[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"metastasis-to-liver\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:metastasis-to-liver":32},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,1,0,[8],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":15,"eligibilityCriteria":16,"healthyVolunteers":17,"sex":18,"minAge":19,"maxAge":4,"enrollmentInfo":20,"targetDuration":4,"studyType":23,"phases":24,"briefSummary":26,"conditions":27,"keywords":33,"overallStatus":36,"whyStopped":4,"lastUpdateSubmitDate":37,"lastUpdatePostDateStruct":38,"startDateStruct":41,"completionDateStruct":43,"leadSponsor":45,"locationsCount":48},"100540464","assessment-of-liver-diseases-using-a-deep-learning-approach-based-on-ultrasound-rf-data-100540464",false,"NCT06317181","Assessment of Liver Diseases Using a Deep-Learning Approach Based on Ultrasound RF-Data","Acquisition and Frequency Spectroscopic Evaluation of Broadband Clinical Ultrasound Raw Data for Liver Cirrhosis and Focal Pathologies Using Neural Networks for Tissue and Pathology Differentiation","LivSPECTRUS","Inclusion Criteria:\n\n* scheduled for an ultrasound investigation by an independent physician\n* signed declaration of consent\n\nExclusion Criteria:\n\n* smaller interventions in the same liver during the last 2 Week (for example liver biopsy)\n* contrast enhanced ultrasound less than a day ago\n* major intervention at the liver (for example partial resection)",true,"ALL","18 Years",{"count":21,"type":22},200,"ESTIMATED","INTERVENTIONAL",[25],"NA","The goal of this clinical trial is to test the performance of neuronal networks trained on ultrasonic raw Data (=radiofrequency data) for the assessment of liver diseases in patients undergoing a clinical ultrasound examination. The general feasibility is currently evaluated in a retrospective cohort.\n\nThe main questions the study aims to answer are:\n\n* Can a neuronal network trained on RF Data perform equally good as elastography in the assessment of diffuse liver diseases?\n* Can a neuronal network trained on RF Data perform better than a neuronal network trained on b-mode images in the assessment of diffuse liver diseases?\n* Can a neuronal network trained on RF Data distinguish focal pathologies in the liver from healthy tissue?\n\nTo answer these questions participants with a clinically indicated fibroscan will undergo:\n\n* a clinical elastography in Case ob suspected diffuse liver disease\n* a reliable ground truth (if normal ultrasound is not sufficient e.g. contrast enhanced ultrasound, biopsy, MRI or CT) in case of focal liver diseases, depending on the standard routine of the participating center\n* a clinical ultrasound examination during which b-mode images and the corresponding RF-Data sets are captured",[28,29,30,31,32],"Artificial Intelligence","Ultrasonography","Elasticity Imaging Techniques","Liver Diseases","Metastasis to Liver",[34,35],"Quantitative Ultrasound","Radiofrequency Data","RECRUITING","2025-08-18",{"date":39,"type":40},"2025-08-20","ACTUAL",{"date":42,"type":40},"2024-04-01",{"date":44,"type":22},"2025-12",{"name":46,"class":47},"Technische Universität Dresden","OTHER",4]