Clinical Research Directory

An AI-Based Prediction of Cognitive Capacity in Older Adults and Individuals With Mild Cognitive Impairment During Virtual Reality Driving Tasks

Driving ability in older adults is essential for independent mobility and social participation, yet declines under high cognitive load or distraction often lead to visual attention failures such as "look-but-fail-to-see," increasing crash risk. Older adults and individuals with mild cognitive impairment (MCI) show impairments in visual attention, executive control, and visuomotor integration, which are not adequately captured by conventional assessments. Virtual reality (VR) integrated with eye-tracking and upper-limb motion analysis enables ecologically valid simulation of driving scenarios and precise quantification of visuomotor behavior. However, current studies are limited by single-scenario designs, unimodal AI models, and insufficient integration of action-related data. This study proposes a multi-phase framework: Year 1 develops an eye-movement-based AI model for MCI identification; Year 2 integrates multimodal data in VR driving tasks; and Year 3 establishes an explainable AI system with longitudinal validation. The study aims to advance cognitive assessment and develop a digital tool for early MCI detection and driving risk prediction.

The Predictive Value of Multimodal Brain Monitoring for Perioperative Stroke in Cardiac Surgery Patients

Postoperative stroke following cardiac surgery is associated with a ninefold increase in mortality risk compared to patients without stroke. Perioperative monitoring in cardiac surgery involves a range of complex and diverse techniques, presenting significant challenges for anesthetic management. Multimodal brain monitoring technology offers a novel approach to cerebral protection during the perioperative period of cardiac surgery by integrating hemodynamic parameters, autonomic nervous responses, cerebral oxygen saturation and indices, electroencephalographic activity, and cerebral blood flow velocity. Therefore, this study aims to evaluate the comprehensive early-warning efficacy of multimodal brain monitoring for perioperative stroke in cardiac surgery patients, determine the cumulative incidence of perioperative stroke-including covert stroke-and provide a new theoretical basis for optimizing cerebral protection strategies in cardiac surgery.

Effect of Multimodal Monitoring Cluster Management on Patients with Severe Traumatic Brain Injury

Traumatic brain injury (TBI) is a serious global health problem, with about 27 million new cases each year. Severe head trauma can lead to intracranial hypertension and a range of complications such as cerebral edema and hypoxia of brain tissue. Intracranial pressure monitoring is key to modern TBI treatment, but this approach does not fully reflect all relevant pathophysiological changes. Multimodal monitoring allows for a more comprehensive assessment of the severity of TBI and guidance for individualized treatment to improve patient outcomes by integrating different monitoring methods. Although more research is needed to confirm its efficacy, multimodal monitoring has been recognized as an important clinical decision support tool in neuroscience intensive care. Future studies may further optimize this approach to monitoring and provide a more effective treatment strategy for patients with TBI.