[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"chest-x-ray-for-clinical-evaluation\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:chest-x-ray-for-clinical-evaluation":33},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,4,0,[8,51,80,107],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":4,"eligibilityCriteria":15,"healthyVolunteers":11,"sex":16,"minAge":17,"maxAge":4,"enrollmentInfo":18,"targetDuration":4,"studyType":21,"phases":22,"briefSummary":24,"conditions":25,"keywords":4,"overallStatus":38,"whyStopped":4,"lastUpdateSubmitDate":39,"lastUpdatePostDateStruct":40,"startDateStruct":43,"completionDateStruct":45,"leadSponsor":47,"locationsCount":50},"100637399","unilateral-ventilation-on-cardiopulmonary-bypass-during-cardiac-surgery-100637399",false,"NCT07612709","Unilateral Ventilation on Cardiopulmonary Bypass During Cardiac Surgery","Unilateral Ventilation on Cardiopulmonary Bypass During Cardiac Surgery in Patients at Increased Risk for Severe Postoperative Pulmonary Complications","Inclusion Criteria:\n\n* Patients at increased risk for postoperative pulmonary complications\n* Major elective cardiac surgery\n* Prolonged duration of cardiopulmonary bypass\n* Patients older than 65 years of age\n* Informed consent\n\nExclusion Criteria:\n\n* Emergency\n* Urgent procedures\n* Patients with implanted pacemakers\n* Patients with internal cardioverter\u002Fdefibrillators\n* Decompensated cardiac disease\n* Pulmonary disease\n* Recent pneumonia\n* Need for temporary perioperative mechanical support\n* Patients not willing to participate\n* Treatment with inhaled nitric oxide","ALL","65 Years",{"count":19,"type":20},45,"ESTIMATED","INTERVENTIONAL",[23],"NA","This study investigates if single lung ventilation on cardiopulmonary bypass can mitigate postoperative lung water accumulation determined by lung ultrasound in the ventilated lung as compared to the non-ventilated lung in patients at high-risk for developing severe pulmonary complications after cardiac surgery.",[26,27,28,29,30,31,32,33,34,35,36,37],"Lung Protective Ventilation","Cardiopulmonary Bypass","High-risk Cardiac Patients","Lung Ultrasound","Lung Water Assessment","Pulmonary Complications in Surgical Patients","Single-lung Ventilation","Chest X-ray for Clinical Evaluation","Electrical Impedance Tomography (EIT)","Lung Compliance","Oxygenation Indices","Biomarkers of Vascular Endothelial Injury","NOT_YET_RECRUITING","2026-05-24",{"date":41,"type":42},"2026-05-29","ACTUAL",{"date":44,"type":20},"2026-10-01",{"date":46,"type":20},"2029-07-31",{"name":48,"class":49},"Medical University of Vienna","OTHER",1,{"id":52,"slug":53,"hasResults":11,"nctId":54,"briefTitle":55,"officialTitle":56,"acronym":4,"eligibilityCriteria":57,"healthyVolunteers":11,"sex":16,"minAge":58,"maxAge":59,"enrollmentInfo":60,"targetDuration":4,"studyType":62,"phases":4,"briefSummary":63,"conditions":64,"keywords":67,"overallStatus":38,"whyStopped":4,"lastUpdateSubmitDate":71,"lastUpdatePostDateStruct":72,"startDateStruct":74,"completionDateStruct":76,"leadSponsor":78,"locationsCount":4},"100632935","chest-x-rays-for-early-detection-of-congenital-heart-disease-in-newborns-100632935","NCT07520149","Chest X-Rays for Early Detection of Congenital Heart Disease in Newborns","Evaluation of Chest X-Ray Findings in Early Detection of Congenital Heart Disease in Neonates","Inclusion Criteria:\n\n* Neonates aged 0-28 days admitted to the NICU with X-ray findings suspecting CHD during the study period.\n* Neonates presenting with clinical signs suggestive of congenital heart disease, including: cyanosis (central or peripheral), heart murmur detected on auscultation, tachypnea, or respiratory distress.