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METHODS article

Front. Artif. Intell.
Sec. Machine Learning and Artificial Intelligence
Volume 7 - 2024 | doi: 10.3389/frai.2024.1325219

Automating Parasite Egg Detection: Insights from the first AI-KFM Challenge

Provisionally accepted
Salvatore Capuozzo Salvatore Capuozzo 1Stefano Marrone Stefano Marrone 1Michela Gravina Michela Gravina 1*Giuseppe Cringoli Giuseppe Cringoli 2Laura Rinaldi Laura Rinaldi 2Maria Paola Maurelli Maria Paola Maurelli 2Antonio Bosco Antonio Bosco 2Giulia Orrù Giulia Orrù 3Gian Luca Marcialis Gian Luca Marcialis 3Luca Ghiani Luca Ghiani 4Stefano Bini Stefano Bini 5Alessia Saggese Alessia Saggese 5Mario Vento Mario Vento 5Carlo Sansone Carlo Sansone 1
  • 1 Department of Electrical Engineering and Information Technology, Polytechnic and Basic Sciences School, University of Naples Federico II, Naples, Campania, Italy
  • 2 Department of Veterinary Medicine and Animal Production, University of Naples Federico II, Naples, Campania, Italy
  • 3 Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Sardinia, Italy
  • 4 Department of Biomedical Sciences, Faculty of Medicine and Surgery, University of Sassari, Sassari, Italy
  • 5 Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano (SA), Campania, Italy

The final, formatted version of the article will be published soon.

    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.

    Keywords: microscope, FLOTAC, Semantic segmentation, object detection, Veterinary, Parasite eggs

    Received: 20 Oct 2023; Accepted: 09 Aug 2024.

    Copyright: © 2024 Capuozzo, Marrone, Gravina, Cringoli, Rinaldi, Maurelli, Bosco, Orrù, Marcialis, Ghiani, Bini, Saggese, Vento and Sansone. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Michela Gravina, Department of Electrical Engineering and Information Technology, Polytechnic and Basic Sciences School, University of Naples Federico II, Naples, 80125, Campania, Italy

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.