Eric McMullen, Rajan Grewal, Kyle Storm, Mahan Maazi, Abu Bakar Butt, Raghav Gupta, Howard Maibach
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引用次数: 0
Abstract
Background
Machine learning (ML) offers an opportunity in contact dermatitis (CD) research, where with full clinical picture, may support diagnosis and patch test accuracy.
Objective
This review aims to summarise the existing literature on how ML can be applied to CD in its entirety.
Methods
Embase, Medline, IEEE Xplore, and ACM Digital Library were searched from inception to February 7, 2024, for primary literature reporting on ML models in CD.
Results
7834 articles were identified in the search, with 110 moving to full-text review, and six articles included. Two used ML to identify key biomarkers to help distinguish between allergic contact dermatitis (ACD) and irritant contact dermatitis (ICD), three used image data to distinguish between ACD and ICD, and one used clinical and demographical data to predict the risk of positive patch tests. All studies used supervision in their ML model training with a total of 49 704 patients across all data sets. There was sparse reporting of the accuracy of these models.
Conclusions
Although the available research is still limited, there is evidence to suggest that ML has potential to support diagnostic outcomes in a clinical setting. Further research on the use of ML in clinical practice is recommended.
期刊介绍:
Contact Dermatitis is designed primarily as a journal for clinicians who are interested in various aspects of environmental dermatitis. This includes both allergic and irritant (toxic) types of contact dermatitis, occupational (industrial) dermatitis and consumers" dermatitis from such products as cosmetics and toiletries. The journal aims at promoting and maintaining communication among dermatologists, industrial physicians, allergists and clinical immunologists, as well as chemists and research workers involved in industry and the production of consumer goods. Papers are invited on clinical observations, diagnosis and methods of investigation of patients, therapeutic measures, organisation and legislation relating to the control of occupational and consumers".