Call for Papers
We invite submissions on all aspects of point-of-care ultrasound research, including methodological advances, clinical validation, and translational deployment.
- POCUS AI study design and evaluation
- AI-assisted ultrasound
- Clinical outcomes and validation
- Portable and wearable ultrasound systems
- Education and implementation science
Submission format: 4 page full paper
Submission platform: OpenReview
Paper Submission Deadline: March 12, 2026 (Anywhere on Earth)
Review and Notification of Decision: March 16, 2026 (Anywhere on Earth)
Camera-ready version: March 20, 2026 (Anywhere on Earth)
Example Accepted Papers
Shoulder Rotator Cuff Tear Detection from Ultrasound Videos Using Deep Reinforcement Learning
Ghosh S, Vadali G, Singh A, Zhou Y, Felfeliyan B, Wahd A, Knight J, Panicker MR, Jaremko JL, Hareendranathan AR
This work presents a deep reinforcement learning–based video summarization approach for rotator cuff tear assessment in ultrasound imaging. By automatically selecting diagnostically relevant frames and classifying them using a CNN, the method reduces redundancy and computational complexity while improving diagnostic performance. Evaluated on 100 patients, the approach achieved higher accuracy than full-video classification and cut training time by 50%, demonstrating strong potential for efficient, low-cost AI-assisted ultrasound diagnosis.