{"title":"AI-powered microscopy image analysis for parasitology: integrating human expertise.","authors":"Ruijun Feng, Sen Li, Yang Zhang","doi":"10.1016/j.pt.2024.05.005","DOIUrl":null,"url":null,"abstract":"<p><p>Microscopy image analysis plays a pivotal role in parasitology research. Deep learning (DL), a subset of artificial intelligence (AI), has garnered significant attention. However, traditional DL-based methods for general purposes are data-driven, often lacking explainability due to their black-box nature and sparse instructional resources. To address these challenges, this article presents a comprehensive review of recent advancements in knowledge-integrated DL models tailored for microscopy image analysis in parasitology. The massive amounts of human expert knowledge from parasitologists can enhance the accuracy and explainability of AI-driven decisions. It is expected that the adoption of knowledge-integrated DL models will open up a wide range of applications in the field of parasitology.</p>","PeriodicalId":23327,"journal":{"name":"Trends in parasitology","volume":null,"pages":null},"PeriodicalIF":7.0000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Trends in parasitology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.pt.2024.05.005","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/5/31 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"PARASITOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Microscopy image analysis plays a pivotal role in parasitology research. Deep learning (DL), a subset of artificial intelligence (AI), has garnered significant attention. However, traditional DL-based methods for general purposes are data-driven, often lacking explainability due to their black-box nature and sparse instructional resources. To address these challenges, this article presents a comprehensive review of recent advancements in knowledge-integrated DL models tailored for microscopy image analysis in parasitology. The massive amounts of human expert knowledge from parasitologists can enhance the accuracy and explainability of AI-driven decisions. It is expected that the adoption of knowledge-integrated DL models will open up a wide range of applications in the field of parasitology.
期刊介绍：
Since its inception as Parasitology Today in 1985, Trends in Parasitology has evolved into a highly esteemed review journal of global significance, reflecting the importance of medical and veterinary parasites worldwide. The journal serves as a hub for communication among researchers across all disciplines of parasitology, encompassing endoparasites, ectoparasites, transmission vectors, and susceptible hosts.
Each monthly issue of Trends in Parasitology offers authoritative, cutting-edge, and yet accessible review articles, providing a balanced and comprehensive overview, along with opinion pieces offering personal and novel perspectives. Additionally, the journal publishes a variety of short articles designed to inform and stimulate thoughts in a lively and widely-accessible manner. These include Science & Society (discussing the interface between parasitology and the general public), Spotlight (highlighting recently published research articles), Forum (presenting single-point hypotheses), Parasite/Vector of the Month (featuring a modular display of the selected species), Letter (providing responses to recent articles in Trends in Parasitology), and Trendstalk (conducting interviews). Please note that the journal exclusively publishes literature reviews based on published data, with systematic reviews, meta-analysis, and unpublished primary research falling outside our scope.
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