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Predictive stroke risk model with vision transformer-based Doppler features.

Chung-Ming Lo¹, Peng-Hsiang Hung²

¹Graduate Institute of Library, Information and Archival Studies, National Chengchi University, Taipei, Taiwan.

Medical Physics

|December 3, 2023

Summary

This summary is machine-generated.

A new computer-aided diagnosis system using carotid color Doppler images and vision transformers can rapidly and accurately detect ischemic stroke, improving early diagnosis and patient outcomes.

Keywords:

carotid ultrasound computer-aided diagnosis deep learning vision transformer

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Area of Science:

Medical Imaging
Artificial Intelligence in Medicine
Neurology

Background:

Acute stroke is a leading global cause of death and disability, with millions of cases annually.
Carotid stenosis can reduce intracranial blood flow, leading to ischemic stroke.
Early recognition of ischemic stroke is critical for effective treatment and management.

Purpose of the Study:

To develop a computer-aided diagnosis (CAD) system for rapid ischemic stroke evaluation.
To utilize carotid color Doppler (CCD) images within the CAD system.
To assess the system's performance in identifying ischemic stroke.

Main Methods:

Vision transformer (ViT) features were extracted from CCD images (513 stroke, 458 normal).
Features were combined with machine learning classifiers for stroke likelihood prediction.
Pretrained ImageNet weights accelerated the training process.

Main Results:

The CAD system achieved 89% accuracy, 94% sensitivity, and 84% specificity.
The area under the receiver operating characteristic curve was 0.95.
The system outperformed several convolutional neural network models with significantly faster training times (30s).

Conclusions:

Carotid color Doppler images show promise for automated stroke estimation.
The pretrained ViT architecture enables efficient, automated feature generation.
The proposed CAD system offers a rapid and reliable diagnostic aid for ischemic stroke.