[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120446-en":3,"doc-seo-120446-105":30,"detail-sidebar-cat-0-en-105":91},{"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},120446,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Integrating machine learning and biosensors in microfluidic devices - A review","Microfluidic devices are increasingly used for chemical research, point-of-care systems, drug development, and clinical studies, but effective operation requires local control and interpretation of experimental variables. Biosensors provide accuracy, selectivity, and responsiveness for sensing physical quantities and biochemical concentrations, yet their signals often demand careful analysis. Machine learning algorithms enable automatic learning from biosensor data, improve extraction of signal features, and support the emerging concept of “intelligent microfluidics,” where sensing and computation co-evolve to enhance microfluidic performance. This review highlights the microfluidics-biosensors-machine-learning triad and its reported applications.","Biosensors and Bioelectronics 263 (2024) 116632  \nContents lists available at ScienceDirect  \nBiosensors and Bioelectronics  \njournal [homepage: www.elsevier.com/locate/bios](homepage: www.elsevier.com/locate/bios)  \n| Integrating machine learning and biosensors A review |  |  | in microfluidic devices: |  |\n| --- | --- | --- | --- | --- |\n| Gianni Antonelli , Joanna Filippi , Michele D’Orazio , Giorgia Curci , Paola Casti ,\u003Cbr>*\u003Cbr>Arianna Mencattini , Eugenio Martinelli\u003Cbr>Department of Electronic Engineering & Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), University of Rome Tor Vergata, Via del Politecnico, 1, 00133, Rome, Italy |  |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |  |\n| Keywords:\u003Cbr>Machine learning Lab-on-a-Chip Biosensing system Intelligent microfluidics Biosensors integration |  | Microfluidic devices are increasingly widespread in the literature, being applied to numerous exciting applications, from chemical research to Point-of-Care devices, passing through drug development and clinical scenarios. Setting up these microenvironments, however, introduces the necessity of locally controlling the variables involved in the phenomena under investigation. For this reason, the literature has deeply explored the possibility of introducing sensing elements to investigate the physical quantities and the biochemical concentration inside microfluidic devices. Biosensors, particularly, are well known for their high accuracy, selectivity, and responsiveness. However, their signals could be challenging to interpret and must be carefully analysed to carry out the correct information. In addition, proper data analysis has been demonstrated even to increase biosensors’mentioned qualities. To this regard, machine learning algorithms are undoubtedly among the most suitable approaches to undertake this job, automatically learning from data and highlighting biosensor signals’ characteristics at best. Interestingly, it was also demonstrated to benefit microfluidic devices themselves, in a new paradigm that the literature is starting to name “intelligent microfluidics”, ideally closing this benefic interaction among these disciplines. This review aims to demonstrate the advantages of the triad paradigm microfluidicsbiosensors-machine learning, which is still little used but has a great perspective. After briefly describing the single entities, the different sections will demonstrate the benefits of the dual interactions, highlighting the applications where the reviewed triad paradigm was employed. |  |  |\n\n1. Introduction  \nThe ever-increasing development of microfluidics systems (in particular, Lab-on-a-Chip and Organ-on-a-Chip devices, used to mimic human organ and system functionalities in vitro) has made necessary the integration of biosensing elements (Zhu et al., 2021). Physical biosensors (i.e., biosensors probing physical quantities, such as temperature, pressure, or strain) can be used to inspect the environment of the tissues and maintain them more representative of the native conditions (Lu et al., 2019; Sang et al., 2014; Salvo et al., 2017; Haleem et al., 2021; Mohankumar et al., 2021; Cao et al., 2024). On the other hand, biochemical sensors, inspecting chemical analytes and biological compounds, can help in checking cellular functionality and behaviour, keeping them similar to their in vivo counterparts (Zhang et al., 2024; Xu et al., 2024; Wang et al., 2024a; Bouquerel et al., 2022). Without such understanding, it is impossible to correlate the outcome of a desired  \nmeasure to external stimuli and obtain new knowledge about the biological phenomenon under study. However, the unmet need to monitor the status of Lab/Organ-on-a-Chips during experimentation remains tobe fully addressed.  \nIntegrating artificial intelligence (and machine learning in particular) was demonstrated to be crucial when dealing with complex biosensor signals (Cui et al., 202","cbCaikpczhNu5b9x","https://ap.wps.com/l/cbCaikpczhNu5b9x","pdf",6957715,1,13,"English","en",105,"# Introduction\n## Microfluidic systems and lab/organ-on-a-chip context\n## Role of biosensors in physical and biochemical monitoring\n## Need for data interpretation and intelligent microfluidics paradigm","[{\"question\":\"Why are biosensors important in microfluidic lab/organ-on-a-chip devices?\",\"answer\":\"Biosensors allow monitoring of physical quantities and biochemical concentrations within microfluidic environments, helping maintain conditions representative of native tissue and supporting cellular functionality assessment.\"},{\"question\":\"What challenge arises when interpreting biosensor signals in these devices?\",\"answer\":\"Biosensor outputs can be difficult to interpret, requiring careful analysis to correctly translate sensor readings into meaningful information about the biological phenomenon under study.\"},{\"question\":\"How does machine learning improve biosensor-based microfluidic workflows?\",\"answer\":\"Machine learning algorithms automatically learn patterns from data and highlight key signal characteristics, enabling better processing of complex biosensor signals and supporting the “intelligent microfluidics” paradigm.\"}]","Integrating machine learning and biosensors in microfluidic devices - A review | PDF",1785730160,33,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"integrating-machine-learning-and-biosensors-in-microfluidic-devices-a-review","",{"@graph":36,"@context":85},[37,54,68],{"@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/integrating-machine-learning-and-biosensors-in-microfluidic-devices-a-review/120446/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are biosensors important in microfluidic lab/organ-on-a-chip devices?","Question",{"text":75,"@type":76},"Biosensors allow monitoring of physical quantities and biochemical concentrations within microfluidic environments, helping maintain conditions representative of native tissue and supporting cellular functionality assessment.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What challenge arises when interpreting biosensor signals in these devices?",{"text":80,"@type":76},"Biosensor outputs can be difficult to interpret, requiring careful analysis to correctly translate sensor readings into meaningful information about the biological phenomenon under study.",{"name":82,"@type":73,"acceptedAnswer":83},"How does machine learning improve biosensor-based microfluidic workflows?",{"text":84,"@type":76},"Machine learning algorithms automatically learn patterns from data and highlight key signal characteristics, enabling better processing of complex biosensor signals and supporting the “intelligent microfluidics” paradigm.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]