Diagnosis of Onychomycosis: From Conventional Techniques and Dermoscopy to Artificial Intelligence

Sophie Soyeon Lim; Jungyoon Ohn; Je-Ho Mun

doi:10.3389/fmed.2021.637216

Diagnosis of Onychomycosis: From Conventional Techniques and Dermoscopy to Artificial Intelligence

Front Med (Lausanne). 2021 Apr 15:8:637216. doi: 10.3389/fmed.2021.637216. eCollection 2021.

Authors

Sophie Soyeon Lim¹, Jungyoon Ohn^{2

3}, Je-Ho Mun^{2

3}

Affiliations

¹ Alfred Health, Melbourne, VIC, Australia.
² Department of Dermatology, Seoul National University College of Medicine, Seoul, South Korea.
³ Institute of Human-Environment Interface Biology, Seoul National University, Seoul, South Korea.

Abstract

Onychomycosis is a common fungal nail infection. Accurate diagnosis is critical as onychomycosis is transmissible between humans and impacts patients' quality of life. Combining clinical examination with mycological testing ensures accurate diagnosis. Conventional diagnostic techniques, including potassium hydroxide testing, fungal culture and histopathology of nail clippings, detect fungal species within nails. New diagnostic tools have been developed recently which either improve detection of onychomycosis clinically, including dermoscopy, reflectance confocal microscopy and artificial intelligence, or mycologically, such as molecular assays. Dermoscopy is cost-effective and non-invasive, allowing clinicians to discern microscopic features of onychomycosis and fungal melanonychia. Reflectance confocal microscopy enables clinicians to observe bright filamentous septate hyphae at near histologic resolution by the bedside. Artificial intelligence may prompt patients to seek further assessment for nails that are suspicious for onychomycosis. This review evaluates the current landscape of diagnostic techniques for onychomycosis.

Keywords: artificial intelligence; dermoscopy; diagnosis; diagnostic imaging; fungi; onychomycosis; pathology; reflectance confocal microscopy.

Publication types

Review