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A dedicated deep learning workflow for automatic Fasciola hepatica and Calicophoron daubneyi egg detection using the
Salvatore Capuozzo1, Maria Paola Maurelli2, Stefano Marrone1
1Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Via Claudio 21, 80125, Naples, Italy.
International Journal for Parasitology
|August 29, 2025
Summary
The Kubic FLOTAC Microscope (KFM) uses AI for accurate parasite egg detection in livestock, improving diagnosis and control of fascioliasis and calicophoriasis.
Area of Science:
- Veterinary Parasitology
- Artificial Intelligence in Diagnostics
- Livestock Health Management
Background:
- Fasciola hepatica and Calicophoron daubneyi infections pose significant health and economic threats to ruminant livestock.
- Accurate and efficient diagnosis is crucial for controlling the spread of these parasitic trematodes.
- Current copromicroscopic methods require improvement for field and laboratory settings.
Purpose of the Study:
- To optimize the Kubic FLOTAC Microscope (KFM) system for enhanced discrimination between Fasciola hepatica and Calicophoron daubneyi eggs.
- To evaluate the performance of the KFM system with an Artificial Intelligence (AI) predictive model for automated parasite egg detection.
- To validate the KFM system's accuracy using both simulated and field-collected livestock fecal samples.
Main Methods:
- Development and optimization of an AI-powered detection model integrated into the portable Kubic FLOTAC Microscope (KFM).
- Utilized FLOTAC/Mini-FLOTAC techniques for high-sensitivity parasite egg detection.
- Trained and evaluated the AI model using two protocols: egg-spiked samples and naturally infected samples, followed by validation with field samples verified by optical microscopy.
Main Results:
- The optimized KFM system demonstrated satisfactory detection performance for both Fasciola hepatica and Calicophoron daubneyi eggs.
- The AI predictive model achieved a mean absolute error of only 8 eggs per sample for fecal egg count determination.
- The system successfully integrated automated parasite egg detection with a web interface and AI server for image analysis.
Conclusions:
- The Kubic FLOTAC Microscope (KFM) is a valuable and reliable tool for parasitological diagnosis in livestock.
- The AI-driven KFM system significantly improves the accuracy and efficiency of diagnosing Fasciola hepatica and Calicophoron daubneyi infections.
- This technology supports the livestock industry by enabling better disease control and management strategies.
