Advancing mold identification in the routine laboratory, performance of smartphone-based imaging and a newly developed convolutional neural network

ABSTRACT Mold identification in clinical diagnostics is traditionally labor-intensive and is dependent on expert interpretation. MoldVision is a deep-learning approach that uses smartphone images of mold cultures to automate identification. We analyzed 161 clinical isolates across four common mold g...

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Bibliografski detalji

Glavni autori:	Lukas Weber, Sarah Brueningk, Bettina Schulthess, Gloria Stillhart, Michelle Bressan, Nadja Pulver-Vontobel, Jasmin Schimetzki, Rosmi Puthan, Andrea Egli-Berini, Oliver Nolte, Adrian Egli
Format:	Artigo
Jezik:	Inglês
Izdano:	American Society for Microbiology 2026-02-01
Serija:	Microbiology Spectrum
Teme:	diagnostic mycology machine learning mycology artificial intelligence deep learning
Online pristup:	https://journals.asm.org/doi/10.1128/spectrum.02924-25
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