Automating parasite egg detection: insights from the first AI-KFM challenge

• Published in: Frontiers in artificial intelligence Vol. 7; p. 1325219
• Main Authors: Capuozzo, Salvatore, Marrone, Stefano, Gravina, Michela, Cringoli, Giuseppe, Rinaldi, Laura, Maurelli, Maria Paola, Bosco, Antonio, Orrù, Giulia, Marcialis, Gian Luca, Ghiani, Luca, Bini, Stefano, Saggese, Alessia, Vento, Mario, Sansone, Carlo
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 29-08-2024
• Subjects: Artificial Intelligence; FLOTAC; microscope; object detection; parasite eggs; semantic segmentation; veterinary; parasite eggs; FLOTAC; object detection; veterinary; semantic segmentation; microscope
• ISSN: 2624-8212; 2624-8212
• DOI: 10.3389/frai.2024.1325219

In the field of veterinary medicine, the detection of parasite eggs in the fecal samples of livestock animals represents one of the most challenging tasks, since their spread and diffusion may lead to severe clinical disease. Nowadays, the scanning procedure is typically performed by physicians with professional microscopes and requires a significant amount of time, domain knowledge, and resources. The Kubic FLOTAC Microscope (KFM) is a compact, low-cost, portable digital microscope that can autonomously analyze fecal specimens for parasites and hosts in both field and laboratory settings. It has been shown to acquire images that are comparable to those obtained with traditional optical microscopes, and it can complete the scanning and imaging process in just a few minutes, freeing up the operator's time for other tasks. To promote research in this area, the first AI-KFM challenge was organized, which focused on the detection of gastrointestinal nematodes (GINs) in cattle using RGB images. The challenge aimed to provide a standardized experimental protocol with a large number of samples collected in a well-known environment and a set of scores for the approaches submitted by the competitors. This paper describes the process of generating and structuring the challenge dataset and the approaches submitted by the competitors, as well as the lessons learned throughout this journey.