Predicting transcranial ultrasound insertion loss using skull CT: A deep learning approach

Key Points

• Insertion loss is a critical factor for transcranial ultrasound effectiveness, influencing treatment outcomes.
• A predictive model achieved a significant accuracy of 85% in estimating insertion loss based on skull CT images.
• Approach utilizes deep learning algorithms to analyze CT scan data, enhancing predictive capabilities.
• Findings underscore the impact of precise imaging in optimizing ultrasound therapies; further validation is needed.