Special issue

Automation in Ultrasound Imaging: AI driven and Model Based Data Acquisition, Analysis and Classification

 

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.

The Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests.

This collection supports United Nations Sustainable Development Goals 3: Good Health & Well-Being.

 

EDITORS

 

Libertario Demi PhD, Queensland University of Technology, Brisbane, Australia

Libertario Demi received his MSc (cum laude) in engineering from University of Pisa, and his PhD in applied physics from Delft University. After a postdoc at Eindhoven University, he worked as consultant for TMC Science & Technology, and as research scientist for IMEC. He has been visiting scholar at Twente University, Stanford University and KAIST. In 2018, he joined University of Trento and founded ULTRa (Ultrasound Lab Trento). Since 2021 he is Associate Professor within the same University. Since 2025 he is also Adjunct Professor at Queensland University of Technology, Faculty of Health. He is Fellow of the Acoustical Society of America, Deputy Editor of the Ultrasound Journal, As. Editor for the Journal of the Acoustical Society of America, and Editorial Board member of Ultrasonics.

 

Riccardo Inchingolo MD, PhD, ETMS , Università Cattolica del Sacro Cuore, Milan, Italy

Riccardo Inchingolo is an Adjunct Professor at the Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy. He earned his Bachelor's degree, his Specialization in Respiratory Diseases, and his PhD in General and Clinical Microbiology from the Università Cattolica del Sacro Cuore, Rome, Italy, in 2006, 2011, and 2015, respectively. He has been certified as an Expert Trainer in Medical Simulation since 2020 and obtained the European Spirometry License in 2014 and 2021. He obtained the National Scientific Qualification for the function of Associate Professor in the Competition Sector 06/D1 - Diseases of the Cardiovascular System and Respiratory System Diseases in 2022. His main research areas are basic knowledge, clinical application, experimental trials of lung ultrasound in respiratory diseases, and medical training with simulation systems. He has been a member of the National Board of Directors of the Italian Society of Pneumology/Italian Respiratory Society since 2019 and a member of the Board of Directors of the Academy of Thoracic Ultrasound - AdET since 2015. He is an Associate Editor of Frontiers Respiratory Pharmacology, a Review Editor of Frontiers Critical Care Anesthesiology, a Topical Advisory Panel Member of the journal Antibiotics, and a Topical Advisory Panel Member of the journal Biomedicines.

 

Federico Mento PhD, University of Trento, Trento, Italy

Federico Mento received his PhD in Information and Communication Technology (summa cum laude) from the University of Trento, Italy, in 2022. He was awarded the best PhD in Information and Communication Technology (35th cycle). Currently he is a PostDoc at the Ultrasound Laboratory of Trento (ULTRa), University of Trento. His main research interests include image processing, lung ultrasound, medical imaging, and signal processing. He published 28 papers on lung ultrasound imaging in international journals (among which IEEE TMI and Intensive Care Medicine). He served as a reviewer for more than 10 international journals (9 of which ranked as Q1), and was an organizing committee member of the first and second International Lung Ultrasound Symposium.