\n* Neonates with abnormal pulse oximetry findings (oxygen saturation \\\u003C95% in room air).\n* Neonates with abnormal antenatal ultrasound findings suggesting cardiac anomalies.\n\nExclusion Criteria:\n\n* Infants aged more than 28 days.\n* Infants with no clinical or radiological evidence of CHD.","0 Days","28 Days",{"count":61,"type":20},60,"OBSERVATIONAL","Congenital heart disease (CHD) is a common condition affecting newborns that can be serious if not caught early. While echocardiograms are the best way to diagnose CHD, they are not always immediately available. Chest X-rays are widely available and are often the first test used when a newborn has breathing problems, a heart murmur, or a bluish skin tint.\n\nThe purpose of this study is to determine how accurate standard chest X-rays are at finding congenital heart disease in newborns.\n\nResearchers will observe 60 newborns (up to 28 days old) admitted to the Neonatal Intensive Care Unit (NICU) who have signs or symptoms that suggest they might have a heart problem. Each baby in the study will receive a standard chest X-ray within 24 hours of their clinical presentation. Within 72 hours, they will also receive an echocardiogram, which is the standard, definitive test used to confirm if there is a heart defect.\n\nBy comparing the initial chest X-ray results to the final echocardiogram results, researchers hope to figure out exactly which X-ray patterns are best at predicting specific heart diseases. This could help doctors make faster decisions about treating newborns, potentially reducing delays in diagnosis and improving care.",[65,33,66],"Congenital Heart Disease","Neonates",[65,68,66,69,70],"Chest X-Ray","Echocardiography","Chest Radiography","2026-04-03",{"date":73,"type":42},"2026-04-09",{"date":75,"type":20},"2026-05",{"date":77,"type":20},"2027-06",{"name":79,"class":49},"Assiut University",{"id":81,"slug":82,"hasResults":11,"nctId":83,"briefTitle":84,"officialTitle":85,"acronym":4,"eligibilityCriteria":86,"healthyVolunteers":87,"sex":16,"minAge":4,"maxAge":4,"enrollmentInfo":88,"targetDuration":90,"studyType":62,"phases":4,"briefSummary":91,"conditions":92,"keywords":93,"overallStatus":38,"whyStopped":4,"lastUpdateSubmitDate":98,"lastUpdatePostDateStruct":99,"startDateStruct":101,"completionDateStruct":103,"leadSponsor":105,"locationsCount":5},"100631174","x-ray-assisted-diagnostic-system-100631174","NCT07497243","X-ray Assisted Diagnostic System","Construction and Clinical Application of an X-ray AI-Aided Diagnosis System: A Randomized Controlled Trial","Inclusion Criteria:\n\n* Clinically suspected thoracic diseases (such as pneumonia, tuberculosis, or lung cancer) requiring X-ray diagnosis；\n* Patients providing written informed consent for research data use；\n* Complete clinical records (including chief complaints, medical history, and laboratory test results)\n\nExclusion Criteria:\n\n* Substandard X-ray image quality (including severe motion artifacts, over-\u002Funderexposure, or missing anatomical structures)\n* Pregnant or lactating women",true,{"count":89,"type":20},16000,"4 Weeks","X-ray examination is one of the most commonly used imaging modalities, especially chest X-ray, which is routinely performed for hospitalized patients. However, due to the low density resolution of X-ray images, radiologists' ability to diagnose diseases-particularly small lesions-is often affected. Studies have shown that the diagnostic accuracy of radiologists using chest X-rays is only around 70%, which does not meet clinical demands.