[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"deep-learning-model\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:deep-learning-model":29},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,3,0,[8,55,107],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":13,"acronym":14,"eligibilityCriteria":15,"healthyVolunteers":11,"sex":16,"minAge":4,"maxAge":4,"enrollmentInfo":17,"targetDuration":4,"studyType":20,"phases":21,"briefSummary":23,"conditions":24,"keywords":36,"overallStatus":43,"whyStopped":4,"lastUpdateSubmitDate":44,"lastUpdatePostDateStruct":45,"startDateStruct":48,"completionDateStruct":50,"leadSponsor":52,"locationsCount":4},"100640138","development-and-validation-of-a-deep-learning-model-to-predict-endodontic-retreatment-difficulty-from-periapical-radiographs-100640138",false,"NCT07611279","Development and Validation of a Deep Learning Model to Predict Endodontic Retreatment Difficulty From Periapical Radiographs","Ai Retreatment","Inclusion Criteria:\n\nPeriapical radiographs of maxillary and mandibular molars requiring non-surgical endodontic retreatment will be included. Radiographs should exhibit satisfactory image quality, characterized by adequate sharpness, contrast, and minimal distortion or noise to allow accurate assessment of relevant anatomical and treatment-related features. Images should clearly display the tooth of interest, surrounding periapical structures, and any existing root canal filling materials or restorations.\n\nExclusion Criteria:\n\nDeciduous teeth, non-restorable, non-treated teeth","ALL",{"count":18,"type":19},123,"ESTIMATED","INTERVENTIONAL",[22],"NA","The aim of this study is to develop and evaluate an artificial intelligence-based model capable of analyzing periapical radiographs of maxillary and mandibular molars to predict the difficulty level of non-surgical root canal retreatment. By integrating deep learning techniques with routinely acquired periapical radiographs, this study aims to enhance diagnostic support, improve clinical decision-making, and facilitate appropriate case selection or referral in endodontic practice.",[25,26,27,28,29,30,31,32,33,34,35],"Endodontic Retreatment","Non-surgical Retreatment","Endodontics","AI (Artificial Intelligence)","Deep Learning Model","DIFFICULTY ASSESSMENT","SEPARATED INSTRUMENT","Perforation","Missed Canals","Poor Obturation","Obturation Quality",[37,38,39,40,41,42],"endodontic retreatment","difficulty assessment","endodontics","ai","artificial intelligence","deep learning model","NOT_YET_RECRUITING","2026-05-20",{"date":46,"type":47},"2026-05-28","ACTUAL",{"date":49,"type":19},"2026-07",{"date":51,"type":19},"2027-01",{"name":53,"class":54},"Cairo University","OTHER",{"id":56,"slug":57,"hasResults":11,"nctId":58,"briefTitle":59,"officialTitle":60,"acronym":4,"eligibilityCriteria":61,"healthyVolunteers":62,"sex":16,"minAge":4,"maxAge":4,"enrollmentInfo":63,"targetDuration":65,"studyType":66,"phases":4,"briefSummary":67,"conditions":68,"keywords":85,"overallStatus":96,"whyStopped":4,"lastUpdateSubmitDate":97,"lastUpdatePostDateStruct":98,"startDateStruct":100,"completionDateStruct":102,"leadSponsor":104,"locationsCount":106},"100579293","raman-spectroscopy-based-deep-learning-model-for-early-pan-cancer-early-diagnosis-100579293","NCT06822413","Raman Spectroscopy-Based Deep Learning Model for Early Pan-Cancer Early Diagnosis","A Novel Raman Spectroscopy-Based Method for Pan-Cancers Early Diagnosis Supported by Deep Learning: A Prospective, Single-Arm, Multicentre Study","Inclusion Criteria:\n\n* Histopathological diagnosis of malignant tumors, including colorectal cancer, gastric cancer, hepatic cancer, pancreatic cancer, and esophageal cancer.\n* Patients in normal physiological conditions without any malignant tumors or precancerous lesions.\n* Patients with malignant tumor without recieving any interventions, including chemotherapy, surgery, radiotherapy, immunotherapy or other anti-tumor treatments.\n* Patients with a histopathological diagnosis of any precancerous lesions or non-malignant disease.