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

NCT07471984 | Enrolling By Invitation DMC

• 2 other identifiers: 2025-A102025-A09
• Study Type: observational
• Target: 50,000
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Central Nervous System Disease; MRI; CT; AI (Artificial Intelligence); Diagnose Disease; Classification

Outcome Measures

Primary Outcomes (2)

• The diagnostic performance of human-AI collaborative approach in identifying abnormal case is not inferior to that of human-only and AI-only

  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

  6 months

• The diagnostic performance of human-AI collaborative approach in critical value judgment is not inferior to that of human-only and AI-only

  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.

  6 months

Secondary Outcomes (3)

• The diagnostic performance of human-AI collaborative approach in disease classification is not inferior to that of human-only and AI-only

  6 months

• The diagnostic performance of human-AI collaborative approach in disease classification is superior to that of human-only

  6 months

• The diagnostic performance of current model in disease classification on brain CT and MRI is superior to comparable models on a large-scale external dataset

  6 months

Interventions

Foundation Model Specific to Neurological Diagnosis DIAGNOSTIC_TEST

Validating the diagnostic efficacy of AI-assisted systems and their applicability in clinical settings based on CT/MRI

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

All patient exams are suspected of harboring brain tumors or the other neurological diseases, with or without neurological symptoms, and without a history of prior brain surgery or inpatient/outpatient medical records. Patients underwent brain CT and MRI, and were primarily examined at Beijing Tiantan hospital, between 2012-2026.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Yaou Liu: lead

Study Sites (1)

Beijing Tiantan Hospital

Beijing, China

Location

Biospecimen

Retention: SAMPLES WITH DNA

Biospecimen is not stored beyond the timeframe of this study, for the purpose of this study. However, biospecimen is stored beyond the timeframe of this study, for the purpose of regular clinical care. In this case, biospecimen refers to histopathology tissue acquired from confirmatory brain biopsies. Within the scope of clinical routine, storing such specimen can facilitate reassessments through the future, e.g. for comparisons if the patient presents new findings or metastasis of their initial findings, for comparisons against histopathology findings of the original patient's offspring, or even for legal purposes in the case of misdiagnosis.

MeSH Terms

Conditions

Central Nervous System Diseases

Condition Hierarchy (Ancestors)

Nervous System Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Director of the Radiology Department

Study Record Dates

• First Submitted: March 5, 2026
• First Posted: March 13, 2026
• Study Start: May 1, 2025
• Primary Completion: October 1, 2025
• Study Completion (Estimated): December 1, 2030
• Last Updated: March 13, 2026

Record last verified: 2025-09

Locations

CN (1)