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Clinical Study on Predicting 90-Day mRS Scores in Stroke Patients Using a Deep Learning-Based Multimodal Fusion Model

Clinical Study on Predicting 90-Day mRS Scores in Stroke Patients Using a Deep Learning-Based Multimodal Fusion Model

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500097722
Enrollment
Unknown
Registered
2025-02-25
Start date
2025-03-01
Completion date
Unknown
Last updated
2025-03-03

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Cerebral small vessel Disease

Interventions

Observation group:NA

Sponsors

General Hospital of Xuzhou Mining Group
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: 1.Patients aged 18 and above; 2.Acute<= 24 hours; 3.All modal imaging and clinical data are complete and meet diagnostic requirements.

Exclusion criteria

Exclusion criteria: 1.The patient's imaging shows intracranial hemorrhage; 2.There are serious motion artifacts, metal artifacts, implants, and other factors that affect the interpretation of patient images; 3.The patient's imaging shows the presence of intracranial tumors, cysts, and other space occupying lesions; 4.The patient's imaging shows defects in the cranial tissue caused by surgery, trauma, etc.

Design outcomes

Primary

MeasureTime frame
Accuracy;Intraclass Correlation Coefficient;Precision;—

Secondary

MeasureTime frame
Area Under the Receiver Operating Characteristic Curve (AUC-ROC);Recall;F1 Score;—

Countries

China

Contacts

Public ContactShuai Yu

General Hospital of Xuzhou Mining Group

451567858@qq.com+86 187 6143 0920

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026