[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128198-en":3,"doc-seo-128198-105":31,"detail-sidebar-cat-0-en-105":84},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128198,2336475104042,"Skyler","https://ap-avatar.wpscdn.com/avatar/22000c4c32af1715be0?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786537525561427321",8,"Research & Report","Machine learning assisted classification of staphylococcal biofilm maturity - academic research summary","An increasing incidence of device-related, biofilm-associated infections drives the need for reliable in vitro models and consistent characterization of biofilm maturity. The work addresses the lack of a systematic maturity description beyond incubation time by introducing six maturity classes derived from atomic force microscopy (AFM) topographic features of substrate, bacterial cells, and extracellular matrix. Independent human observers achieved mean accuracy of 0.77, while a developed machine-learning algorithm reached mean accuracy 0.66 with comparable recall and 0.91 off-by-one accuracy, and is provided as an open access desktop tool.","Machine learning assisted classification of staphylococcal biofilm maturity  \nDun, S.C.J. van; Knol, R.; Silva-Herdade, A.S.; Veiga, A.S.; Castanho, M.A.R.B.; Nibbering, P.H.; ... ; Boer, [M.G.J. de](M.G.J. de)  \nCitation  \nDun, S. C. J. van, Knol, R., Silva-Herdade, A. S., Veiga, A. S., Castanho, M. A. R. B., Nibbering, P. H.,…Boer, [M. G. J. de](M. G. J. de). (2025). Machine learning assisted classification of staphylococcal biofilm maturity. Biofilm, 9. doi:10.1016/j.bioflm.2025.100283  \nVersion: Publisher's Version  \nLicense:  Creative Commons CC BY 4.0 license  \nDownloaded from:  [https://hdl.handle.net/1887/4251345](https://hdl.handle.net/1887/4251345)  \nNote: To cite this publication please use the final published version (if applicable) .  \nBioϧlm 9 (2025) 100283  \nContents lists available at ScienceDirect  \nBiofilm  \n[journal homepage:](journal homepage: www.sciencedirect.com/journal/biofilm)[ www.sciencedirect.com/journal/biofilm](journal homepage: www.sciencedirect.com/journal/biofilm)  \n| Machine learning assisted classification of staphylococcal biofilm maturity  \u003Cbr>S.C.J. van Duna,* , R. Knol a, A.S. Silva-Herdadeb,c, A.S. Veiga b,c, M.A.R.B. Castanhob,c, P.H. Nibbering a,d, B.G.C.W. Pijlse, A.M. van der Doesf, J. Dijkstra g, [M.G.J. de](M.G.J. de) Boera\u003Cbr>a Leiden University Center for Infectious Diseases (LUCID), Laboratory of Infectious Diseases, Leiden University Medical Center, Leiden, the Netherlands\u003Cbr>b Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal c Gulbenkian Institute for Molecular Medicine, Lisboa, Portugal d Human Health Vision, Zwolle, the Netherlands\u003Cbr>e Department of Orthopaedics, Leiden University Medical Center, Leiden, the Netherlands\u003Cbr>f PulmoScience Lab, Department of Pulmonology, Leiden University Medical Center, Leiden, the Netherlands g Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords: Biofilm Classification\u003Cbr>Atomic force microscopy Staphylococcus aureus Machine learning algorithm |  | An increasing incidence of device-related, biofilm-associated infections has been observed in clinical practice worldwide. In vitro biofilm models are essential to study these burdensome infections and to design and test potential new treatment approaches. However, there is considerable variation in in vitro biofilm models, and a generally accepted systematic description of biofilm maturity – apart from incubation time – is lacking.\u003Cbr>Therefore, we proposed a scheme comprised of 6 different classes based on common topographic characteristics, i.e., the substrate, bacterial cells and extracellular matrix, identified by atomic force microscopy (AFM), to describe biofilm maturity independent of incubation time. Evaluation of a test set of staphylococcal biofilm images by a group of independent researchers showed that human observers were capable of classifying images with a mean accuracy of 0.77 ± 0.18. However, manual evaluation of AFM biofilm images is time-consuming, and subject to observer bias. To circumvent these disadvantages, a machine learning algorithm was designed and developed to aid in classification of biofilm images.\u003Cbr>The designed algorithm was capable of identifying pre-set characteristics of biofilms and able to discriminate between the six different classes in the proposed framework. Compared to the established ground truth, the mean accuracy of the developed algorithm amounted to 0.66 ± 0.06 with comparable recall, and off-by-one accuracy of 0.91 ± 0.05. This algorithm, which classifies AFM images of biofilms, has been made available as an open access desktop tool. |\n\n1. Introduction  \nBiofilms are multicellular bacterial communities in a self-produced extracellular matrix, typically found in almost every part of nature. Bacterial biofilms are an important contributor to global health problems due to their resistance t","cbCainTJzYScdmdQ","https://ap.wps.com/l/cbCainTJzYScdmdQ","pdf",3139523,2,1,9,"English","en",105,"# Introduction\n# Materials and Methods\n## AFM-based classification framework\n## Evaluation with human observers\n## Machine learning algorithm\n# Results\n## Classification accuracy and off-by-one performance\n# Open access tool availability","[{\"question\":\"Is the biofilm classification tool available to users?\",\"answer\":\"Yes. The study states that the algorithm that classifies AFM images of biofilms has been made available as an open access desktop tool.\"}]","Machine learning assisted classification of staphylococcal biofilm maturity - academic research summary | PDF",1785945502,23,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":79,"head_meta":81,"extra_data":83,"updated_unix":29},"machine-learning-assisted-classification-of-staphylococcal-biofilm-maturity-academic-research-summary","",{"@graph":37,"@context":78},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/machine-learning-assisted-classification-of-staphylococcal-biofilm-maturity-academic-research-summary/128198/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72],{"name":73,"@type":74,"acceptedAnswer":75},"Is the biofilm classification tool available to users?","Question",{"text":76,"@type":77},"Yes. 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