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Automated Noninvasive Burn Diagnostic System for Health Care Using Artificial Intelligence: AMBUSH-AI
Mohamed El Masry1,2, Md Masudur Rahman3,4, Surya C Gnyawali1,2
1Department of Surgery, School of Medicine, University of Pittsburgh, Pittsburgh, PA.
Objective:
Develop technology to predict burn wound depth using a combination of FDA-approved ultrasound modalities and interpretation of these images using artificial intelligence (AI).
Summary Background Data:
Physical examination by burn surgeons is the diagnostic gold standard to determine the need for burn surgery. Distinguishing between deep partial and third-degree burns to determine the need for surgery is the ultimate diagnostic challenge. Reported accuracy for this process is 76% for burn experts and 50% for nonexperts.
Methods:
A pig burn model (n=12) was used to develop the initial AI framework, which was subsequently tested in a nonrandomized prospective study of thermal burn human subjects (n=30). Images from Tissue Doppler Elastography Imaging (TDI), to measure tissue stiffness, Harmonic B-mode ultrasound, to identify anatomic landmarks, and digital photographs were collected. Biopsies were obtained from 5 subjects who went to the OR for debridement as ground truth for AI image interpretation. The AI model analyzed both TDI and B-mode images to predict burn depth. AI accuracy and explainability in predicting burn depth were the main outcomes.
Results:
The AI algorithm identified third-degree burns in pigs with 100% accuracy. For human subjects the mean age was 47.6±17.6 years old and TBSA is 7.7%±8.5%. The AI method achieved a 95% accuracy in identifying 3rd-degree burns in humans.
Conclusions:
These results indicate that the strategy to use AI interpretation of B-mode ultrasound and TDI images to increase diagnostic accuracy in predicting burn depth is feasible.
