An Implementable Deep Learning Approach for Automated Risk Assessment of Stroke in Patients with Carotid Atherosclerotic Plaque

Category	Detail
Condition	Ischemic stroke risk associated with carotid atherosclerotic plaque
Key Mechanisms	Plaque vulnerability features detected via carotid ultrasound imaging; deep learning models extract complex subvisual features for risk stratification
Target Population	Patients with carotid atherosclerotic plaques identified by ultrasound (age >18 years, plaque thickness ≥1.5 mm)
Care Setting	Clinical diagnostic setting utilizing carotid ultrasound imaging

Deep learning model ResNet-50 achieved superior stroke risk prediction with AUC of 0.982, outperforming traditional machine learning models.
Ultrasound-based DL enables objective, reproducible assessment of plaque vulnerability beyond subjective physician interpretation.
Automated DL risk stratification facilitates early identification of high-risk patients for targeted stroke prevention.

Use carotid ultrasound as first-line screening for carotid atherosclerotic plaques.
Apply deep learning models (e.g., ResNet-50) to ultrasound images for objective plaque vulnerability assessment and stroke risk prediction.

Implement individualized prevention and treatment strategies based on DL-derived risk stratification to reduce ischemic stroke morbidity and mortality.

Regular ultrasound imaging and DL-based risk reassessment to monitor plaque progression and adjust management accordingly.

Subjective interpretation of ultrasound images may lead to inconsistent plaque vulnerability assessment.
Traditional ML models may underperform due to reliance on manual feature engineering and limited capture of complex plaque features.

Patients with carotid plaques identified by ultrasound imaging, including both stroke and non-stroke individuals.

DL models provide enhanced accuracy and sensitivity in stroke risk prediction, supporting clinical decision-making for preventive interventions.

Incorporate deep learning algorithms into carotid ultrasound workflows to improve diagnostic accuracy and reproducibility.
Prefer end-to-end DL models over traditional ML classifiers for automated plaque vulnerability assessment.
Use validated DL models like ResNet-50 trained on annotated plaque images for clinical stroke risk stratification.
Ensure standardized ultrasound image acquisition and annotation to optimize DL model performance.

Clinical Scorecard: An Implementable Deep Learning Approach for Automated Risk Assessment of Stroke in Patients with Carotid Atherosclerotic Plaque