AI-Driven Realtime Monitoring of Early Indicators for Ichthyophthirius multifiliis Infection of Rainbow Trout

A novel video-based real-time system based on AI (artificial intelligence) was developed to detect clinical signs in fish exposed to pathogens. We selected a White Spot Disease model involving rainbow trout as the experimental animal and the parasitic ciliate Ichthyophthirius multifiliis as a pathogen. We compared two identical fish tank systems: one tank was infected by co-habitation, whereas the other tank was kept non-infected (sham infection). The two fish tanks were separately video monitored (full top and side view) during the course of infection, during which fish were removed whenever they developed clinical signs (direct visual inspection by the observer). Image analysis (object detection, classification and tracking) was used to track behavioural changes in fish (in every recorded video frame), focusing on movement patterns and spatial localisation. Initially, the two fish groups (infected and non-infected) exhibited similar behaviour and non-infected fish did not change behaviour during the 15 d observation period (from 5 d before infection until 10 dpi). At 4, 7, 8, 9 and 10 dpi some infected fish showed clinical signs (equilibrium disturbance, gasping and lethargy) and were removed from the experiment. Anorexia occurred from 5 dpi and a gradual progression of gasping behaviour was noted, whereas the frequency of fish flashing (rubbing/scratching against objects) was low. Equilibrium disturbances and the development of white spots in the skin appeared to be a much later (8-10 dpi at this temperature) indicator of infection. The video analysis showed a general distribution of non-infected fish in all parts of the fish tank during the entire experiment, whereas infected fish already at 4-5 dpi moved towards higher water currents in the top and bottom positions. This change of fish positioning within the tank appeared as a promising early indicator of infection. The study suggests that continuous monitoring of fish behaviour using AI can potentially optimise the timing of humane endpoints, indicate disease signs earlier and thereby improve animal welfare in both animal experimentation and in aquaculture settings.