[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100634172":3},{"organization":4,"armGroups":7,"interventions":10,"overallOfficials":10,"centralContacts":15,"locations":21,"responsibleParty":33,"collaborators":35,"id":37,"slug":10,"hasResults":38,"nctId":39,"briefTitle":40,"officialTitle":41,"acronym":10,"eligibilityCriteria":42,"healthyVolunteers":43,"sex":44,"minAge":10,"maxAge":10,"enrollmentInfo":45,"targetDuration":48,"studyType":49,"phases":10,"briefSummary":50,"conditions":51,"keywords":10,"overallStatus":60,"whyStopped":10,"lastUpdateSubmitDate":61,"lastUpdatePostDateStruct":62,"startDateStruct":65,"completionDateStruct":67,"leadSponsor":69,"locationsCount":70},{"fullName":5,"class":6},"Universitair Ziekenhuis Brussel","OTHER",[8,12],{"label":9,"type":10,"description":11,"interventionNames":10},"Prospective",null,"Prospective Cohort",{"label":13,"type":10,"description":14,"interventionNames":10},"Restrospective","Retrospective Cohort",[16],{"name":17,"role":18,"phone":19,"phoneExt":10,"email":20},"Hugo Carvalho, MD, PhD","CONTACT","+32 50 45 24 19","hugo.nogueiracarvalho@azsintjan.be",[22],{"facility":23,"status":10,"city":24,"state":10,"zip":25,"country":26,"cosmosGeoPoint":27,"geoPoint":32,"contacts":10},"AZ Sint-Jan AV","Bruges","8000","Belgium",{"type":28,"coordinates":29},"Point",[30,31],3.22424,51.20892,{"lat":31,"lon":30},{"type":34,"investigatorFullName":10,"investigatorTitle":10,"investigatorAffiliation":10,"oldNameTitle":10,"oldOrganization":10},"SPONSOR",[36],{"name":23,"class":6},"100634172",false,"NCT07536230","Deep Learning Framework for Continuous Depth of Anesthesia Forecasting","Validation of a Deep Learning Framework for Continuous Forecasting of Pharmacodynamic Responses and Physiological Trajectories During General Anesthesia","Inclusion Criteria:\n\n* Patients scheduled for elective surgery requiring general anesthesia.\n* Procedures requiring continuous depth of anesthesia monitoring (BIS).\n\nExclusion Criteria:\n\n\\- Procedures where the primary anesthetic plan does not involve continuous electronic data capture.",true,"ALL",{"count":46,"type":47},115,"ESTIMATED","1 Day","OBSERVATIONAL","The integration of Artificial Intelligence (AI) in anesthesiology offers the potential to shift patient monitoring from reactive to predictive. Deep learning architectures, specifically Long Short-Term Memory (LSTM) networks, excel at processing complex, time-series data to forecast future clinical states.\n\nWhile standard PK\u002FPD models (such as the state of the art Eleveld model for Propofol and Remifentanil) estimate target-site drug concentrations (Ce), they do not account for real-time, patient-specific dynamic responses. This study aims to deploy an AI framework designed to predict future physiological states.",[52,53,54,55,56,57,58,59],"BIS","BIS-EEG","Artifical Intelligence","Intraoperative","Machine Learning","Anesthesia","Anesthesia Awareness","Predictive Model","NOT_YET_RECRUITING","2026-04-10",{"date":63,"type":64},"2026-04-17","ACTUAL",{"date":66,"type":47},"2026-06-01",{"date":68,"type":47},"2026-09-01",{"name":5,"class":6},1]