Prospective, comparative evaluation of a deep neural network and dermoscopy in the diagnosis of onychomycosis.

Journal: PloS one

PMID: 32525908

Abstract

BACKGROUND: Onychomycosis is the most common nail disorder and is associated with diagnostic challenges. Emerging non-invasive, real-time techniques such as dermoscopy and deep convolutional neural networks have been proposed for the diagnosis of this condition. However, comparative studies of the two tools in the diagnosis of onychomycosis have not previously been conducted.

Authors

Young Jae Kim

Department of Biomedical Engineering, College of Medicine, Gachon University, Gyeonggi-do, Republic of Korea.
Seung Seog Han

I Dermatology, Seoul, Korea.
Hee Joo Yang

Department of Dermatology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Sung Eun Chang

Department of Dermatology, Asan Medical Center, Ulsan University College of Medicine, Seoul, Korea.

Keywords

Adult Algorithms Computer Systems Deep Learning Dermatologists Dermoscopy Diagnosis, Computer-Assisted Diagnostic Errors Female Foot Dermatoses Humans Hydroxides Male Middle Aged Mycological Typing Techniques Neural Networks, Computer Onychomycosis Photography Potassium Compounds Predictive Value of Tests Prospective Studies Young Adult

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View on PubMed Access via DOI PubMed (32525908)

Prospective, comparative evaluation of a deep neural network and dermoscopy in the diagnosis of onychomycosis.

Abstract

Authors

Keywords

External Resources

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