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DrABC: deep learning accurately predicts germline pathogenic mutation status in breast cancer patients based on phenotype data.

Genome medicine
BACKGROUND: Identifying breast cancer patients with DNA repair pathway-related germline pathogenic variants (GPVs) is important for effectively employing systemic treatment strategies and risk-reducing interventions. However, current criteria and ris...

Ensemble Deep Learning and Internet of Things-Based Automated COVID-19 Diagnosis Framework.

Contrast media & molecular imaging
Coronavirus disease (COVID-19) is a viral infection caused by SARS-CoV-2. The modalities such as computed tomography (CT) have been successfully utilized for the early stage diagnosis of COVID-19 infected patients. Recently, many researchers have uti...

Assessing port service quality: An application of the extension fuzzy AHP and importance-performance analysis.

PloS one
It is argued that ports are playing a crucial role in developing nations' economy. Still, solutions to improving port service quality (PSQ) to boost ports' competitive capacity is questionable. Hence, this study aims to investigate port service quali...

Automated detection scheme for acute myocardial infarction using convolutional neural network and long short-term memory.

PloS one
The early detection of acute myocardial infarction, which is caused by lifestyle-related risk factors, is essential because it can lead to chronic heart failure or sudden death. Echocardiography, among the most common methods used to detect acute myo...

Impact of Age and Sex on COVID-19 Severity Assessed From Radiologic and Clinical Findings.

Frontiers in cellular and infection microbiology
BACKGROUND: Data on the epidemiological characteristics and clinical features of COVID-19 in patients of different ages and sex are limited. Existing studies have mainly focused on the pediatric and elderly population.

Use of deep learning in the MRI diagnosis of Chiari malformation type I.

Neuroradiology
PURPOSE: To train deep learning convolutional neural network (CNN) models for classification of clinically significant Chiari malformation type I (CM1) on MRI to assist clinicians in diagnosis and decision making.

Normalization of photoplethysmography using deep neural networks for individual and group comparison.

Scientific reports
Photoplethysmography (PPG) is easy to measure and provides important parameters related to heart rate and arrhythmia. However, automated PPG methods have not been developed because of their susceptibility to motion artifacts and differences in wavefo...

Deep learning-based quantitative analyses of spontaneous movements and their association with early neurological development in preterm infants.

Scientific reports
This study aimed to develop quantitative assessments of spontaneous movements in high-risk preterm infants based on a deep learning algorithm. Video images of spontaneous movements were recorded in very preterm infants at the term-equivalent age. The...

Machine learning detects altered spatial navigation features in outdoor behaviour of Alzheimer's disease patients.

Scientific reports
Impairment of navigation is one of the earliest symptoms of Alzheimer's disease (AD), but to date studies have involved proxy tests of navigation rather than studies of real life behaviour. Here we use GPS tracking to measure ecological outdoor behav...