Central Nervous System DiseaseMRICTAI (Artificial Intelligence)Diagnose DiseaseClassification
Investigational drug(s) / intervention(s)
Foundation Model Specific to Neurological Diagnosis
Foundation Model Specific to Neurological Diagnosis: Validating the diagnostic efficacy of AI-assisted systems and their applicability in clinical settings based on CT/MRI
Study summary
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.
Eligibility
Sex
ALL
Min age
—
Max age
—
Healthy volunteers
Accepted
Inclusion Criteria:
* For MRI: patients suspected of harboring ischemic, hemorrhagic, brain tumors, degenerative brain disease, or traumatic brain injury at initiating or other institution, who subsequently underwent brain MRI;
* For CT: patients with or without neurological symptoms, suspected of harboring ischemic, hemorrhagic, space-occupying, degenerative brain disease, or traumatic brain injury, who subsequently underwent brain CT.
Exclusion Criteria:
Exclusion Criteria:
* Patients who opted-out or did not give permission to reuse clinical data.
* Patients with a history of prior brain surgery.
* Patients whose brain CT or MRI exhibit severe artifacts (e.g. heavy warping due to air, metal artifacts, heavy motion artifacts), thereby impeding the usage of the data.
Primary outcome measure(s)
The diagnostic performance of human-AI collaborative approach in identifying abnormal case is not inferior to that of human-only and AI-only — 6 months AI models and over 50 radiologists at neurological diseases on brain CT/MRI to assess the working performance of neuroimaging AI diagnostic tools for differentiating normal and abnormal examinations
The diagnostic performance of human-AI collaborative approach in critical value judgment is not inferior to that of human-only and AI-only — 6 months To improve the turnaround time and quality of urgent diagnostic reports on brain CT/MRI, the performance of three paradigms-human-only, AI-only, and human+AI-was evaluated based on diagnostic accuracy, time efficiency, and the critical metric of lead time in identifying urgent findings when AI was integrated compared to that of human-only.
This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.
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