ArticleMycopathologia2025
Assessing the Validity and Impact of Remote Digital Image Reading in Fungal Diagnostics.
Article in Mycopathologia, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Artificial Intelligence in the Diagnosis of Superficial Mycoses: A Scoping Review.Journal of fungi (Basel, Switzerland) · 2026Review
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mycological diagnostics play a crucial role in patient management and treatment of invasive fungal infections. Despite the significant global burden of fungal diseases, awareness and diagnostic capabilities in mycology laboratories lag behind other microbiological disciplines. Mycological diagnostics often require microscopic analysis of clinical samples and culture. The interpretation of microscopy requires extensive expertise in clinical mycology. This study aimed to explore the feasibility of remote digital reading for preliminary identification of fungi. In this study, five mycology-trained participants were asked to analyze a total of 474 images divided into three main groups of yeasts (73 images), filamentous fungi (341 images), and direct fluorescent microscopy from clinical samples (60 images). The accuracy of the assessments varied, with an average correct decision rate between 78 and 93% across the three image groups. Individual participant's performance showed a mean accuracy rate ranging between 76 and 92%. A significant difference was observed in the assessment accuracy across specimen groups and among individual participants (p < 0.05). However, there was no significant interaction effect between participants and image group (p = 0.118). In conclusion, telemycology offers a promising alternative to standard microscopy diagnostics of fungal infections, especially in settings where skilled mycologists are lacking, including low- and middle-income countries.
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