Prosjekter
Flere publikasjoner
- Real-time guidance by deep learning of experienced operators to improve the standardization of echocardiographic acquisitions
- Automatic measurements of left ventricular volumes and ejection fraction by artificial intelligence: clinical validation in real time and large databases Les publikasjonen
- Echocardiographic Reference Ranges of Global Longitudinal Strain for All Cardiac Chambers Using Guideline-Directed Dedicated Views Les publikasjonen
- Segmentation of 2D cardiac ultrasound with deep learning: simpler models for a simple task
- Real-Time Echocardiography Guidance for Optimized Apical Standard Views Les publikasjonen
- Tracking-based mitral annular plane systolic excursion (MAPSE) measurement using deep learning in B-mode ultrasound Les publikasjonen
- Myocardial Function Imaging in Echocardiography Using Deep Learning Les publikasjonen
- Real-time temporal adaptation of dynamic movement primitives for moving targets Les publikasjonen
- Can a Dinosaur Think? Implementation of Artificial Intelligence in Extracorporeal Shock Wave Lithotripsy Les publikasjonen
- Annotation Web - An open-source web-based annotation tool for ultrasound images Les publikasjonen
Annen formidling
- Deep Learning-Based Functional Measurements from Parasternal Short-Axis in Echocardiography are Related to Hypertension and Left Ventricle Ejection Fraction
- Fastleger skal lære seg å bruke bærbart UL-apprat; hovedpulsåra dypt inne i kroppen til pasienten
- Kunstig intelligens har blitt hjertelegens "superassistent"
- Roboter kan bidra til å avlaste et overbelastet helsevesen
- Response to “Minimal Detectable Change and Reproducibility of Echocardiographic Strain: Implications for Clinical Practice”
- Machine learning in Echocardiography
- EchoBot: An open-source robotic ultrasound system
- EchoBot: An open-source robotic ultrasound system