[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127767-en":3,"doc-seo-127767-105":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":4,"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},127767,1099523882182,"Eliana","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",7,"Healthcare","SCALING ARTIFICIAL INTELLIGENCE IN ENDOSCOPY: FROM MODEL DEVELOPMENT TO MACHINE LEARNING OPERATIONS FRAMEWORKS - Dottorato di ricerca","The thesis explores scaling artificial intelligence in endoscopy by connecting model development with machine learning operations frameworks. It focuses on videomics as a pathway to apply deep learning to diagnostic endoscopic workflows, covering key tasks such as quality assessment, classification, detection, segmentation, and detailed characterization. The work emphasizes the need for standardization in deep learning studies and discusses clinical perspectives, including preliminary results for automatic segmentation of oral and oropharyngeal cancer using narrow band imaging.","DOTTORATO DI RICERCA IN INTELLIGENZA ARTIFICIALE IN MEDICINA EINNOVAZIONE NELLA RICERCA CLINICA E METODOLOGICA  \nCICLO  \nXXXVI  \nSCALING ARTIFICIAL INTELLIGENCE IN ENDOSCOPY: FROM MODEL DEVELOPMENT TO MACHINE LEARNING OPERATIONS FRAMEWORKS  \nSettore Scientifico Disciplinare: MED/31 OTORINOLARINGOIATRIA  \nDottorando: Alberto Paderno  \nSupervisore: Prof. Davide Farina  \nSummary  \nINTRODUCTION ....................................................................................................................................................7  \n1. VIDEOMICS: BRINGING DEEP LEARNING TO DIAGNOSTIC ENDOSCOPY ........................................................... 15  \nINTRODUCTION ........................................................................................................................................................... 15  \nMACHINE LEARNING IN ENDOSCOPY................................................................................................................................ 16  \nMACHINE LEARNING APPLICATIONS IN UPPER AERO-DIGESTIVE TRACT ENDOSCOPY ................................................................... 18  \nOral cavity and oropharynx ............................................................................................................................... 19  \nLarynx and hypopharynx ................................................................................................................................... 20  \nFUTURE PERSPECTIVES AND CONCLUSIONS ....................................................................................................................... 22  \nREFERENCES............................................................................................................................................................... 23  \n2. ARTIFICIAL INTELLIGENCE IN CLINICAL ENDOSCOPY: INSIGHTS IN THE FIELD OF VIDEOMICS ...........................29  \nINTRODUCTION ........................................................................................................................................................... 29  \nAIMS OF VIDEOMICS .................................................................................................................................................... 30  \nQuality assessment ........................................................................................................................................... 34  \nClassification ..................................................................................................................................................... 35  \nDetection ........................................................................................................................................................... 37  \nSegmentation .................................................................................................................................................... 39  \nIn-depth characterization .................................................................................................................................. 41  \nFUTURE PERSPECTIVES ................................................................................................................................................. 42  \nREFERENCES............................................................................................................................................................... 44  \n3. DEEP LEARNING IN ENDOSCOPY: THE IMPORTANCE OF STANDARDIZATION ...................................................49  \nINTRODUCTION ........................................................................................................................................................... 49  \nSTUDY DEFINITION....................................................................................................................................................... 50  \nDEFINITION OF PRIMARY OUTCOMES ..................................................","cbCaia1hxeJ2yOJ4","https://ap.wps.com/l/cbCaia1hxeJ2yOJ4","pdf",18873413,1,162,"English","en",105,"# INTRODUCTION\n## VIDEOMICS: BRINGING DEEP LEARNING TO DIAGNOSTIC ENDOSCOPY\n## ARTIFICIAL INTELLIGENCE IN CLINICAL ENDOSCOPY: INSIGHTS IN THE FIELD OF VIDEOMICS\n## DEEP LEARNING IN ENDOSCOPY: THE IMPORTANCE OF STANDARDIZATION\n## DEEP LEARNING FOR AUTOMATIC SEGMENTATION OF ORAL AND OROPHARYNGEAL CANCER USING NARROW BAND IMAGING: PRELIMINARY EXPERIENCE IN A CLINICAL PERSPECTIVE\n# REFERENCES","[{\"question\":\"What is the core goal of the thesis on AI in endoscopy?\",\"answer\":\"To scale artificial intelligence in endoscopy by linking model development with machine learning operations (MLOps) frameworks.\"},{\"question\":\"How does the thesis use videomics in diagnostic endoscopy?\",\"answer\":\"It frames videomics as an approach to bring deep learning to endoscopic diagnosis, addressing stages like quality assessment, classification, detection, segmentation, and in-depth characterization.\"},{\"question\":\"Why does the thesis emphasize standardization in deep learning for endoscopy?\",\"answer\":\"It highlights that consistent study definitions and standardized primary outcomes are necessary for reliable clinical evaluation and comparability across work.\"}]","SCALING ARTIFICIAL INTELLIGENCE IN ENDOSCOPY: FROM MODEL DEVELOPMENT TO MACHINE LEARNING OPERATIONS FRAMEWORKS - Dottorato di ricerca | PDF",1785941504,408,{"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},"scaling-artificial-intelligence-in-endoscopy-from-model-development-to-machine-learning-operations-frameworks-research-doctorate","",{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/scaling-artificial-intelligence-in-endoscopy-from-model-development-to-machine-learning-operations-frameworks-research-doctorate/127767/",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","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the core goal of the thesis on AI in endoscopy?","Question",{"text":75,"@type":76},"To scale artificial intelligence in endoscopy by linking model development with machine learning operations (MLOps) frameworks.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the thesis use videomics in diagnostic endoscopy?",{"text":80,"@type":76},"It frames videomics as an approach to bring deep learning to endoscopic diagnosis, addressing stages like quality assessment, classification, detection, segmentation, and in-depth characterization.",{"name":82,"@type":73,"acceptedAnswer":83},"Why does the thesis emphasize standardization in deep learning for endoscopy?",{"text":84,"@type":76},"It highlights that consistent study definitions and standardized primary outcomes are necessary for reliable clinical evaluation and comparability across work.","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},[93,97,101,105,110,115,118,123,128,131,135],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",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"]