Automated Evans index measurement using deep learning in acute subarachnoid hemorrhage: reliability, agreement with experts, and association with external ventricular drainage - Scorecard - MDSpire
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Automated Evans index measurement using deep learning in acute subarachnoid hemorrhage: reliability, agreement with experts, and association with external ventricular drainage
Clinical Scorecard: Deep Learning-Based Automation of Evans Index Assessment in Acute Subarachnoid Hemorrhage: Evaluation of Reliability, Expert Agreement, and Correlation with External Ventricular Drainage
At a Glance
Category
Detail
Condition
Acute Subarachnoid Hemorrhage
Key Mechanisms
Automated measurement of Evans index using deep learning for assessment of ventricular enlargement.
Target Population
Patients with spontaneous subarachnoid hemorrhage.
Care Setting
Neurocritical care settings.
Key Highlights
Automated EI measurement demonstrated excellent reproducibility (ICC = 0.996).
Good agreement between automated and expert EI measurements (ICC = 0.76).
Automated EI was independently associated with EVD placement (adjusted OR = 1.09).
TS identified more positive EI > 0.30 cases compared to expert assessment (29% vs. 17%).
Further refinement may improve robustness in hemorrhage-related ventricular distortion.
Guideline-Based Recommendations
Diagnosis
Use Evans index to assess ventricular enlargement in acute SAH.
Management
Consider automated EI measurement to support EVD decision-making.
Monitoring & Follow-up
Regularly evaluate ventricular size and hydrocephalus status in SAH patients.
Risks
Be aware of inter-reader variability in manual EI measurements.
Patient & Prescribing Data
Patients with spontaneous SAH undergoing CT imaging.
Automated EI measurement can enhance decision-making for EVD placement.
Clinical Best Practices
Implement automated EI measurement in neurocritical care workflows.
Ensure training for clinicians on interpreting automated EI results.
