[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290193-105":59,"doc-detail-290193-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","machine-learning-for-biomedical-applications-editorial","Machine Learning for Biomedical Applications - Editorial","","Machine Learning for Biomedical Applications presents a Special Issue overview on how ML techniques are advancing biomedical decision support. The editorial explains that improved computational capacity alongside ML enables outperforming or replacing classical methods across engineering and healthcare. Contributions include ML and deep learning pipelines for medical and biomedical imaging, covering retinal disease diagnosis, non-invasive ultrasonography for fatty liver disease, and liver, renal, and kidney image segmentation and classification tasks.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-for-biomedical-applications-editorial/290193/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-for-biomedical-applications-editorial/290193.png","ImageObject",300,407,{"name":92,"@type":93},"Himbo","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-17","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is the purpose of this Special Issue described in the editorial?","Question",{"text":113,"@type":114},"It aims to gather high-quality contributions that describe novel ML applications in the biomedical field, highlighting the topics covered by participating research groups.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does the editorial connect advances in computing to ML progress in biomedicine?",{"text":118,"@type":114},"It links improved computational capacity with the development of ML algorithms, arguing this combination supports ML’s ability to outperform or replace some classical methods and enables biomedical adoption.",{"name":120,"@type":111,"acceptedAnswer":121},"Which biomedical tasks are highlighted as major application areas?",{"text":122,"@type":114},"The editorial emphasizes medical/biomedical imaging, including retinal disease diagnosis, non-invasive ultrasonography for liver-related disease quantification, and segmentation or classification for liver and kidney imaging.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290193,1789640909,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":29,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":144,"preview_page_count":4},687197100911,"https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697","bioengineering  \nEditorial  \nMachine Learning for Biomedical Applications  \nGiuseppe Cesarelli 1, *,†, Alfonso Maria Ponsiglione 1,†, Mario Sansone 1, Francesco Amato 1, Leandro Donisi 2,‡ and Carlo Ricciardi 1,‡  \n1 Department of Electrical Engineering and Information Technology, University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy; [alfonsomaria.ponsiglione@unina.it](alfonsomaria.ponsiglione@unina.it) (A.M.P.); [msansone@unina.it](msansone@unina.it) (M.S.); [framato@unina.it](framato@unina.it) (F.A.); [carloricciardi.93@gmail.com](carloricciardi.93@gmail.com) (C.R.)  \n2 Department of Advanced Medical and Surgical Sciences, University of Campania Luigi Vanvitelli, Via De Crecchio 7, 80138 Naples, Italy; [lean.donisi@gmail.com](lean.donisi@gmail.com)  \n* Correspondence: [giuseppe.cesarelli@unina.it](giuseppe.cesarelli@unina.it)[ ](giuseppe.cesarelli@unina.it)† These authors contributed equally to this work.  \n‡ These authors contributed equally to this work.  \nMachine learning (ML) is a field of artificial intelligence that uses algorithms capable of extracting knowledge directly from data that could support decisions in multiple fields of engineering. Particularly in the last few years, calculating capacity has progressed and done so at the same speed as ML algorithms, a situation which potentiates the outperformance/replacement of several classical methods. This paradigm has also been confirmed in the context of the biomedical sector. For instance, previous research has documented the introduction of ML algorithms in studies aimed at disease diagnosis and monitoring based on biomedical signals and/or data [1–6] or on biomedical images [7–10] . Considering the great interest in these topics, this Special Issue (SI) sought high-quality contributions that  \nCitation: Cesarelli, G.; Ponsiglione, A.M.; Sansone, M.; Amato, F.; Donisi, L.; Ricciardi, C. Machine Learning for Biomedical Applications.  \nBioengineering 2024, 11, 790 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)bioengineering11080790  \nReceived: 4 July 2024  \nAccepted: 9 July 2024  \nPublished: 5 August 2024  \nCopyright: © 2024 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://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \ndescribed novel applications for ML strategies in the context of the biomedical field.  \nTable 1 shows the 21 contributions proposed to this SI so as to provide an overview of the topics addressed by the research groups that selected this SI as the best-suited platform in which to publish their studies.  \nMedical/biomedical imaging technology was mainly used by research groups that have proposed a contribution to this SI. In this last field, ML has gradually interested the authorized personnel since it has been demonstrated to potentially better support clinicians’decisions with respect to the use of the imaging techniques alone. These aspects have been investigated by two reviews/surveys proposed to this SI. Saleh et al. focused on ophthalmic imaging modalities to diagnose, detect, and stage retinal diseases (in particular, the Authors investigated age-related macular degeneration and diabetic retinopathy), which currently are mainly fundus imaging, optical coherence tomography, and optical coherence tomography angiography [11] . Therefore, the Authors collected studies that described applications where images were then fed to artificial intelligence (AI)-based methods, such as both traditional ML algorithms and deep learning (DL) pipelines; albeit, the Authors found that both strategies effectively helped to diagnose, detect, and stage retinal diseases. Saleh et al. conclude with their belief that only DL will eventually become state-of-the-art being bette","cbCaiigARbSFFtUz","https://ap.wps.com/l/cbCaiigARbSFFtUz","pdf",197998,"English","# Editorial\n## Special Issue scope and motivation\n## ML and deep learning for biomedical imaging\n## Examples: retinal, liver, and kidney applications\n## Imaging-based decision support and survey conclusions","[{\"question\":\"What is the purpose of this Special Issue described in the editorial?\",\"answer\":\"It aims to gather high-quality contributions that describe novel ML applications in the biomedical field, highlighting the topics covered by participating research groups.\"},{\"question\":\"How does the editorial connect advances in computing to ML progress in biomedicine?\",\"answer\":\"It links improved computational capacity with the development of ML algorithms, arguing this combination supports ML’s ability to outperform or replace some classical methods and enables biomedical adoption.\"},{\"question\":\"Which biomedical tasks are highlighted as major application areas?\",\"answer\":\"The editorial emphasizes medical/biomedical imaging, including retinal disease diagnosis, non-invasive ultrasonography for liver-related disease quantification, and segmentation or classification for liver and kidney imaging.\"}]","Machine Learning for Biomedical Applications - Editorial | PDF",15]