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Automated in-depth cerebral arterial labelling using cerebrovascular vasculature reframing and deep neural networks.

Identifying the cerebral arterial branches is essential for undertaking a computational approach to cerebrovascular imaging. However, the complexity and inter-individual differences involved in this process have not been thoroughly studied. We used machine learning to examine the anatomical profile of the cerebral arterial tree. The method is less sensitive to inter-subject and cohort-wise anatomi...

Increasing transparency in machine learning through bootstrap simulation and shapely additive explanations.

Machine learning methods are widely used within the medical field. However, the reliability and efficacy of these models is difficult to assess, making it difficult for researchers to identify which machine-learning model to apply to their dataset. We assessed whether variance calculations of model metrics (e.g., AUROC, Sensitivity, Specificity) through bootstrap simulation and SHapely Additive ex...

Lorcaserin and phentermine exert anti-obesity effects with modulation of the gut microbiota.

Although drugs have been reported to modulate the gut microbiota, the effects of anti-obesity drugs on the gut microbiota remain unclear. Lorcaserin (...

Mechano-fluorescence actuation in single synaptic vesicles with a DNA framework nanomachine.

Biomimetic machines that can convert mechanical actuation to adaptive coloration in a manner analogous to cephalopods have found widespread applicatio...

Efficient targeted learning of heterogeneous treatment effects for multiple subgroups.

In biomedical science, analyzing treatment effect heterogeneity plays an essential role in assisting personalized medicine. The main goals of analyzin...

Aortic Distensibility Measured by Automated Analysis of Magnetic Resonance Imaging Predicts Adverse Cardiovascular Events in UK Biobank.

Background Automated analysis of cardiovascular magnetic resonance images provides the potential to assess aortic distensibility in large populations....

Finding the influential clinical traits that impact on the diagnosis of heart disease using statistical and machine-learning techniques.

In recent years, the omnipresence of cardiac problems has been recognized as an epidemic. With the correct and quick diagnosis, both mortality and mor...

Gene-gene interaction detection with deep learning.

The extent to which genetic interactions affect observed phenotypes is generally unknown because current interaction detection approaches only conside...

[Future of interventional cardiology : Does everything revolve around AI and robotics?].

In recent years, software-assisted imaging systems, such as computed tomography, have contributed to the improvement of noninvasive options for the di...

Using modern risk engines and machine learning/artificial intelligence to predict diabetes complications: A focus on the BRAVO model.

Management of diabetes requires a multifaceted approach of risk factor reduction; through management of risk factors such as glucose, blood pressure a...

Development of non-bias phenotypic drug screening for cardiomyocyte hypertrophy by image segmentation using deep learning.

The number of patients with heart failure and related deaths is rapidly increasing worldwide, making it a major problem. Cardiac hypertrophy is a cruc...

Automated detection of intracranial artery stenosis and occlusion in magnetic resonance angiography: A preliminary study based on deep learning.

BACKGROUND AND OBJECTIVES: Intracranial atherosclerotic stenosis of a major intracranial artery is the common cause of ischemic stroke. We evaluate th...

Improved Harris Hawks Optimization with Hybrid Deep Learning Based Heating and Cooling Load Prediction on residential buildings.

In digital era, energy efficient building remains a hot research topic because of increasing concern regarding their environmental impact and energy c...

Emotional Semantics-Preserved and Feature-Aligned CycleGAN for Visual Emotion Adaptation.

Thanks to large-scale labeled training data, deep neural networks (DNNs) have obtained remarkable success in many vision and multimedia tasks. However...

A Novel Ground Metric for Optimal Transport-Based Chronological Age Estimation.

Label distribution learning (LDL) is the state-of-the-art approach to dealing with a number of real-world applications, such as chronological age esti...

Research progress and hotspot of the artificial intelligence application in the ultrasound during 2011-2021: A bibliometric analysis.

Ultrasound, as a common clinical examination tool, inevitably has human errors due to the limitations of manual operation. Artificial intelligence is ...

Deep Learning of Coronary Calcium Scores From PET/CT Attenuation Maps Accurately Predicts Adverse Cardiovascular Events.

BACKGROUND: Assessment of coronary artery calcium (CAC) by computed tomographic (CT) imaging provides an accurate measure of atherosclerotic burden. C...

Federated machine learning for a facilitated implementation of Artificial Intelligence in healthcare - a proof of concept study for the prediction of coronary artery calcification scores.

The implementation of Artificial Intelligence (AI) still faces significant hurdles and one key factor is the access to data. One approach that could s...

Multicenter Study on COVID-19 Lung Computed Tomography Segmentation with varying Glass Ground Opacities using Unseen Deep Learning Artificial Intelligence Paradigms: COVLIAS 1.0 Validation.

Variations in COVID-19 lesions such as glass ground opacities (GGO), consolidations, and crazy paving can compromise the ability of solo-deep learning...

The Artificial Intelligence System for the Generation of Sports Education Guidance Model and Physical Fitness Evaluation Under Deep Learning.

In recent years, China's achievements in artificial intelligence (AI) have attracted the attention of the world, and AI technology has penetrated into...