\n\nExclusion Criteria:\n\n* Patients with metastatic tumors or in the condition with two or more kinds of malignant tumors at the same time\n* Post-cancer treatment patients.",true,{"count":64,"type":19},600,"1 Year","OBSERVATIONAL","The goal of this observational study is to explore whether a Raman-based, deep learning-assisted approach can be used to develop an effective method for early pan-cancer screening. The study includes healthy individuals, patients at risk of cancer, and patients with diagnosed cancers. The main questions it aims to answer are:\n\n* Evaluating the deep-learning model's accuracy and specificity in identifying cancer-specific features in Raman spectral data and determining whether this method can accurately classify patients based on risk.\n* Identifying which model is more adaptable to the Raman spectrum\n* Providing an interpretable analysis of the model-generated diagnosis Participants are already being diagnosed and follow-up to determine the type of cancer.",[69,70,71,72,73,74,75,76,29,77,78,79,80,81,82,83,84],"Cancer Diagnosis","Liver Cancer, Adult","Cancer Screening","Colorectal Cancer (CRC)","Gastric Cancers","Normal Physiology","Pancreatic Cancer, Adult","Raman Spectroscopy","Esophageal Cancer","Malignant Tumours","Precancerous Conditions","Pancreatitis","Adenoma Colon Polyp","Gastric Ulcer","Oesophagitis","Cirrhoses, Liver",[86,87,71,76,88,89,90,77,91,92,80,93,94,83,95],"Pan-cancer","Deep Learning Models","Colorectal Cancer","Pancreatic Cancer","Gastric Cancer","malignant tumour","Precancerous Condtions","Colorectal Adenoma","Gastirc Ulcer","Cirrhoses","RECRUITING","2025-04-19",{"date":99,"type":47},"2025-04-24",{"date":101,"type":47},"2022-09-01",{"date":103,"type":19},"2025-07-28",{"name":105,"class":54},"Second Affiliated Hospital, School of Medicine, Zhejiang University",4,{"id":108,"slug":109,"hasResults":11,"nctId":110,"briefTitle":111,"officialTitle":112,"acronym":4,"eligibilityCriteria":113,"healthyVolunteers":62,"sex":16,"minAge":114,"maxAge":115,"enrollmentInfo":116,"targetDuration":118,"studyType":66,"phases":4,"briefSummary":119,"conditions":120,"keywords":4,"overallStatus":43,"whyStopped":4,"lastUpdateSubmitDate":125,"lastUpdatePostDateStruct":126,"startDateStruct":128,"completionDateStruct":130,"leadSponsor":132,"locationsCount":134},"100572544","deep-learning-model-for-predicting-a-peripheral-venous-waveform-based-pulse-pressure-variation-100572544","NCT06734650","Deep Learning Model for Predicting a Peripheral Venous Waveform-based Pulse Pressure Variation","Development and Validation of a Peripheral Venous Waveform-based Pulse Pressure Variation Calculating Deep Learning Model","Inclusion Criteria:\n\n* Patients who voluntarily agreed and signed the written informed consent form before participating in this study\n* Adult aged 19 years or older\n* American Society of Anesthesiologists physical class (ASA) 1-3\n* Patients scheduled for elective hepatectomy under general anesthesia\n* Patients who require arterial pressure monitoring and additional peripheral venous access for routine anesthesia preparation\n* Non-smokers with normal pulmonary function\n\nExclusion Criteria:\n\n* Patients with abnormal findings on electrocardiogram before surgery\n* Patients who cannot undergo peripheral venous puncture","19 Years","80 Years",{"count":117,"type":19},150,"1 Day","Pulse pressure variation is a monitoring index that indicates the response to fluid therapy in patients receiving mechanical ventilation, and is used as a reference for patients with unstable hemodynamic conditions. However, it is invasive because it requires arterial puncture to collect it. In a previous study by the investigators, the investigators developed and verified an artificial intelligence model that predicts stroke volume variation, in real time using only the central venous pressure waveform. However, since a large vein such as the jugular vein must be punctured to collect the central venous pressure waveform, it is still invasive, and its clinical utility is low. Therefore, in this study, the investigators collected waveforms from peripheral veins that are less invasive and can be a wide range of applications because all surgical patients have them. The investigators aimed to develop and verify an artificial intelligence model that predicts pulse pressure variation obtained from peripheral venous waveforms .",[121,122,123,124,29],"Peripheral Vein","Arterial Wave Reflections","Pulse Pressure Variation","Stroke Volume Variation","2024-12-12",{"date":127,"type":47},"2024-12-16",{"date":129,"type":19},"2024-12-28",{"date":131,"type":19},"2026-11-28",{"name":133,"class":54},"Seoul National University Bundang Hospital",1]