Search Results

The O6-methylguanine-DNA methyltransferase (MGMT) is a deoxyribonucleic acid (DNA) repairing enzyme that has been established as an essential clinical brain tumor biomarker for Glioblastoma Multiforme (GBM). Knowing the status of MGMT ...

• The proposed study aims to provide solutions for building an efficient predictive model of MGMT for GBM patients using deep radiomics features obtained from mp-MRI with the end-to-end ResNet-18 3D and FDL imaging signatures.
• The hand-...

Cardiovascular disease (CVD) is a major healthcare challenge and therefore early risk assessment is vital. Previous assessment techniques use either “conventional CVD risk calculators (CCVRC)” or machine learning (ML) paradigms. These ...

Stroke risk assessment using deep learning (DL) requires automated, accurate, and real-time risk assessment while ensuring compact model size. Previous DL paradigms suffered from challenges like memory size, low speed, and complex in nature ...

• Stroke Risk assessment system in UNet-based segmentation.
• Three new HDL models: Inception-UNet, Squeeze-UNet, and Fractal-UNet.
• HDL is benchmarked against four SDL models: UNet, UNet+, UNet++ and UNet3P.
• HDL models show low ...

The Neonatal mortality rate in the United States is 3.8 deaths per 1000 live births, which is comparably higher than other nations.

The aim of the proposed study is to design and develop Artificial Intelligence (AI) models (...

• Developed neonatal and infant risk of death prediction paradigm using artificial intelligence.
• Implemented machine learning on a dataset containing birth records of 15.8 million patients.
• Used four different machine learning ...

COVLIAS 1.0: an automated lung segmentation was designed for COVID-19 diagnosis. It has issues related to storage space and speed. This study shows that COVLIAS 2.0 uses pruned AI (PAI) networks for improving both storage and speed, ...

• Eight evolutional algorithms (EA) based on Deep Learning for reduced storage and high speed.
• Multicenter study with 9,000 CT slices.
• COVID-19 based CT lung segmentation and lesion localization.
• Benchmarking against the EA ...

Artificial intelligence (AI) has become a prominent technique for medical diagnosis and represents an essential role in detecting brain tumors. Although AI-based models are widely used in brain lesion segmentation (BLS), understanding ...

• PRISMA search strategy and statistical distributions for brain tumor segmentation (BTS).
• Comparison between different AI models, its qualitative and quantitative analysis.
• Ranking Score Method for Risk-of-bias (RoB) estimation in ...

Artificial Intelligence (AI), in particular, machine learning (ML) has shown promising results in coronary artery disease (CAD) or cardiovascular disease (CVD) risk prediction. Bias in ML systems is of great interest due to its over-...

• Risk-of-Bias (RoB) in Machine Learning (ML) studies for cardiovascular disease (CVD) risk prediction using 46 AI attributes.
• Mean score computed, ranked, plotted using slope method, and two cutoffs established, and evaluating the three ...

Early and automated detection of carotid plaques prevents strokes, which are the second leading cause of death worldwide according to the World Health Organization. Artificial intelligence (AI) offers automated solutions for plaque ...

• Fusion of deep learning with ten kinds of machine learning classifiers.
• Fusion of Inception and ResNet.
• Three kinds of loss functions such as cross-entropy loss, hinge loss, or mean-squared-error loss, and hypothesis was validated.

Machine learning (ML) algorithms can provide better cardiovascular event (CVE) prediction. However, ML algorithms are mostly explored for predicting a single CVE at a time. The objective of this study is to design ...

• The first study, which predicts multi-label cardiovascular events using the machine learning (ML) paradigm.

Artificial intelligence (AI) has served humanity in many applications since its inception. Currently, it dominates the imaging field—in particular, image classification. The task of image classification became ...

• Use of PRISMA model for search strategy.
• Three types of hybrid deep learning ...

The automated and accurate carotid plaque segmentation in B-mode ultrasound (US) is an essential part of stroke risk stratification. Previous segmented methods used AtheroEdge™ 2.0 (AtheroPoint™, Roseville, CA) for the common carotid ...

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• Carotid plaque and wall segmentation and quantification using ten types of solo and hybrid deep learning models.

COVID-19 has infected 77.4 million people worldwide and has caused 1.7 million fatalities as of December 21, 2020. The primary cause of death due to COVID-19 is Acute Respiratory Distress Syndrome (ARDS). According to the World Health ...

• The review presents the PRISMA model for ARDS-based studies and eight stages of pathophysiology in ARDS due to COVID-19.
• Selection of comorbidity studies, and their statistical distribution in ARDS framework.
• Classification of the ...

Cardiovascular disease (CVD) is the leading cause of mortality and poses challenges for healthcare providers globally. Risk-based approaches for the management of CVD are becoming popular for recommending ...

• Cardiovascular disease (CVD) risk prediction algorithms are becoming popular for recommending treatment plans for asymptomatic individuals.

Conventional cardiovascular risk factors (CCVRFs) and carotid ultrasound image-based phenotypes (CUSIP) are independently associated with long-term risk of cardiovascular (CV) disease. In this study, 26 ...

BackgroundThis pilot study presents a completely automated, novel, smart, cloud-based, point-of-care system for (a) carotid lumen diameter (LD); (b) stenosis severity index (SSI) and (c) total lumen area (TLA) measurement using B-mode ultrasound. The ...

BackgroundPlanning of percutaneous interventional procedures involves a pre-screening and risk stratification of the coronary artery disease. Current screening tools use stand-alone plaque texture-based features and therefore lack the ability to ...

Lung disease risk stratification is important for both diagnosis and treatment planning, particularly in biopsies and radiation therapy. Manual lung disease risk stratification is challenging because of: (a) large lung data sizes, (b) inter- and intra-...

BackgroundAccurate and fast quantitative assessment of calcium volume is required during the planning of percutaneous coronary interventions procedures. Low resolution in intravascular ultrasound (IVUS) coronary videos poses a threat to calcium ...

Stroke risk stratification based on grayscale morphology of the ultrasound carotid wall has recently been shown to have a promise in classification of high risk versus low risk plaque or symptomatic versus asymptomatic plaques. In previous studies, this ...

This study presents AtheroCloudź - a novel cloud-based smart carotid intima-media thickness (cIMT) measurement tool using B-mode ultrasound for stroke/cardiovascular risk assessment and its stratification. This is an anytime-anywhere clinical tool for ...