AI in sonography enhances early detection and risk stratification of carotid atherosclerotic plaques by improving accuracy and reducing variability.
Does artificial intelligence in sonography improve the early detection and risk stratification of vulnerable carotid atherosclerotic plaques?
AI-driven models in carotid ultrasound facilitate automated analysis, reducing interobserver variability and enhancing reproducibility in plaque assessment.
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Objective: Artificial intelligence (AI) has emerged as a transformative tool in ultrasound imaging enhancing the accuracy and efficiency of carotid plaque characterization. In addition, AI-driven models facilitate automated analysis reducing interobserver variability and enhancing reproducibility in plaque assessment. Materials and Methods: A systematic review process is outlined that provides an observational study curated from the available results from 1165 unique studies and were screened based on their titles and abstracts. Results: This review is based on 11 studies that satisfied all eligibility requirements and were included in the final review synthesis. The summative results highlighted the current state-of-the-art applications of AI in sonography, as well as providing early detection and risk stratification of carotid atherosclerotic plaques. The review also addressed emerging technologies, limitations, and future directions in AI-driven vascular imaging. Conclusion: With continued advancements, AI has the potential to revolutionize carotid sonography, enabling early intervention and reducing the global burden of stroke and cardiovascular diseases.
Muhammad Zubair (Thu,) reported a other. AI in sonography enhances early detection and risk stratification of carotid atherosclerotic plaques by improving accuracy and reducing variability.