[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128713-en":3,"doc-seo-128713-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128713,1099523882182,"Eliana","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Applications of Artificial Intelligence, Deep Learning, and Machine Learning to Support the Analysis of Microscopic Images of Cells and Tissues","Artificial intelligence (AI) transforms image data analysis across many biomedical fields, enabling object detection, feature extraction, classification, and segmentation. Deep learning (DL) research has driven major advances in computer vision for biomedical image analysis and data mining, supported by open-source tools and new deep neural network architectures. Improved accuracy in cell detection and segmentation enables automated extraction of quantifiable cellular and spatial features from microscope images, revealing cellular organization across diseases. This review consolidates current AI and DL methods for microscopy-based cell analysis and helps biologists apply ML models with limited backgrounds.","Review  \nApplications of Artificial Intelligence, Deep Learning, and Machine Learning to Support the Analysis of Microscopic Images of Cells and Tissues  \nMuhammad Ali 1,2, Viviana Benfante 2,3,4, *, Ghazal Basirinia 1,2, Pierpaolo Alongi 3,5, Alessandro Sperandeo 4, Alberto Quattrocchi 6, Antonino Giulio Giannone 6, Daniela Cabibi 6, Anthony Yezzi 7,  \nDomenico Di Raimondo 2, Antonino Tuttolomondo 2 and Albert Comelli 1, *  \nAcademic Editor: Hocine Cherifi  \nReceived: 31 December 2024  \nRevised: 8 February 2025  \nAccepted: 12 February 2025  \nPublished: 15 February 2025  \nCitation: Ali, M.; Benfante, V.; Basirinia, G.; Alongi, P.; Sperandeo, A.; Quattrocchi, A.; Giannone, A.G.; Cabibi, D.; Yezzi, A.; Di Raimondo, D.; et al. Applications of Artificial Intelligence, Deep Learning, and Machine Learning to Support the Analysis of Microscopic Images of Cells and Tissues. J. Imaging 2025, 11, 59. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)jimaging11020059  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 Ri.MED Foundation, Via Bandiera 11, 90133 Palermo, Italy; [amuhammad@fondazionerimed.com](amuhammad@fondazionerimed.com) (M.A.); [gbasirinia@fondazionerimed.com](gbasirinia@fondazionerimed.com) (G.B.)  \n2 Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Molecular and Clinical Medicine, University of Palermo, 90127 Palermo, Italy; [domenico.diraimondo@unipa.it](domenico.diraimondo@unipa.it) (D.D.R.); [bruno.tuttolomondo@unipa.it](bruno.tuttolomondo@unipa.it) (A.T.)  \n3 Advanced Diagnostic Imaging—INNOVA Project, Department of Radiological Sciences, A.R.N.A.S. Civico, Di Cristina e Benfratelli Hospitals, P.zza N. Leotta 4, 90127 Palermo, Italy; [pierpaolo.alongi@arnascivico.it](pierpaolo.alongi@arnascivico.it)  \n[4](4 Pharmaceutical Factory)[ Pharmaceutical Factory](4 Pharmaceutical Factory), [La Maddalena S.P.A](La Maddalena S.P.A)., [Via San Lorenzo Colli](Via San Lorenzo Colli), [312/d](312/d), [90146 Palermo](90146 Palermo), [Italy](Italy); [sperandeo@lamaddalenanet.it](sperandeo@lamaddalenanet.it)  \n5 Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, 90127 Palermo, Italy  \n6 Pathologic Anatomy Unit, Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, University of Palermo, 90127 Palermo, Italy; alberto.quattrocchi@unipa.it (A.Q.); [giulio.giannone@unipa.it](giulio.giannone@unipa.it) (A.G.G.); [cabibidaniela@virgilio.it](cabibidaniela@virgilio.it) (D.C.)  \n7 Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; [anthony.yezzi@ece.gatech.edu](anthony.yezzi@ece.gatech.edu)  \n* [Correspondence: viviana.benfante@arnascivico.it](Correspondence: viviana.benfante@arnascivico.it) (V.B.); acomelli@fondazionerimed.com (A.C.)  \nAbstract: Artificial intelligence (AI) transforms image data analysis across many biomedical fields, such as cell biology, radiology, pathology, cancer biology, and immunology, with object detection, image feature extraction, classification, and segmentation applications. Advancements in deep learning (DL) research have been a critical factor in advancing computer techniques for biomedical image analysis and data mining. A significant improvement in the accuracy of cell detection and segmentation algorithms has been achieved as a result of the emergence of open-source software and innovative deep neural network architectures. Automated cell segmentation now enables the extraction of quantifiable cellular and spatial features from microscope images of cells and tissues, providing critical insights into ","cbCaieAskGSLiTJ8","https://ap.wps.com/l/cbCaieAskGSLiTJ8","pdf",1620880,1,42,"English","en",105,"# Introduction\n## Cell culture and its biological value\n## Conventional workflows for cell analysis\n# Challenges in microscopic cell identification\n## Manual analysis in histopathology and cell culture workflows\n# AI methods for microscopy image analysis\n## Detection and segmentation\n## Feature extraction and data mining","[{\"question\":\"How does AI support the analysis of microscopic cell and tissue images?\",\"answer\":\"AI applies object detection, feature extraction, classification, and segmentation to microscopy images. These capabilities help convert raw image data into quantifiable cellular information.\"},{\"question\":\"What role do deep learning advances play in cell detection and segmentation?\",\"answer\":\"Deep learning research, along with open-source software and modern deep neural network architectures, has improved the accuracy of cell detection and segmentation algorithms.\"},{\"question\":\"What outcomes can automated cell segmentation provide for biomedical research?\",\"answer\":\"Automated segmentation extracts quantifiable cellular and spatial features from microscope images. These features support insights into cellular organization in different diseases.\"}]","Applications of Artificial Intelligence, Deep Learning, and Machine Learning to Support the Analysis of Microscopic Images of Cells and Tissues | PDF",1786002817,106,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"applications-of-artificial-intelligence-deep-learning-and-machine-learning-to-support-the-analysis-of-microscopic-images-of-cells-and-tissues","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/applications-of-artificial-intelligence-deep-learning-and-machine-learning-to-support-the-analysis-of-microscopic-images-of-cells-and-tissues/128713/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-25","2026-08-06",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"How does AI support the analysis of microscopic cell and tissue images?","Question",{"text":76,"@type":77},"AI applies object detection, feature extraction, classification, and segmentation to microscopy images. These capabilities help convert raw image data into quantifiable cellular information.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What role do deep learning advances play in cell detection and segmentation?",{"text":81,"@type":77},"Deep learning research, along with open-source software and modern deep neural network architectures, has improved the accuracy of cell detection and segmentation algorithms.",{"name":83,"@type":74,"acceptedAnswer":84},"What outcomes can automated cell segmentation provide for biomedical research?",{"text":85,"@type":77},"Automated segmentation extracts quantifiable cellular and spatial features from microscope images. 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