\n\nBased on this, we developed an artificial intelligence model to assist radiologists in interpreting X-ray images and generating reports, with the aim of improving diagnostic accuracy and reducing interpretation time.",[33],[94,95,96,97],"X-Ray","AI","Chest diseases","Accuracy","2026-03-22",{"date":100,"type":42},"2026-03-27",{"date":102,"type":20},"2026-05-01",{"date":104,"type":20},"2026-11-30",{"name":106,"class":49},"Union Hospital, Tongji Medical College, Huazhong University of Science and Technology",{"id":108,"slug":109,"hasResults":11,"nctId":110,"briefTitle":111,"officialTitle":111,"acronym":4,"eligibilityCriteria":112,"healthyVolunteers":87,"sex":16,"minAge":113,"maxAge":114,"enrollmentInfo":115,"targetDuration":4,"studyType":62,"phases":4,"briefSummary":117,"conditions":118,"keywords":121,"overallStatus":38,"whyStopped":4,"lastUpdateSubmitDate":124,"lastUpdatePostDateStruct":125,"startDateStruct":127,"completionDateStruct":129,"leadSponsor":131,"locationsCount":50},"100624132","clinical-study-on-an-artificial-intelligence-assisted-chest-radiograph-model-based-on-big-data-and-deep-learning-for-early-detection-of-kawasaki-disease-100624132","NCT07405658","Clinical Study on an Artificial Intelligence-Assisted Chest Radiograph Model Based on Big Data and Deep Learning for Early Detection of Kawasaki Disease","Inclusion Criteria:\n\n1. Case group\n\n   * The age of seeking medical treatment is less than or equal to 18 years old; ·The medical record system diagnosis contains the diagnosis of \"Kawasaki Disease\", \"mucocutaneous lymph node syndrome\" or \"IVIG non-response Kawasaki disease\"\n   * At least one complete chest X-ray examination data (images and reports) is available during the same hospitalization\n2. Control group\n\n   * The age of seeking medical treatment is less than or equal to 18 years old\n   * The same period as the case group\n   * Fever lasts for 3 days or more\n   * Rule out the possibility of diagnosing Kawasaki disease\n\nExclusion Criteria:\n\n1. Case group\n\n   * Chest X-ray quality issues: Severe artifacts, overexposure\u002Funderexposure leading to inability to assess key structures\n   * Incomplete clinical information, including lack of chest X-ray examination, laboratory tests, and unclear days of fever Inability to determine the final diagnosis (such as loss to follow-up, diagnosis in doubt)\n2. Control group\n\n   * Chest X-ray quality issues: Severe artifacts, overexposure\u002Funderexposure leading to inability to assess key structures\n   * Incomplete clinical information, including lack of chest X-ray examination, laboratory tests, and unclear days of fever\n   * Inability to make a clear final diagnosis (such as loss to follow-up, questionable diagnosis)","0 Years","18 Years",{"count":116,"type":20},20000,"The goal of this observational study is to develop an AI-based early warning system for Kawasaki Disease (KD) using chest X-rays (CXR) in children diagnosed with Kawasaki Disease. The main question\\[s\\] it aims to answer are:\n\n1. Can AI modeling of CXR features help identify high-risk KD patients earlier than current diagnostic methods?\n2. Can the AI system predict the optimal IVIG treatment window and coronary artery risks in KD patients?\n\nParticipants will:\n\nProvide retrospective data on chest X-rays and clinical data (CRP, coronary ultrasound, etc.) Allow analysis of CXR features using deep learning models to extract relevant patterns Have their data incorporated into a federated learning model to ensure privacy and data security",[119,33,120],"Kawasaki Disease","Mucocutaneous Lymph Node Syndrome",[119,122,123],"Artificial Intelligence","Mucocutaneous lymph node syndrome","2026-02-10",{"date":126,"type":42},"2026-02-12",{"date":128,"type":20},"2026-02-01",{"date":130,"type":20},"2027-12-31",{"name":132,"class":49},"Xinhua Hospital, Shanghai Jiao Tong University School of Medicine"]