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92 Improved Burn Healing Classification via Artificial Intelligence


 
 
 Current methods of assessment for burn severity and healing potential are insufficient but this determination remains critical in providing optimal burn care. A novel device using multispectral imaging and artificial intelligence (AI) has shown prior success aiding in this process. Promising initial results of this proof-of-concept case series have been presented previously, invoking much interest from the burn care community. These results reflect the final results of this study and provide support for continued investigation with this device for the treatment of burn wounds.
 
 
 
 Subjects with thermal burns were enrolled within 72 hours of injury. The goal was to enroll at least 12 burn wounds from each category of severity: 1°; superficial 2°; deep 2°; and 3°. Upon enrollment burns were imaged with the study device serially for up to 7 days post-burn. The true healing potential of each imaged burn area was determined using either healing assessment at 21 days post-burn or multiple punch biopsies of the burn wound taken at time of excision and grafting. This ground truth was used to train the AI algorithm to automatically identify the margins of non-healing burn tissue within device images. Accuracy of the study device to identify and differentiate healing and non-healing burn tissue within the captured wound area was computed.
 
 
 
 Data from 38 subjects with 58 total burns and 393 wound images were obtained during this study. Following completion of training, AI predictions achieved 90.7% sensitivity with 87.5% specificity in predicting non-healing burn tissue using cross-validation. Results improved throughout the study as more wound images were used in AI training (Figure 1).
 
 
 
 The AI algorithm trained on 58 burn wounds (38 subjects) had an area under the curve of 0.955 and has continued to improve with the addition of more burn wounds. Expansion of this study has been initiated at multiple burn centers to continue collecting data for AI algorithm training.
 
 
 
 These results demonstrate feasibility of utilizing AI-assisted diagnosis in burn wounds and possible application in the management of burn patients.
 
