AI4AI
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Work Packages
1
Image acquisition and formation
Facilitate accurate acquisition and image formation from affordable equipment.
Improve image quality and reduce artefacts.
Automate image quality assessment.
Evaluate clinical performance, coded biases, and end-user acceptance and feedback.
2
Imaging device handling and setup
Enable AI-based real-time device calibration.
Provide AI-driven solutions that offer real-time guidance during image acquisition.
Evaluate end-user and patient acceptance of and feedback on AI-guided image acquisition.
3
Image interpretation
Maintain performance in the face of changes in imaging protocols or patient distributions.
Minimize misinterpretation errors or reject outputs from data with suboptimal image quality.
Provide transparency and accountability of automatic algorithm decisions.
Evaluate end-user and patient acceptance and feedback on AI-based image interpretation.
Use Cases
Case C: Novel Volumetric Biomarkers for First Trimester Pregnancies Assessed using 3D Ultrasound
Develop novel, robust, failure-aware and resource-efficient AI methods to automatically derive quantitative biomarkers from 3d ultrasounds to monitoring growth and development of the fetus.
Nikolai Herrmann
,
Wietske Bastiaansen
,
Stefan Klein
,
Régine Steegers-Theunissen
,
Melek Rousian
Case D: Non-invasive Identification of Patients Requiring Invasive Coronary Artery Treatment
Developing AI-based CT-derived FFR methods for non-invasively identifying patients requiring invasive coronary artery treatment.
Ioanna Gogou
,
Simone Saitta
,
Richard Takx
,
Tim Leiner
,
Ivana Išgum
Case F: Workflow Improvement of Image-guided Radiotherapy
We are investigating an alternative radiotherapy treatment workflow on the MR-Linac for high-risk prostate cancer patients.
Emma ten Hoor
,
Bas Raaymakers
,
Josien Pluim
,
Hans de Boer
Case G: Urgent Care Referral Using Lung Ultrasound Imaging of Lungs
Short summary here
Joel Jacob Tomson
,
Henkjan Huisman
,
Anindo Saha
Case H: Triaging Patients with Suspected Cardiac Disease
The project aims to develop AI models to automatically analyze echocardiographic images, supporting primary care physicians in triaging patients and determining the need for cardiac referral.
Li-Hsin Cheng
,
Robertus van der Geest
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