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Noncontrast CT-Based Deep Learning for Predicting Hematoma Expansion Risk in Patients with Spontaneous Intracerebral Hemorrhage

NCT06602115 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 2000

Last updated 2024-09-19

No results posted yet for this study

Summary

Hematoma expansion is an independent predictor of poor prognosis and early neurological deterioration in patients with spontaneous intracerebral hemorrhage. Early identification of high-risk patients and timely targeted medical interventions may provide a crucial opportunity to limit hematoma growth and improve neurological outcomes. This study aims to develop an end-to-end deep learning model based on noncontrast computed tomography images to predict the risk of hematoma expansion in patients with spontaneous intracerebral hemorrhage. This model could serve as a valuable risk stratification tool for patients with hematoma expansion, facilitating targeted treatment and providing clinicians with streamlined decision-making support in emergency situations.

Conditions

  • Spontaneous Intracerebral Hemorrhage
  • Hematoma Expansion

Interventions

OTHER

Observational study, no interventions involved

Observational study, no interventions involved

Sponsors & Collaborators

  • Xiangya Hospital of Central South University

    collaborator OTHER

  • The First Affiliated Hospital with Nanjing Medical University

    collaborator OTHER

  • Southwest Hospital, China

    collaborator OTHER

  • Liuzhou Workers' Hospital

    collaborator OTHER_GOV

  • Qiang Yu

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-09-25
Primary Completion
2024-10-31
Completion
2024-12-31

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06602115 on ClinicalTrials.gov