ArticleMicroorganisms2026
Evaluation of an AI-Assisted Colony Counting System Across Multiple Culture Media Using Standardized Pure Culture Plates.
Article in Microorganisms, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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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.
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Authors and funding
5 authors.
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Abstract
Automated AI-assisted colony counting may improve standardization in digital microbiology, but performance can be affected by colony density, culture medium, colony morphology, adhesion, and plate artifacts. We evaluated the Starry-300 AI colony counting system using 382 standardized pure culture bacterial and yeast plates across four agar media. AI-assisted counts were compared with a three-reader median ImageJ-assisted manual comparator derived from independent counts by experienced technologists. The AI workflow showed close agreement with the manual consensus comparator across a broad colony density range. Overall, 360/382 plates (94.24%) were within ±10 CFU and 377/382 plates (98.69%) were within ±30 CFU of the manual median count. Error-based and agreement analyses showed a mean absolute error of 3.19 CFU/plate; both the intraclass correlation coefficient and Lin's concordance correlation coefficient were0.99. AI software analysis required approximately 5-15 s/plate, although this did not include plate handling, correction, or reporting. These findings support the analytical feasibility of reviewable AI-assisted colony enumeration under controlled pure culture conditions. Further validation using primary clinical specimens, mixed cultures, near-threshold samples, and external sites is required before broad clinical implementation.
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