ArticleRisk management and healthcare policy2026
Deep Learning-Based Rapid Identification of
Article in Risk management and healthcare policy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. 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.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Urinary tract infections (UTIs) are among the most common bacterial infections worldwide, with Patients and Methods: We analyzed 1547 chromogenic agar urine culture images (850 Results: The model achieved 99% accuracy (precision, recall, F1-score: 0.99) on internal testing. External validation yielded 100% accuracy, with the critical note that species labels in this independent set were inferred from colony colour. Rare errors involved atypical (eg, gold-pigmented) Conclusion: This AI model enables rapid (sub-second), accurate phenotypic classification of the most common UTI pathogens directly from routine culture plates. Integration into automated systems could reduce the diagnostic timeline by approximately 18 hours, facilitating earlier targeted therapy and supporting antimicrobial stewardship. A key consideration for implementation is the model's basis in phenotypic identification, which aligns with standard laboratory workflow but requires awareness of rare chromogenic variants. The reliance on chromogenic morphology for
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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.