Clinical Research Directory

Comprehensive Multimodal Analysis of Neuroimmunological Diseases of the Central Nervous System

Inflammatory or degenerative diseases of the brain and spinal cord, such as multiple sclerosis, may be related to problems with an individual s immune system. However, more information is needed on the ways in which the cells of the immune system interact with the central nervous system (CNS). This study will compare tests performed on both healthy volunteers and individuals who have signs or symptoms of immune-related damage to their CNS. This study will include two groups of subjects at least 12 years old. Subjects will either have symptoms of immune-related CNS damage, or will be healthy volunteers selected for comparison purposes. Study participants will visit the NIH Clinical Center on an outpatient basis for an initial evaluation visit. During the visit, patients will provide a comprehensive medical history and undergo a neurological examination, and will provide blood samples for research purposes. The healthy volunteers will be asked to schedule a return visit for a magnetic resonance imaging (MRI) procedure, and may be asked to undergo other tests requested by the study researchers on an as-needed basis. The group of patients with symptoms of immune-related CNS damage will be asked to undergo a series of tests, including the following: * MRI procedures, with a minimum of three brain MRIs and one spinal cord MRI taken approximately 4 weeks apart * A diagnostic lumbar puncture, performed on an outpatient basis * Tests of brain and vision activity * Additional blood and tissue samples Patients with symptoms of immune-related CNS damage may be offered the opportunity to participate in additional followup tests with NIH researchers. ...

Validation on Clinical Adaptability of the Foundation Model Specific to Neuroimaging Diagnosis

This clinic trial aims to investigate whether artificial intelligence (AI) diagnostic tools at neurological diseases diagnosis on brain CT/MRI can improve the work efficiency of specialized neuroimaging physicians, with a specific focus on its clinical value in distinguishing normal from abnormal findings, critical value identification, and neurological disease classification. Using pathological and/or discharge diagnoses of neurological diseases as the gold standard, an AI model will be trained on over 10,000 CT/MRI cases to achieve diagnostic performance comparable to that of neurological radiologists before being transformed and putted to use. Furthermore, clinical trials will be conducted in sub-studies (abnormal cases identification, critical value assessment, and neurological disease classification) to validate the clinical utility of AI and human-AI collaboration in the precise diagnosis of neurological disorders. The expected outcomes include reducing missed and misdiagnosis rates, enabling rapid screening of critical conditions, and achieving precise imaging-based diagnosis by using AI tools.

Long-term Brain Stimulation of the Motor Ventral Thalamus (VOP/VIM) to Improve Motor Function

This study aims to recruit patients already implanted with Deep Brain Stimulation (DBS) for movement disorders to complete tasks assessing parameters of motor output, speech, and swallowing functions, both with and without stimulation. DBS parameters would be adjusted prior to motor testing. Patients would then participate in multiple sessions performing contralateral upper extremity movement tasks measuring movement speed, grip strength, and strength modulation, facial movement, swallowing, and speech tasks.

An Explainable Neuroradiologist Artificial Intelligence Assistance System for Brain CT and MRI

This clinic trial aims to validate the working performance of radiologists with or without artificial intelligence (AI) diagnostic tool at neurological diseases diagnosis on brain CT/MRI. Routine diagnosis workflow in real clinical scenario including imaging reading, feature interpretation, differential diagnosis, writing initial report and optimizing revised version. And the gold standards of diagnosis are the histopathology references for brain tumors and the discharge diagnosis integrating all the examination results for the other neurological diseases. The performance of AI-assisted tools on diagnosing should be examined in a clinical process with multiple aspects identical to human radiologists' work before being transformed and putted to use. This study hypothesizes that AI models, trained with over 100,000 patient scans, are non-inferior to radiologists in neurological disease diagnosis on brain CT and MRI. For the secondary end-points, we investigate the performance of AI-radiologist collaboration of reasoning-enhanced AI-assisted systems. We hypothesize that, by visualizing the process of imaging interpretation and diagnosis, reasoning-enhanced AI can not only improve working performance of radiologists but also boost their trust in AI tools.