ArticleBMC research notes2026
Monitoring infestation levels using an AI-based Chironomid quantification tool (CQT) in a laboratory Xenopus laevis facility.
Article in BMC research notes, 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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Abstract
objectiveInsect infestations in laboratory animal facilities represent a potential risk to animal welfare, staff hygiene, and experimental integrity. In this study, we developed and applied a custom AI-assisted image analysis workflow, the Chironomid Quantification Tool (CQT-AI), to detect and quantify infestations of non-biting midges (Chironomidae) in an aquatic housing system for Xenopus laevis. To evaluate the applicability of CQT-AI for monitoring infestation dynamics and treatment effects, different control strategies, including increased water exchange and biological control using Bacillus thuringiensis israelensis (Bti), were tested in parallel.
resultsCQT-AI proved to be a reliable tool for detecting and quantifying infestation dynamics of adult Chironomidae, enabling objective assessment of treatment effects within 1 min. Increased water exchange resulted in measurable changes in water chemistry but only moderately reduced infestation levels. In contrast, treatment with Bti led to rapid and near-complete eradication of adult midges within six weeks without detectable adverse effects on animal health. Overall, the study demonstrates that CQT-AI provides an efficient and objective approach for monitoring infestation dynamics and evaluating control strategies in aquatic laboratory systems.
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