Research Presentation Sessions: Artificial Intelligence & Machine Learning & Imaging Informatics

RPS 1205 - Using AI for quality control in radiography

Lectures

1
Predicting a radiograph’s diagnostic quality before its acquisition: an artificial intelligence approach using depth imaging in a cadaver study

Predicting a radiograph’s diagnostic quality before its acquisition: an artificial intelligence approach using depth imaging in a cadaver study

07:00Malte Maria Sieren, Lübeck / DE

2
Can AI help us in acquiring good skeletal images? Towards automated quality assessment of 2D skeletal X-ray imaging using AI-assisted pose estimation

Can AI help us in acquiring good skeletal images? Towards automated quality assessment of 2D skeletal X-ray imaging using AI-assisted pose estimation

07:00Omar Hertgers, Den Haag / NL

3
Automated quality control of chest X-rays

Automated quality control of chest X-rays

07:00Ian Andrew Selby, Cambridge / UK

4
Detecting patient mix-ups in chest radiographs: an artificial intelligence approach

Detecting patient mix-ups in chest radiographs: an artificial intelligence approach

07:00Lennart Berkel, Lübeck / DE

5
How does AI feedback impact student and qualified radiographers’ decision making?

How does AI feedback impact student and qualified radiographers’ decision making?

07:00Clare Rainey, Portrush / UK

6
Quality assessment compliance using a combination of deep learning, NLP and limited human intervention: feasibility for real time institutional deployment

Quality assessment compliance using a combination of deep learning, NLP and limited human intervention: feasibility for real time institutional deployment

07:00Vasantha Kumar Venugopal, New Delhi / IN

7
Whole-body magnetic resonance imaging in the large population-based German national cohort study (NAKO): use of an automated image quality assessment for the prediction of perceived image quality

Whole-body magnetic resonance imaging in the large population-based German national cohort study (NAKO): use of an automated image quality assessment for the prediction of perceived image quality

07:00Christopher Schuppert, Heidelberg / DE

8
Moderation

Moderation

00:00Linda Bergli Hammerstrøm, Greåker / NO

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