ArticleFrontiers in cellular and infection microbiology2026
Fungal recognition in vaginal discharge using deep learning analysis of mobile device-acquired microscopic images.
Article in Frontiers in cellular and infection microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
2 citing papers in PubMed.
- Fungal morphotype detection and quantification in microscopic images with TU_MyCo-vision: a user-friendly deep learning object detection tool.Fungal biology and biotechnology · 2026Article
- Diagnostic performance of deep learning-based vaginal microecological morphology assessment for bacterial vaginosis and vulvovaginal candidiasis.Frontiers in microbiology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Vulvovaginal candidiasis (VVC) is a common fungal infection that is frequently diagnosed through manual microscopic examination of vaginal discharge. Artificial Intelligence (AI)-assisted analysis of microscopic images enables rapid and accurate diagnosis, supporting timely and effective antifungal therapeutic interventions. However, conventional light microscopy often lacks cameras, limiting digital image analysis and AI applications. While mobile devices offer a practical alternative, no AI tools currently exist for the automated detection of fungal cellular morphology in microscopic images captured by smartphones and tablets. In this study, we developed deep learning models to segment fungal morphologies in microscopic images of vaginal discharge acquired with smartphones and tablets. Methods: Three models were developed: ResNet18 for binary classification ( Results: ResNet18 achieved F1-score=0.986, AUC = 0.99. YOLOv5 performed best at IoU=0.50 (precision=0.812, recall=0.622, mAP50 = 0.730); YOLOv11 at IoU=0.25 (precision=0.766, recall=0.700, mAP50 = 0.727). Expert ratings averaged 4.25/5. Only 3.68% of images were rated as inappropriate due to false negative or false positive segmentations. Conclusion: ResNet18 accurately classified microscopic images for fungal elements, while the YOLOv11 model effectively delineated
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.