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Objective <p>Insect 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 <i>Chironomid</i> Quantification Tool (CQT-AI), to detect and quantify infestations of non-biting midges (<i>Chironomidae</i>) in an aquatic housing system for <i>Xenopus laevis</i>. 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 <i>Bacillus thuringiensis israelensis</i> (Bti), were tested in parallel.</p> Results <p>CQT-AI proved to be a reliable tool for detecting and quantifying infestation dynamics of adult <i>Chironomidae</i>, enabling objective assessment of treatment effects within 1&#xa0;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.</p>

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Monitoring infestation levels using an AI-based Chironomid quantification tool (CQT) in a laboratory Xenopus laevis facility

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月19日

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  • Maximilian Schwarzbach,
  • Bastian Popper

摘要

Objective

Insect 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.

Results

CQT-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.