Deep Learning Prediction for Distal Aortic Remodeling After Thoracic Endovascular Aortic Repair in Stanford Type B Aortic Dissection.
Min ZhouXiaoyuan LuoXia WangTianchen XieYonggang WangZhenyu ShiManning WangWeiguo FuPublished in: Journal of endovascular therapy : an official journal of the International Society of Endovascular Specialists (2023)
Negative aortic remodeling is the leading cause of late reintervention after proximal thoracic endovascular aortic repair (TEVAR) for Stanford type B aortic dissection (TBAD), and possesses great challenge to endovascular repair. Early recognizing high-risk patients is of supreme importance for optimizing the follow-up interval and therapy strategy. Currently, clinicians predict the prognosis of these patients based on several imaging signs, which is subjective. The computed tomography angiography-based deep learning algorithms may incorporate abundant morphological information of aorta, provide with a definite and objective output value, and finally assist clinicians in automated prediction of distal aortic remodeling after TEVAR for acute or subacute TBAD.
Keyphrases
- aortic dissection
- deep learning
- end stage renal disease
- machine learning
- newly diagnosed
- palliative care
- chronic kidney disease
- prognostic factors
- peritoneal dialysis
- spinal cord
- artificial intelligence
- high resolution
- coronary artery
- aortic valve
- magnetic resonance imaging
- computed tomography
- hepatitis b virus
- social media
- patient reported outcomes
- pulmonary arterial hypertension
- bone marrow
- pulmonary hypertension
- atrial fibrillation
- acute respiratory distress syndrome