Deep Learning for Optimizing Traumatic Injuries and Wound Analysis in Forensic Medicine

This study investigates the use of deep learning (DL) to improve forensic wound analysis, with the goal of creating a taxonomy of DL architectures, evaluating their performance, addressing data scarcity, improving interpretability, and recommending standardized protocols for traumatic injury assessment. We analyze 60 recent studies to investigate DL models such as CNNs, RNNs, GANs, and Transformers for tasks such as gunshot wound classification (98% accuracy), stab wound segmentation (93% pixel accuracy), traumatic brain injury (TBI) detection (94% accuracy), and postmortem imaging analysis (88.42% AUC). Key findings show that DL improves precision, cuts analysis time by up to 50%, and reduces subjectivity in forensic pathology. We address challenges such as small datasets, inconsistent image quality, and ethical concerns with synthetic data generation, FPGA-optimized processing, and explainable AI techniques. The study's significance stems from its potential to transform forensic investigations by providing accurate, efficient, and legally admissible tools that aid in objective cause-of-death determinations and advance medicolegal results.