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Application of machine learning and radiomics in predicting early neurological deterioration in patients with perforating artery infarcts

Application of machine learning and radiomics in predicting early neurological deterioration in patients with perforating artery infarcts

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2200064185
Enrollment
Unknown
Registered
2022-09-29
Start date
2022-09-30
Completion date
Unknown
Last updated
2023-04-17

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

Conditions

cerebrovascular disease

Interventions

2:inapplicability

Sponsors

Jincheng People's Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 100 Years

Inclusion criteria

Inclusion criteria: 1. Aged >= 18 years; 2. Acute perforating artery infarction (acute subcortical cerebellar infarction, no upper limit of diameter) confirmed by MRI; 3. Diagnosis time <= 24 hours; 4. Cranial MRI examination before early neurological deterioration.

Exclusion criteria

Exclusion criteria: 1. Multiple and cortical cerebral infarction; 2. mRS >= 2 points before onset; 3. Similar to stroke; 4. Image artifacts or other factors affect image evaluation.

Design outcomes

Primary

MeasureTime frame
National Institute of Health stroke scale;—

Secondary

MeasureTime frame
mRS scale;—

Countries

China

Contacts

Public ContactLiu Wei

Jincheng People's Hospital

liuiwei19910723@163.com+86 15234677581

Outcome results

None listed

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