AIMC Topic: Predictive Value of Tests

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Research on noninvasive electrophysiologic imaging based on cardiac electrophysiology simulation and deep learning methods for the inverse problem.

BMC cardiovascular disorders
BACKGROUND: The risk stratification and prognosis of cardiac arrhythmia depend on the individual condition of patients, while invasive diagnostic methods may be risky to patient health, and current non-invasive diagnostic methods are applicable to fe...

The value of deep learning and radiomics models in predicting preoperative serosal invasion in gastric cancer: a dual-center study.

Abdominal radiology (New York)
PURPOSE: To establish and validate a model based on deep learning (DL), integrating radiomic features with relevant clinical features to generate nomogram, for predicting preoperative serosal invasion in gastric cancer (GC).

Fast and automatic coronary artery segmentation using nnU-Net for non-contrast enhanced magnetic resonance coronary angiography.

The international journal of cardiovascular imaging
Non-contrast enhanced magnetic resonance coronary angiography (MRCA) is a promising coronary heart disease screening modality. However, its clinical application is hindered by inherent limitations, including low spatial resolution and insufficient co...

Opportunistic assessment of abdominal aortic calcification using artificial intelligence (AI) predicts coronary artery disease and cardiovascular events.

American heart journal
BACKGROUND: Abdominal computed tomography (CT) is commonly performed in adults. Abdominal aortic calcification (AAC) can be visualized and quantified using artificial intelligence (AI) on CTs performed for other clinical purposes (opportunistic CT). ...

Optimising coronary imaging decisions with machine learning: an external validation study.

Open heart
BACKGROUND: Exclusion of coronary stenosis in individuals with suggestive symptoms is challenging. Cardiac CT or coronary angiography is often used but is inefficient and costly and involves risks. Sex-stratified algorithms based on electronic health...

Predicting isolated impaired glucose tolerance without oral glucose tolerance test using machine learning in Chinese Han men.

Frontiers in endocrinology
BACKGROUND: Isolated Impaired Glucose Tolerance (I-IGT) represents a specific prediabetic state that typically requires a standardized oral glucose tolerance test (OGTT) for diagnosis. This study aims to predict glucose tolerance status in Chinese Ha...

Pancreas rejection: quieting the storm to preserve graft function.

Current opinion in organ transplantation
PURPOSE OF REVIEW: Allograft rejection remains enigmatic and elusive following pancreas transplantation. In the absence of early technical pancreas graft failure, pancreas allograft rejection is the major cause of death-censored pancreas graft loss b...

Deep learning-based post hoc denoising for 3D volume-rendered cardiac CT in mitral valve prolapse.

The international journal of cardiovascular imaging
We hypothesized that deep learning-based post hoc denoising could improve the quality of cardiac CT for the 3D volume-rendered (VR) imaging of mitral valve (MV) prolapse. We aimed to evaluate the quality of denoised 3D VR images for visualizing MV pr...

Comparison of machine learning models with conventional statistical methods for prediction of percutaneous coronary intervention outcomes: a systematic review and meta-analysis.

BMC cardiovascular disorders
INTRODUCTION: Percutaneous coronary intervention (PCI) has been the main treatment of coronary artery disease (CAD). In this review, we aimed to compare the performance of machine learning (ML) vs. logistic regression (LR) models in predicting differ...