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

IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Frontiers in Artificial Intelligence Pub Date : 2024-08-29 eCollection Date: 2024-01-01 DOI:10.3389/frai.2024.1325219
Salvatore Capuozzo, Stefano Marrone, Michela Gravina, Giuseppe Cringoli, Laura Rinaldi, Maria Paola Maurelli, Antonio Bosco, Giulia Orrù, Gian Luca Marcialis, Luca Ghiani, Stefano Bini, Alessia Saggese, Mario Vento, Carlo Sansone
{"title":"Automating parasite egg detection: insights from the first AI-KFM challenge.","authors":"Salvatore Capuozzo, Stefano Marrone, Michela Gravina, Giuseppe Cringoli, Laura Rinaldi, Maria Paola Maurelli, Antonio Bosco, Giulia Orrù, Gian Luca Marcialis, Luca Ghiani, Stefano Bini, Alessia Saggese, Mario Vento, Carlo Sansone","doi":"10.3389/frai.2024.1325219","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11390596/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/frai.2024.1325219","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

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.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
寄生虫卵检测自动化:第一次人工智能-KFM 挑战赛的启示。
在兽医领域,检测家畜粪便样本中的寄生虫卵是最具挑战性的任务之一,因为寄生虫卵的传播和扩散可能导致严重的临床疾病。如今,扫描过程通常由医生使用专业显微镜进行,需要大量的时间、领域知识和资源。Kubic FLOTAC 显微镜(KFM)是一种结构紧凑、成本低廉的便携式数码显微镜,可在野外和实验室环境中自主分析粪便标本中的寄生虫和宿主。事实证明,它所获得的图像可与传统光学显微镜获得的图像相媲美,而且只需几分钟就能完成扫描和成像过程,从而使操作人员能够腾出时间从事其他工作。为促进该领域的研究,举办了第一届 AI-KFM 挑战赛,重点是利用 RGB 图像检测牛的胃肠道线虫 (GIN)。该挑战赛旨在提供一个标准化的实验方案,在众所周知的环境中采集大量样本,并为参赛者提交的方法提供一套评分标准。本文介绍了挑战赛数据集的生成和结构化过程、参赛者提交的方法以及整个过程中的经验教训。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
6.10
自引率
2.50%
发文量
272
审稿时长
13 weeks
期刊最新文献
Impact of hypertension on coronary artery plaques and FFR-CT in type 2 diabetes mellitus patients: evaluation utilizing artificial intelligence processed coronary computed tomography angiography. Using large language models to support pre-service teachers mathematical reasoning-an exploratory study on ChatGPT as an instrument for creating mathematical proofs in geometry. Prediction of unobserved bifurcation by unsupervised extraction of slowly time-varying system parameter dynamics from time series using reservoir computing. Enzyme catalytic efficiency prediction: employing convolutional neural networks and XGBoost. Heuristic machine learning approaches for identifying phishing threats across web and email platforms.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1