Precision diagnosis of burn injuries using imaging and predictive modeling for clinical applications

Burns represents a serious clinical problem because the diagnosis and assessment are very complex. This paper proposes a methodology that combines the use of advanced medical imaging with predictive modeling for the improvement of burn injury assessment. The proposed framework makes use of the Adaptive Complex Independent Components Analysis (ACICA) and Reference Region (TBSA) methods in conjunction with deep learning techniques for the precise estimation of burn depth and Total Body Surface Area analysis. It also allows for the estimation of the depth of burns with high accuracy, calculation of TBSA, and non-invasive analysis with 96.7% accuracy using an RNN model. Extensive experimentation on DCE-LUV samples validates enhanced diagnostic precision and detailed texture analysis. These technologies provide nuanced insights into burn severity, improving diagnostic accuracy and treatment planning. Our results demonstrate the potential of these methods to revolutionize burn care and optimize patient outcomes.