Ultrasound is one of the most widely used and accessible diagnostic modalities in global healthcare. However, its effectiveness is often limited by user skill and interpretive subjectivity. Automation in Ultrasound Imaging is revolutionizing this field by offering automated interpretation, real-time guidance, and decision support, enabling more consistent and accurate diagnoses across care settings. This research is central to the future of personalized, affordable, and scalable diagnostic medicine.

This research collection focuses on the intersection of Model Based Data Acquisition, Analysis and Classification and medical ultrasound, covering algorithmic advances, clinical applications, and real-time diagnostic tools. It welcomes interdisciplinary contributions in machine learning, image processing, clinical radiology, and bioengineering that enhance the accuracy, efficiency, and accessibility of ultrasound imaging. Key areas include AI-assisted anomaly detection, automated measurements, portable diagnostics, and point-of-care applications in diverse clinical settings.

Topics of interest include (but are not limited to):

• Deep learning models for ultrasound image classification and segmentation
• Automated quality assessment and real-time feedback systems
• Edge AI and portable ultrasound devices for remote or low-resource settings
• Explainable AI (XAI) in clinical decision-making from ultrasound data
• Integration with electronic health records and diagnostic platforms
• Validation studies and clinical trials involving AI-based ultrasound tools
• Novel AI applications in lung, cardiac, abdominal, brain and obstetric ultrasound

All submitted papers will undergo rigorous peer-review by specialists in the field, with accepted papers set to be published as soon as they are ready.

SEE MORE ...