ArticleFrontiers in microbiology2026
Diagnostic performance of deep learning-based vaginal microecological morphology assessment for bacterial vaginosis and vulvovaginal candidiasis.
Article in Frontiers in microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Routine wet mount microscopy for bacterial vaginosis (BV) and vulvovaginal candidiasis (VVC) is rapid but operator-dependent and lacks ecological context. We evaluated a deep learning-based vaginal microecological morphology assessment (DL-VMM) system integrated in the GE6000 automated vaginal secretion analyzer. Methods: In a prospective cross-sectional study, 500 symptomatic women provided vaginal smears. DL-VMM was compared with routine wet mount microscopy against a composite reference standard (Nugent score, Amsel criteria, fungal microscopy). Key morphological features including clue cells, blastospores, hyphae, trichomonads, leukocytes, epithelial cells, and cleanliness grade were assessed. Diagnostic accuracy and Cohen's kappa were calculated. We also analyzed discordant cases to explore sources of disagreement. Results: For BV diagnosed by clue cells, DL-VMM achieved 100.00% sensitivity (95% CI: 94.19-100.00%), 98.86% specificity (97.37-99.58%), and κ = 0.95. For VVC by blastospores, sensitivity was 99.32% (95.85-99.96%), specificity 98.87% (96.94-99.70%), κ = 0.97; by hyphae, sensitivity 100.00% (97.68-100.00%), specificity 98.81% (97.04-99.63%), κ = 0.98. Total agreement between DL-VMM and manual microscopy for all parameters ranged from 96.40% to 99.60% (κ 0.89-0.98). No statistically significant differences were found between methods (all Conclusion: This prospective diagnostic study demonstrates that the DL-VMM system achieves high diagnostic accuracy for BV and VVC, with substantial to almost perfect agreement with routine wet mount microscopy. The automated system eliminates operator dependence, provides rapid results, and may standardize vaginitis diagnosis, although discordances, though minor, warrant awareness and potential manual review in equivocal cases.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.