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Proposing a novel deep network for detecting COVID-19 based on chest images.

Scientific reports
The rapid outbreak of coronavirus threatens humans' life all around the world. Due to the insufficient diagnostic infrastructures, developing an accurate, efficient, inexpensive, and quick diagnostic tool is of great importance. To date, researchers ...

Machine Learning-Based MRI LAVA Dynamic Enhanced Scanning for the Diagnosis of Hilar Lesions.

Computational and mathematical methods in medicine
OBJECTIVE: To explore the value of machine learning-based magnetic resonance imaging (MRI) liver acceleration volume acquisition (LAVA) dynamic enhanced scanning for diagnosing hilar lesions.

Effectiveness of Artificial Intelligence Models for Cardiovascular Disease Prediction: Network Meta-Analysis.

Computational intelligence and neuroscience
Heart failure is the most common cause of death in both males and females around the world. Cardiovascular diseases (CVDs), in particular, are the main cause of death worldwide, accounting for 30% of all fatalities in the United States and 45% in Eur...

Resting-state electroencephalography based deep-learning for the detection of Parkinson's disease.

PloS one
Parkinson's disease (PD) is one of the most serious and challenging neurodegenerative disorders to diagnose. Clinical diagnosis on observing motor symptoms is the gold standard, yet by this point nerve cells are degenerated resulting in a lower effic...

Concordance rate of radiologists and a commercialized deep-learning solution for chest X-ray: Real-world experience with a multicenter health screening cohort.

PloS one
PURPOSE: Lunit INSIGHT CXR (Lunit) is a commercially available deep-learning algorithm-based decision support system for chest radiography (CXR). This retrospective study aimed to evaluate the concordance rate of radiologists and Lunit for thoracic a...

Blinded intraoperative skill evaluations avoid gender-based bias.

Surgical endoscopy
INTRODUCTION: Gender bias has been identified consistently in written performance evaluations. Qualitative tools may provide a standardized way to evaluate surgical skill and minimize gender bias. We hypothesized that there is no difference in operat...

A Bayesian mixed effects support vector machine for learning and predicting daily substance use disorder patterns.

The American journal of drug and alcohol abuse
Substance use disorder (SUD) is a heterogeneous disorder. Adapting machine learning algorithms to allow for the parsing of intrapersonal and interpersonal heterogeneity in meaningful ways may accelerate the discovery and implementation of clinically...

Robotic abdominal resection of tailgut cysts - A technical note with step-by-step description.

Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland
AIM: Here, we describe a step-by-step standardized technique for tailgut cyst resection using a single-docking robotic approach.

Machine Learning analysis of high-grade serous ovarian cancer proteomic dataset reveals novel candidate biomarkers.

Scientific reports
Ovarian cancer is one of the most common gynecological malignancies, ranking third after cervical and uterine cancer. High-grade serous ovarian cancer (HGSOC) is one of the most aggressive subtype, and the late onset of its symptoms leads in most cas...

Reproducible neuroimaging features for diagnosis of autism spectrum disorder with machine learning.

Scientific reports
Autism spectrum disorder (ASD) is the fourth most common neurodevelopmental disorder, with a prevalence of 1 in 160 children. Accurate diagnosis relies on experts, but such individuals are scarce. This has led to increasing interest in the developmen...