Assessing the Validity and Impact of Remote Digital Image Reading in Fungal Diagnostics
Vilhelmina Lundgren, Özlem Dogan, Anna Ekwall-Larson, Christine Stenström, Erja Chryssanthou, Maria Guglielmeti, Ylva Närström, Patrik Dinnétz, Silvia Botero-Kleiven, Volkan Özenci

TL;DR
This study shows that remote digital image reading can help diagnose fungal infections, especially in areas with limited mycology expertise.
Contribution
The study demonstrates the feasibility of telemycology for fungal diagnostics with varying accuracy across specimen types.
Findings
Accuracy of remote assessments ranged from 78 to 93% across different fungal image groups.
Individual participant accuracy varied between 76 and 92%.
Telemycology is a promising tool for fungal diagnostics in resource-limited settings.
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…
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Taxonomy
TopicsCell Image Analysis Techniques · AI in cancer detection · Antifungal resistance and susceptibility
