Clinical Research Directory

Serial Coronary CTA-based Plaque Progression Detection for Management of Coronary Heart Disease

The primary objective of this study is to evaluate whether a management strategy based on coronary computed tomography angiography (CCTA) for patients with non-obstructive coronary artery disease can improve the LDL-C target achievement rate compared to a traditional management strategy without follow-up CCTA, thereby reducing the incidence of major adverse cardiovascular and cerebrovascular events over a 3 years period, including all-cause mortality, myocardial infarction, ischemia driven revascularation and stroke.

Further Lipid-Lowering With PCSK9 Inhibitors for Cardiovascular Outcomes in High-Risk Coronary Plaques Assessed by CT Angiography

The primary objective was to evaluate the effect of PCSK9 inhibitors in addition to the background lipid-modifying therapy (LMT), compared with standard LMT in terms of clinical outcomes in patients with coronary CT angiography (CCTA)-detected high-risk plaques.

IDEAL Study: Blinded RCT for the Impact of AI Model for Cerebral Aneurysms Detection on Patients' Diagnosis and Outcomes

This study (IEDAL study) intends to prospectively enroll more than 6450 patients who will undergo head CT angiography (CTA) scanning in the outpatient clinic. It will be carried out in 21 hospitals in more than 10 provinces in China. The patient's head CTA images will be randomly assigned to the True-AI and Sham-AI group with a ratio of 1:1, and the patients and radiologists are unaware of the allocation. The primary outcomes are sensitivity and specificity of detecting intracranial aneurysms. The secondary outcomes focus on the prognosis and outcomes of the patients.

Stepped-Wedge Cluster Randomized Trial of AI-Assisted CTA Detection for Intracranial Aneurysms in Regional Hospitals

This study (IDEAL 2) is a nationwide stepped-wedge cluster-randomized trial designed to prospectively enroll over 14,400 patients undergoing outpatient head CT angiography (CTA). The trial will be conducted across more than 72 regional hospitals in China. Clusters were randomly assigned to nine randomization groups. In accordance with the stepped-wedge design, clusters will sequentially transition from the control condition (standard human diagnosis) to the intervention condition (AI-assisted diagnosis) at regular intervals over a 10-month period, until all clusters receive the intervention. The primary outcome is the detection rate of intracranial aneurysms. Secondary outcomes include patient prognosis and clinical outcomes.

AI-Driven CTA Reconstruction for Intracranial LVO

Acute ischemic stroke (AIS) caused by intracranial large vessel occlusion (LVO) in the anterior circulation significantly contributes to stroke-related disability and mortality. Recent randomized controlled trials have demonstrated substantial benefits of endovascular thrombectomy (EVT) when patients are appropriately triaged beforehand. However, accurately orienting the 'missed segment' during EVT remains challenging. Guide-wires often fail to navigate through the occlusion or are mistakenly directed into the small tranches or even cause vessel rupture. To address this clinical need, the investigators developed an artificial intelligence (AI) algorithm to automate the reconstruction of CT angiography (CTA), focusing on the occluded LVO segment. To evaluate the clinical utility of this AI algorithm, the investigators propose a prospective, stepped-wedge cluster-randomized study to determine whether integrating our AI algorithm into AIS care flow can reduce the time for first pass of the thrombus by improving the visualization of the occluded segment on CTA. Physicians will assess patient eligibility for thrombectomy, and all selected patients will receive standard care according to current guidelines. This approach is expected to enhance patient treatment outcomes for endovascular thrombectomy by leveraging readily available data.