[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100429079":3},{"organization":4,"outcomesModule":7,"designInfo":32,"detailedDescription":35,"studyPopulation":36,"armGroups":37,"interventions":31,"overallOfficials":44,"centralContacts":48,"locations":57,"responsibleParty":72,"collaborators":74,"id":78,"slug":31,"hasResults":79,"nctId":80,"briefTitle":81,"officialTitle":82,"acronym":31,"eligibilityCriteria":83,"healthyVolunteers":84,"sex":85,"minAge":86,"maxAge":87,"enrollmentInfo":88,"targetDuration":31,"studyType":91,"phases":31,"briefSummary":92,"conditions":93,"keywords":31,"overallStatus":60,"whyStopped":31,"lastUpdateSubmitDate":95,"lastUpdatePostDateStruct":96,"startDateStruct":99,"completionDateStruct":101,"leadSponsor":103,"locationsCount":104},{"fullName":5,"class":6},"Hull University Teaching Hospitals NHS Trust","OTHER_GOV",{"primaryOutcomes":8,"secondaryOutcomes":12,"otherOutcomes":31},[9],{"measure":10,"description":10,"timeFrame":11},"To develop and train a convolutional neural network to detect and characterise disease severity of inflammatory bowel disease during endoscopy","5 years",[13,16,19,22,25,28],{"measure":14,"description":15,"timeFrame":11},"a) To explore whether Artificial Intelligence can predict response to IBD therapies","To explore whether Artificial Intelligence can predict response to IBD therapies",{"measure":17,"description":18,"timeFrame":11},"b) To develop an endoscopic image repository to advance training and standardisation in endoscopic detection and characterisation of IBD.","b) To develop an endoscopic image repository to advance training and standardisation",{"measure":20,"description":21,"timeFrame":11},"c) To develop and assess methodologies for training and quality assurance of IBD diagnostic endoscopy","To develop and assess methodologies for training and quality assurance of IBD",{"measure":23,"description":24,"timeFrame":11},"d) To evaluate comparisons in endoscopic image interpretation between endoscopist's","To evaluate comparisons in endoscopic image interpretation between endoscopist's",{"measure":26,"description":27,"timeFrame":11},"e) To develop deep learning algorithms and computer vision techniques to allow for automated measurement of quality metrics in endoscopy for IBD","To develop deep learning algorithms and computer vision techniques to allow for automated measurement of quality metrics in endoscopy for IBD",{"measure":29,"description":30,"timeFrame":11},"f) To create a future robust research platform to ensure the above objectives are continuously developed as novel imaging techniques emerge over time.","To create a future robust research platform to ensure the above objectives are continuously developed as novel imaging techniques emerge over time.",null,{"allocation":31,"interventionModel":31,"interventionModelDescription":31,"primaryPurpose":31,"observationalModel":33,"timePerspective":34,"maskingInfo":31},"OTHER","CROSS_SECTIONAL","To develop and train a Convolutional Neural Network to detect and characterize disease severity in inflammatory bowel disease during endoscopy. This initiative will inevitably establish a high-quality large image database. Our secondary study aims are therefore to use the images we collect to advance the field of deep learning and computer aided diagnosis in inflammatory bowel disease by establishing an image database. This will involve developing a framework combining deep learning and computer vision algorithms. The ultimate aim is to use the image database to produce high impact research outcomes and training resources leading to an improvement in the quality of endoscopy performed, reduce inter-observer variability in disease assessment and a reduction in missed bowel cancer rates and associated mortality.","Inflammatory Bowel Disease (IBD) affects at least one in 250 people of the UK population and the prevalence is rising.",[38,41],{"label":39,"type":31,"description":40,"interventionNames":31},"Main group","Patients with\u002Fsuspected Inflammatory Bowel Disease attending for an endoscopic procedure",{"label":42,"type":31,"description":43,"interventionNames":31},"Control","Patients without Inflammatory Bowel disease attending for an endoscopic procedure",[45],{"name":46,"affiliation":5,"role":47},"Shaji Sebastian","PRINCIPAL_INVESTIGATOR",[49,53],{"name":46,"role":50,"phone":51,"phoneExt":31,"email":52},"CONTACT","01482 816764","shaji.sebastian@hey.nhs.uk",{"name":54,"role":50,"phone":55,"phoneExt":31,"email":56},"Laurence Lovat","02076799606","l.lovat@ucl.ac.uk",[58],{"facility":59,"status":60,"city":61,"state":62,"zip":63,"country":64,"countryCode":65,"cosmosGeoPoint":66,"geoPoint":71,"contacts":31},"Hull Royal Infirmary","RECRUITING","Hull","East Yorkshire","HU3 2JZ","United Kingdom","UK",{"type":67,"coordinates":68},"Point",[69,70],-0.33525,53.7446,{"lat":70,"lon":69},{"type":73,"investigatorFullName":31,"investigatorTitle":31,"investigatorAffiliation":31,"oldNameTitle":31,"oldOrganization":31},"SPONSOR",[75],{"name":76,"class":77},"Wellcome\u002FEPSRC Centre for Interventional and Surgical Sciences, University College London","UNKNOWN","100429079",false,"NCT04867408","Endoscopic Severity Image Recognition to Advance Research and Training in Inflammatory Bowel Disease (EVEREST - IBD)","EVEREST - IBD: Endoscopic Severity Image Recognition to Advance Research and Training in Inflammatory Bowel Disease","Inclusion Criteria:\n\n* • Any adult patient aged 16 years or older who has consented to undergo endoscopic investigation where images are captured as part of routine clinical care.\n\nExclusion Criteria:\n\n* • Any patient under the age of 16\n\n  * Patients who are unable to give informed consent to undergo endoscopic investigation or those who do not wish their pseudo-anonymised images to be used",true,"ALL","16 Years","99 Years",{"count":89,"type":90},4000,"ESTIMATED","OBSERVATIONAL","To develop and train a convolutional neural network to detect and characterize disease severity of inflammatory bowel disease during endoscopy",[94],"Inflammatory Bowel Disease 1","2024-11-14",{"date":97,"type":98},"2024-11-15","ACTUAL",{"date":100,"type":98},"2021-09-17",{"date":102,"type":90},"2031-09",{"name":5,"class":6},1]