[HTML][HTML] Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013–2023)
Uncertainty estimation in healthcare involves quantifying and understanding the inherent
uncertainty or variability associated with medical predictions, diagnoses, and treatment …
uncertainty or variability associated with medical predictions, diagnoses, and treatment …
Handling of uncertainty in medical data using machine learning and probability theory techniques: A review of 30 years (1991–2020)
Understanding the data and reaching accurate conclusions are of paramount importance in
the present era of big data. Machine learning and probability theory methods have been …
the present era of big data. Machine learning and probability theory methods have been …
A survey on epistemic (model) uncertainty in supervised learning: Recent advances and applications
Quantifying the uncertainty of supervised learning models plays an important role in making
more reliable predictions. Epistemic uncertainty, which usually is due to insufficient …
more reliable predictions. Epistemic uncertainty, which usually is due to insufficient …
Geometric and biomechanical modeling aided by machine learning improves the prediction of growth and rupture of small abdominal aortic aneurysms
M Lindquist Liljeqvist, M Bogdanovic, A Siika… - Scientific reports, 2021 - nature.com
It remains difficult to predict when which patients with abdominal aortic aneurysm (AAA) will
require surgery. The aim was to study the accuracy of geometric and biomechanical analysis …
require surgery. The aim was to study the accuracy of geometric and biomechanical analysis …
Using constrained-disorder principle-based systems to improve the performance of digital twins in biological systems
T Sigawi, Y Ilan - Biomimetics, 2023 - mdpi.com
Digital twins are computer programs that use real-world data to create simulations that
predict the performance of processes, products, and systems. Digital twins may integrate …
predict the performance of processes, products, and systems. Digital twins may integrate …
Deep learning on multiphysical features and hemodynamic modeling for abdominal aortic aneurysm growth prediction
Prediction of abdominal aortic aneurysm (AAA) growth is of essential importance for the
early treatment and surgical intervention of AAA. Capturing key features of vascular growth …
early treatment and surgical intervention of AAA. Capturing key features of vascular growth …
Efficient parallel simulation of hemodynamics in patient-specific abdominal aorta with aneurysm
Surgical planning for aortic aneurysm repair is a difficult task. In addition to the
morphological features obtained from medical imaging, alternative features obtained with …
morphological features obtained from medical imaging, alternative features obtained with …
Characterization of small abdominal aortic aneurysms' growth status using spatial pattern analysis of aneurismal hemodynamics
Aneurysm hemodynamics is known for its crucial role in the natural history of abdominal
aortic aneurysms (AAA). However, there is a lack of well-developed quantitative …
aortic aneurysms (AAA). However, there is a lack of well-developed quantitative …
Automatic segmentation of abdominal aortic aneurysms from CT angiography using a context-aware cascaded U-Net
We delineate abdominal aortic aneurysms, including lumen and intraluminal thrombosis
(ILT), from contrast-enhanced computed tomography angiography (CTA) data in 70 patients …
(ILT), from contrast-enhanced computed tomography angiography (CTA) data in 70 patients …
Computer-aided shape features extraction and regression models for predicting the ascending aortic aneurysm growth rate
L Geronzi, A Martinez, M Rochette, K Yan… - Computers in Biology …, 2023 - Elsevier
Objective: ascending aortic aneurysm growth prediction is still challenging in clinics. In this
study, we evaluate and compare the ability of local and global shape features to predict the …
study, we evaluate and compare the ability of local and global shape features to predict the …