A Framework for Advancing Burn Assessment With Artificial Intelligence.

Area of Science:

• Medical Imaging
• Artificial Intelligence in Healthcare
• Computational Pathology

Background:

• Burn depth assessment is crucial for treatment but traditionally subjective.
• Traditional methods lead to variability and delayed decision-making in burn care.
• Artificial Intelligence (AI) presents an opportunity to enhance diagnostic accuracy and standardization.

Purpose of the Study:

• To evaluate the diagnostic performance of an AI model for burn depth assessment.
• To compare AI model outputs against a gold standard for burn type and depth diagnosis.
• To explore AI's potential in standardizing burn assessment.

Main Methods:

• Analysis of 29 burn patients' digital images.
• AI model fine-tuning with augmented data (1,200 images) using rotation, flipping, and brightness adjustment.
• Utilized Generative Adversarial Networks (GANs) for style transfer and zero-shot segmentation with foundation models.

Main Results:

• The AI model achieved 79% accuracy in classifying three burn depth categories.
• Data augmentation and style transfer enhanced model robustness and simulated realistic burn variations.
• Zero-shot segmentation effectively localized burn regions without task-specific training.

Conclusions:

• AI demonstrates significant potential for improving burn depth classification and segmentation.
• AI-driven models can enhance diagnostic accuracy, efficiency, and scalability in burn care.
• Automated and standardized burn assessment using AI can improve patient outcomes in diverse settings.