AI-Assisted Fungal Detection in Dermatopathology: A Study on Accuracy, Efficiency, and Usability.

Paul Schmidle^1,2, Tobias Lang³, Sebastian Springenberg³, Diego Calvopiña³, Theresa Köhler³, Valentina Getova⁴, Ana-Maria Gebing⁵, Sonja Hetzer⁵, Johanna Pierrone⁵, Valentin Aebischer⁶, Dieter Metze¹, Almut Böer-Auer^1,4, Stephan Alexander Braun^1,7

¹Department of Dermatology, Medical Faculty, University of Münster, Münster, Germany.

²Department of Dermatology, Medical Center, University of Freiburg, Freiburg, Germany.

³Mindpeak GmbH, Hamburg, Germany.

⁴Dermatologikum Hamburg, Hamburg, Germany.

⁵ProDermpath Vreden, Vreden, Germany.

⁶Department of Dermatology, Medical Faculty, University of Tübingen, Tübingen, Germany.

⁷Department of Dermatology, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany.

PubMed DOI

Abstract

BACKGROUND AND OBJECTIVES: AI-based image analysis is increasingly applied in pathology. Excluding fungal elements in PAS-stained skin sections is labor-intensive and well suited for AI assistance. While fungal detection in nails has been studied, skin-biopsy applications and real-world benefit remain limited. This study aimed to develop an AI algorithm for fungal detection in skin histology, crea…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free Account Sign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.