wounds. As well as offering patients the potential of improved QoL, AI may also enable better use of healthcare resources.

Chinese expert consensus on the Management of Pediatric Deep Partial-Thickness Burn Wounds (2023 edition)

Burns are a main cause of accidental injuries among children in China. Because of the unique wound repair capacity and demand for growth in pediatric patients, the management of pediatric deep partial-thickness burn wounds involves a broader range of treatment options and controversy. We assembled experts from relevant fields in China to reach a consensus on the key points of thermal-induced pediatric deep partial-thickness burn-wound management, including definition and diagnosis, surgical treatments, nonsurgical treatment, choice of wound dressings, growth factor applications, infectious wound treatment, scar prevention and treatment. The committee members hope that the Expert Consensus will provide help and guiding recommendations for the treatment of pediatric deep partial-thickness burn wounds.

A Deep Learning Image-to-Image Translation Approach for a More Accessible Estimator of the Healing Time of Burns

OBJECTIVE

An accurate and timely diagnosis of burn severity is critical to ensure a positive outcome. Laser Doppler imaging (LDI) has become a very useful tool for this task. It measures the perfusion of the burn and estimates its potential healing time. LDIs generate a 6-color palette image, with each color representing a healing time. This technique has very high costs associated. In resource-limited areas, such as low- and middle-income countries or remote locations like space, where access to specialized burn care is inadequate, more affordable and portable tools are required. This study proposes a novel image-to-image translation approach to estimate burn healing times, using a digital image to approximate the LDI.

METHODS

This approach consists of a U-net architecture with a VGG-based encoder and applies the concept of ordinal classification. Paired digital and LDI images of burns were collected. The performance was evaluated with 10-fold cross-validation, mean absolute error (MAE), and color distribution differences between the ground truth and the estimated LDI.

RESULTS

Results showed a satisfactory performance in terms of low MAE ( 0.2370 ±0.0086). However, the unbalanced distribution of colors in the data affects this performance.

SIGNIFICANCE

This novel and unique approach serves as a basis for developing more accessible support tools in the burn care environment in resource-limited areas.

GL-FusionNet: Fusing global and local features to classify deep and superficial partial thickness burn

Burns constitute one of the most common injuries in the world, and they can be very painful for the patient. Especially in the judgment of superficial partial thickness burns and deep partial thickness burns, many inexperienced clinicians are easily confused. Therefore, in order to make burn depth classification automated as well as accurate, we have introduced the deep learning method. This methodology uses a U-Net to segment burn wounds. On this basis, a new thickness burn classification model that fuses global and local features (GL-FusionNet) is proposed. For the thickness burn classification model, we use a ResNet50 to extract local features, use a ResNet101 to extract global features, and finally implement the add method to perform feature fusion and obtain the deep partial or superficial partial thickness burn classification results. Burns images are collected clinically, and they are segmented and labeled by professional physicians. Among the segmentation methods, the U-Net used achieved a Dice score of 85.352 and IoU score of 83.916, which are the best results among all of the comparative experiments. In the classification model, different existing classification networks are mainly used, as well as a fusion strategy and feature extraction method that are adjusted to conduct experiments; the proposed fusion network model also achieved the best results. Our method yielded the following: accuracy of 93.523, recall of 93.67, precision of 93.51, and F1-score of 93.513. In addition, the proposed method can quickly complete the auxiliary diagnosis of the wound in the clinic, which can greatly improve the efficiency of the initial diagnosis of burns and the nursing care of clinical medical staff.

The Role of the Immune System in Pediatric Burns: A Systematic Review

Burns are one of the most common causes of home injuries, characterized by serious damage to the skin and causing the death of affected tissues. In this review, we intended to collect information on the pathophysiological effects of burns in pediatric patients, with particular emphasis on local and systemic responses. A total of 92 articles were included in the review, and the time range of the searched articles was from 2000 to 2021. The occurrence of thermal injuries is a problem that requires special attention in pediatric patients who are still developing. Their exposure to various burns may cause disturbances in the immune response, not only in the area of tissue damage itself but also by disrupting the systemic immune response. The aspect of immunological mechanisms in burns requires further research, and in particular, it is important to focus on younger patients as the existence of subtle differences in wound healing between adults and children may significantly influence the treatment of pediatric patients.