[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"ai-based-dissection-trajectory-prediction-system\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:ai-based-dissection-trajectory-prediction-system":29},{"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":4,"eligibilityCriteria":15,"healthyVolunteers":11,"sex":16,"minAge":17,"maxAge":18,"enrollmentInfo":19,"targetDuration":4,"studyType":22,"phases":23,"briefSummary":25,"conditions":26,"keywords":30,"overallStatus":38,"whyStopped":4,"lastUpdateSubmitDate":39,"lastUpdatePostDateStruct":40,"startDateStruct":43,"completionDateStruct":45,"leadSponsor":47,"locationsCount":4},"100651225","clinical-application-of-an-ai-based-dissection-trajectory-prediction-system-adtps-in-endoscopic-submucosal-dissection-100651225",false,"NCT07757906","Clinical Application of an AI-based Dissection Trajectory Prediction System (ADTPS) in Endoscopic Submucosal Dissection","Clinical Application of an AI-based Dissection Trajectory Prediction System (ADTPS) in Endoscopic Submucosal Dissection: A Prospective Paired Diagnostic Study and a Randomized Controlled Clinical Trial","Inclusion Criteria:\n\n* Chinese patients aged 18-80 years.\n\nLesions meeting ESD indications for esophageal, gastric, or colorectal early cancer or high-grade intraepithelial neoplasia, as defined by:\n\nNon-invasive tumors regardless of size; or\n\nDifferentiated-type intramucosal carcinoma without ulceration, regardless of size; or\n\nDifferentiated-type intramucosal carcinoma with ulceration and a diameter ≤3 cm; or\n\nUndifferentiated-type intramucosal carcinoma without ulceration and a diameter ≤2 cm.\n\nPlanned to undergo ESD treatment.\n\nNo prior treatment for the lesion (including ESD, surgery, radiotherapy, chemotherapy, etc.).\n\nPlatelet count \\>100 × 10⁹\u002FL and PT-INR \\\u003C1.5, with antiplatelet agents (aspirin, clopidogrel, etc.) discontinued for at least 5 days.\n\nAmerican Society of Anesthesiologists (ASA) physical status grade I or II.\n\nVoluntarily signed informed consent.\n\nExclusion Criteria:\n\n* Patients currently undergoing dialysis.\n\nPatients with severe cardiopulmonary disease or other severe comorbidities that may increase the risk of the ESD procedure.\n\nPregnant or breastfeeding women.","ALL","18 Years","80 Years",{"count":20,"type":21},160,"ESTIMATED","INTERVENTIONAL",[24],"NA","In this prospective paired diagnostic study and single-center, randomized controlled trial, patients with early esophageal squamous neoplasia or high-grade intraepithelial neoplasia meeting the inclusion and exclusion criteria will be enrolled in a paired diagnostic cohort (60 patients) and subsequently randomly assigned (1:1) to receive endoscopic submucosal dissection (ESD) with AI-based Dissection Trajectory Prediction System (ADTPS) guidance or conventional ESD (without AI). Clinical data and operator workload scores (NASA-TLX) are collected during the procedure, and postoperative follow-up assessments are performed at days 1, 3, 7, and 14. The study aims to analyze the impact of ADTPS on the mean single-dissection time and operator workload in patients undergoing ESD by comparing the efficacy differences between the experimental and control groups. Additionally, the study investigates the effects of ADTPS on other postoperative complications including R0 resection rate, muscularis propria injury, intraoperative bleeding, perforation (acute and delayed), and total procedure time; conducts a comparative analysis of the safety and efficiency of AI-assisted versus conventional ESD; and develops effective clinical strategies for optimizing dissection trajectory and reducing complications in endoscopic submucosal dissection.",[27,28,29],"Esophageal Squamous Cell Carcinoma (ESCC)","High-Grade Intraepithelial Neoplasia","AI-Based Dissection Trajectory Prediction System",[31,32,33,34,35,36,37],"Artificial intelligence","R0 resection","Procedural efficiency","NASA-TLX","Randomized controlled trial","Dissection trajectory prediction","Endoscopic submucosal dissection","NOT_YET_RECRUITING","2026-08-06",{"date":41,"type":42},"2026-08-11","ACTUAL",{"date":44,"type":21},"2026-07-29",{"date":46,"type":21},"2027-10-01",{"name":48,"class":49},"Qilu Hospital of Shandong University","OTHER"]