Machine learning in burn care and research: A systematic review of the literature

Abstract

BackgroundTo date, there are no reviews on machine learning (ML) in burn care. Considering the growth of ML in medicine and the complexities and challenges of burn care, this review specializes on ML applications in burn care. The objective was to examine the features and impact of applications in targeting various aspects of burn care and research. MethodsMEDLINE, the Cochrane Database of Systematic Reviews, ScienceDirect, and citation review of relevant primary and review articles were searched for studies involving burn care/research and machine learning. Data were abstracted on study design, study size, year, population, application of burn care/research, ML technique(s), and algorithm performance. Results15 retrospective observational studies involving burn patients met inclusion criteria. In total 5105 patients with acute thermal injury, 171 clinical burn wounds, 180 9-mer peptides, and 424 12-mer peptides were included in the studies. Studies focused on burn diagnosis (n=5), aminoglycoside response (n=3), hospital length of stay (n=2), survival/mortality (n=4), burn healing time (n=1), and antimicrobial peptides in burn patients (n=1). Of these 15 studies, 11 used artificial neural networks. Importantly, all studies demonstrated the benefits of ML in burn care/research and superior performance over traditional statistical methods. However, algorithm performance was assessed differently by different authors. Feature selection varied among studies, but studies with similar applications shared specific features including age, gender, presence of inhalation injury, total body surface area burned, and when available, various degrees of burns, infections, and previous histories/conditions of burn patients. ConclusionA common feature base may be determined for ML in burn care/research, but the impact of ML will require further validation in prospective observational studies and randomized clinical trials, establishment of common performance metrics, and high quality evidence about clinical and economic impacts. Only then can ML applications be advanced and accepted widely in burn care/research.