Children with severe burns display no sex differences in exercise capacity at hospital discharge or adaptation after exercise rehabilitation training

Enhanced skin burn assessment through transfer learning: a novel framework for human tissue analysis

Visual inspection is the typical way for evaluating burns, due to the rising occurrence of burns globally, visual inspection may not be sufficient to detect skin burns because the severity of burns can vary and some burns may not be immediately apparent to the naked eye. Burns can have catastrophic and incapacitating effects and if they are not treated on time can cause scarring, organ failure, and even death. Burns are a prominent cause of considerable morbidity, but for a variety of reasons, traditional clinical approaches may struggle to effectively predict the severity of burn wounds at an early stage. Since computer-aided diagnosis is growing in popularity, our proposed study tackles the gap in artificial intelligence research, where machine learning has received a lot of attention but transfer learning has received less attention. In this paper, we describe a method that makes use of transfer learning to improve the performance of ML models, showcasing its usefulness in diverse applications. The transfer learning approach estimates the severity of skin burn damage using the image data of skin burns and uses the results to improve future methods. The DL technique consists of a basic CNN and seven distinct transfer learning model types. The photos are separated into those displaying first, second, and third-degree burns as well as those showing healthy skin using a fully connected feed-forward neural network. The results demonstrate that the accuracy of 93.87% for the basic CNN model which is significantly lower, with the VGG-16 model achieving the greatest accuracy at 97.43% and being followed by the DenseNet121 model at 96.66%. The proposed approach based on CNN and transfer learning techniques are tested on datasets from Kaggle 2022 and Maharashtra Institute of Technology open-school medical repository datasets that are clubbed together. The suggested CNN-based approach can assist healthcare professionals in promptly and precisely assessing burn damage, resulting in appropriate therapies and greatly minimising the detrimental effects of burn injuries.

Manufacture and clinical application of the forearm pronation's assistant tableware in the severely burned

INTRODUCTION

Even after reconstructive surgery, it is still difficult for patients with severe burns to achieve independent eating activity. In this project, we customized the forearm pronation's assistant tableware to assist in improvement with eating activities.

METHODS

From January 2017 to December 2018, 28 patients with severe burns including the hands were recruited. For the patient's independent eating activities, we customized forearm pronation's tableware (forks and spoons). We compared modified Barthel index (MBI) and Visual analogue scale (VAS) of satisfaction under three conditions: no auxiliary tableware, ADL universal cuff, or forearm pronation tableware; to compare the duration and the weight of food spilled during lunch when the patients wore the ADL universal cuff or the forearm pronation's tableware. Differences in MBI (rank data) were tested by the Friedman test, differences in VAS (normal distribution) were tested with One-way ANOVA (Bonferroni), differences in the duration and the weight (normal distribution data) were tested by paired sample t test.

RESULTS

After wearing the forearm pronation's assistant tableware, MBI VAS both increased more than when the patients did not wear the auxiliary tableware (all p＜0.05). When the subjects wore forearm pronation tableware, the duration of lunch significantly decreased and the quality of eating activity significantly improved compared to the ADL universal cuff in eating activity (all p＜0.05).

CONCLUSION

After wearing the forearm pronation's assistant tableware, the patients with severe burns completely or almost completely accomplished independent eating, the duration was decreased, and during eating activity the quality and the satisfaction were improved.

CLINICAL TRIAL REGISTRATION

Chinese Clinical trial registry, ChiCTR1800019963.

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

While visual assessment is the standard technique for burn evaluation, computer-aided diagnosis is increasingly sought due to high number of incidences globally. Patients are increasingly facing challenges which are not limited to shortage of experienced clinicians, lack of accessibility to healthcare facilities and high diagnostic cost. Certain number of studies were proposed in discriminating burn and healthy skin using machine learning leaving a huge and important gap unaddressed; whether burns and related skin injuries can be effectively discriminated using machine learning techniques. Therefore, we specifically use transfer learning by leveraging pre-trained deep learning models due to deficient dataset in this paper, to discriminate two classes of skin injuries—burnt skin and injured skin. Experiments were extensively conducted using three state-of-the-art pre-trained deep learning models that includes ResNet50, ResNet101 and ResNet152 for image patterns extraction via two transfer learning strategies—fine-tuning approach where dense and classification layers were modified and trained with features extracted by base layers and in the second approach support vector machine (SVM) was used to replace top-layers of the pre-trained models, trained using off-the-shelf features from the base layers. Our proposed approach records near perfect classification accuracy in categorizing burnt skin ad injured skin of approximately 99.9%.

Predictability of exercise capacity following pediatric burns: a preliminary investigation

PURPOSE

Describe the course of exercise capacity in pediatric burn patients during the initial 6 months after hospital discharge, and examine whether its recovery can be predicted from burn characteristics, sociodemographic characteristics, and/or prior assessment.

MATERIALS AND METHODS

Exercise capacity was assessed at discharge, and 6 weeks, 3 months, and 6 months after discharge using the Steep Ramp Test (SRT).

RESULTS

Twenty-four pediatric patients with burns affecting 0.1-34% of total body surface area were included. At group level, exercise capacity was low at discharge and did not reach healthy reference values within 6 months, despite significant improvement over time. At individual level, the course of exercise capacity varied widely. Six months after discharge, 48% of participants scored more than one standard deviation below healthy age- and sex-specific reference values. SRT outcomes at 6 weeks and 3 months were the best predictors of exercise capacity 6 months after discharge, explaining, respectively, 76% and 93% of variance.

CONCLUSIONS

Forty-eight percent of participants did not achieve healthy reference values of exercise capacity and were therefore considered "at risk" for diminished functioning. Our preliminary conclusion that early assessment of exercise capacity with the SRT can timely identify those patients, needs to be strengthened by further research.IMPLICATIONS FOR REHABILITATIONPediatric burns can be considered as a chronic medical condition because of the lifelong consequences.Exercise capacity is reduced following- even minor -pediatric burns.Recovery patterns vary widely: some pediatric burn patients achieve healthy levels of exercise capacity without specific intervention, while others do not.The Steep Ramp Test can be used to assess exercise capacity, identifying those "at risk" for adverse outcomes at an early stage.Patients "at risk" should be encouraged to play sports and adopt an active lifestyle.