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

Abstract

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.