Contrast-enhanced ultrasound (CEUS) substantially improves the potential of ultrasound (US) for the identification and characterization of focal liver lesions (FLLs). Compared to contrasted-enhanced MRI and CT, it has some unique advantages, such as the absence of ionizing radiation, and easy operability and repeatability. However, the efficacy of CEUS in diagnosing liver lesions is challenged by several factors including being highly dependent on doctor's experience, low signal-to-noise ratio, and low interobserver agreement. Therefore, it is a beneficial attempt to construct an intelligent CEUS diagnosis system using digital information technology. This study aims to collect standard data of CEUS cines recordings and develop deep learning model for accurate segmentation, detection and classification of liver lesions.
Study Type
OBSERVATIONAL
Enrollment
5,000
there is no intervention diagnosis or treatment for patients
Chinese PLA General Hospital
Beijing, Beijing Municipality, China
RECRUITINGAUC value
Area under the receiver operating characteristic (ROC) curve (AUC）
Time frame: through study completion, an average of 7 year
specificity
diagnosis specificity of intelligent CEUS analysis
Time frame: through study completion, an average of 7 year
sensitivity
diagnosis sensitivity of intelligent ultrasound analysis
Time frame: through study completion, an average of 3 year
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.