Artificial Intelligence-Assisted Sac Diameter Assessment for Complex Endovascular Aortic Repair.
Moritz WegnerVincent FontainePetroula NanaBryan V DieffenbachDominique FabreStéphan HaulonPublished in: Journal of endovascular therapy : an official journal of the International Society of Endovascular Specialists (2023)
In this retrospective analysis of preoperative and postoperative imaging from 50 patients managed with FEVAR, AI provided accurate aortic diameter measurements in 89% of the CTAs reviewed, despite the complexity of the aortic anatomies, and in post-operative CTAs despite metal artifact from stent grafts, markers and embolization materials. Outliers with imprecise automated aortic overlays were easily identified by scrolling through the axial AI-generated segmentation MPR cuts of the entire aorta.This study supports the notion that such emerging AI technologies can improve efficiency of routine clinician workflows while maintaining excellent measurement accuracy when analyzing complex aortic anatomies by CTA.
Keyphrases
- artificial intelligence
- aortic valve
- deep learning
- machine learning
- pulmonary artery
- big data
- aortic dissection
- left ventricular
- end stage renal disease
- high resolution
- patients undergoing
- chronic kidney disease
- ejection fraction
- convolutional neural network
- newly diagnosed
- pulmonary hypertension
- peritoneal dialysis
- optic nerve
- pulmonary arterial hypertension
- magnetic resonance imaging
- clinical practice
- magnetic resonance
- computed tomography
- mass spectrometry
- image quality