[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100398278":3},{"organization":4,"armGroups":7,"interventions":8,"overallOfficials":13,"centralContacts":18,"locations":28,"responsibleParty":46,"collaborators":7,"id":49,"slug":50,"hasResults":51,"nctId":52,"briefTitle":53,"officialTitle":54,"acronym":55,"eligibilityCriteria":56,"healthyVolunteers":57,"sex":58,"minAge":59,"maxAge":7,"enrollmentInfo":60,"targetDuration":7,"studyType":63,"phases":7,"briefSummary":64,"conditions":65,"keywords":71,"overallStatus":30,"whyStopped":7,"lastUpdateSubmitDate":77,"lastUpdatePostDateStruct":78,"startDateStruct":81,"completionDateStruct":83,"leadSponsor":85,"locationsCount":86},{"fullName":5,"class":6},"Munich Leukemia Laboratory","INDUSTRY",null,[9],{"type":10,"name":11,"description":12,"armGroupLabels":7,"otherNames":7},"DIAGNOSTIC_TEST","Automated AI-Guided Diagnosis of Hematological Malignancies","In BELUGA, we want to investigate whether the automated analysis of blood (from peripheral blood and bone marrow aspirates) smears and flow-cytometry-based analyses can provide a benefit for diagnostic quality and, ultimately, patient care.",[14],{"name":15,"affiliation":16,"role":17},"Wolfgang Kern, Prof. Dr.","MLL Munich Leukemia Laboratory","PRINCIPAL_INVESTIGATOR",[19,24],{"name":20,"role":21,"phone":22,"phoneExt":7,"email":23},"Adam Wahida, MD","CONTACT","+49 (0)89 99017 338","adam.wahida@mll.com",{"name":25,"role":21,"phone":26,"phoneExt":7,"email":27},"Torsten Haferlach, Prof. Dr.Dr.","+49 (0)89 99017 100","torsten.haferlach@mll.com",[29],{"facility":16,"status":30,"city":31,"state":32,"zip":33,"country":34,"countryCode":35,"cosmosGeoPoint":36,"geoPoint":41,"contacts":42},"RECRUITING","Munich","Bavaria","81377","Germany","DE",{"type":37,"coordinates":38},"Point",[39,40],11.57549,48.13743,{"lat":40,"lon":39},[43,44],{"name":20,"role":21,"phone":7,"phoneExt":7,"email":23},{"name":45,"role":21,"phone":7,"phoneExt":7,"email":27},"Torsten Haferlach, Prof.Dr.Dr.",{"type":17,"investigatorFullName":47,"investigatorTitle":48,"investigatorAffiliation":5,"oldNameTitle":7,"oldOrganization":7},"Torsten Haferlach","Prof. Dr. Dr.","100398278","better-leukemia-diagnostics-through-ai-beluga-100398278",false,"NCT04466059","Better Leukemia Diagnostics Through AI (BELUGA)","A Case-Control Study To Determine The Suitability Of Artificial Intelligence For Leukemia Diagnostics","BELUGA","Inclusion Criteria:\n\n* Patients having been diagnosed with a suspected hematological disorder\n* The suspected diagnoses constitute a primary diagnosis\n* Only samples of patients min.18 years of age will be used\n* Samples must suffice quality attributes which are denoted in \"Exclusion Criteria\"\n\nExclusion Criteria:\n\n* The sample is not fit for state-of-the-art diagnosis or fails initial quality control. For quality insurance, we will exclude samples in heparin- instead of EDTA. Samples with damage due to atmospheric reasons (freeze-thaw damage or elevated temperature) will be excluded.\n* Samples with too scarce material jeopardizing routine gold-standard diagnosis will be excluded.\n* Bone marrow aspirates without sufficient material to assess malignant or healthy hematopoiesis.",true,"ALL","18 Years",{"count":61,"type":62},25000,"ESTIMATED","OBSERVATIONAL","To the best of our knowledge, BELUGA will be the first prospective trial investigating the usefulness of deep learning-based hematologic diagnostic algorithms. Taking advantage of an unprecedented collection of diagnostic samples consisting of flow cytometry datapoints and digitalized blood-smears, categorization of yet undiagnosed patient samples will prospectively be compared to current state-of-the-art diagnosis at the Munich Leukemia Laboratory (hereafter MLL). In total, a collection of 25,000 digitalized blood smears and 25,000 flow cytometry datapoints will be prospectively used to train an AI-based deep neuronal network for correct categorization. Subsequently, the superiority will be challenged for the primary endpoints: sensitivity and specificity of diagnosis, most probable diagnosis, and time to diagnose. The secondary endpoints will compare the consequences regarding further diagnostic work-up and, thus, clinical decision making between routine diagnosis and AI guided diagnostics. BELUGA will set the stage for the introduction of AI-based hematologic diagnostics in a real-world setting.",[66,67,68,69,70],"Hematologic Malignancy","Leukemia","Minimal Residual Disease","Lymphoma","Blood Cancer",[72,73,74,75,76],"hematology","laboratory medicine","AI-based diagnostics","artificial intelligence","deep neuronal networks","2024-12-14",{"date":79,"type":80},"2024-12-17","ACTUAL",{"date":82,"type":80},"2020-01-05",{"date":84,"type":62},"2025-07-31",{"name":5,"class":6},1]