Overview
AI assists interpretation of ultrasound by detecting pathology and quantifying measurements. It supports point of care and diagnostic ultrasound applications. Real time feedback enhances procedural guidance.
Techniques
Models handle variable image quality and operator dependent acquisition. Training uses annotated cine loops and still images for robustness. Transfer learning improves performance across devices.
Clinical Applications
AI aids in fetal assessment cardiac function and abdominal pathology detection. It automates measurements such as ejection fraction and fetal biometry. Integration with handheld devices expands access.
Limitations
Operator dependence and probe variability affect model generalizability. Continuous training and local validation improve reliability. Clear user interfaces support clinician acceptance.