AIMC Topic: Deep Learning

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PCovNet: A presymptomatic COVID-19 detection framework using deep learning model using wearables data.

Computers in biology and medicine
While the advanced diagnostic tools and healthcare management protocols have been struggling to contain the COVID-19 pandemic, the spread of the contagious viral pathogen before the symptom onset acted as the Achilles' heel. Although reverse transcri...

Deep learning DCE-MRI parameter estimation: Application in pancreatic cancer.

Medical image analysis
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an MRI technique for quantifying perfusion that can be used in clinical applications for classification of tumours and other types of diseases. Conventionally, the non-linear least squ...

Real-time denoising of ultrasound images based on deep learning.

Medical & biological engineering & computing
Ultrasound images are widespread in medical diagnosis for muscle-skeletal, cardiac, and obstetrical diseases, due to the efficiency and non-invasiveness of the acquisition methodology. However, ultrasound acquisition introduces noise in the signal, w...

Cardiovascular Disease Diagnosis from DXA Scan and Retinal Images Using Deep Learning.

Sensors (Basel, Switzerland)
Cardiovascular diseases (CVD) are the leading cause of death worldwide. People affected by CVDs may go undiagnosed until the occurrence of a serious heart failure event such as stroke, heart attack, and myocardial infraction. In Qatar, there is a lac...

Deep Learning-Based Method for Compound Identification in NMR Spectra of Mixtures.

Molecules (Basel, Switzerland)
Nuclear magnetic resonance (NMR) spectroscopy is highly unbiased and reproducible, which provides us a powerful tool to analyze mixtures consisting of small molecules. However, the compound identification in NMR spectra of mixtures is highly challeng...

Ensem-HAR: An Ensemble Deep Learning Model for Smartphone Sensor-Based Human Activity Recognition for Measurement of Elderly Health Monitoring.

Biosensors
Biomedical images contain a huge number of sensor measurements that can provide disease characteristics. Computer-assisted analysis of such parameters aids in the early detection of disease, and as a result aids medical professionals in quickly selec...

Evaluation of the models generated from clinical features and deep learning-based segmentations: Can thoracic CT on admission help us to predict hospitalized COVID-19 patients who will require intensive care?

BMC medical imaging
BACKGROUND: The aim of the study was to predict the probability of intensive care unit (ICU) care for inpatient COVID-19 cases using clinical and artificial intelligence segmentation-based volumetric and CT-radiomics parameters on admission.

OptNCMiner: a deep learning approach for the discovery of natural compounds modulating disease-specific multi-targets.

BMC bioinformatics
BACKGROUND: Due to their diverse bioactivity, natural product (NP)s have been developed as commercial products in the pharmaceutical, food and cosmetic sectors as natural compound (NC)s and in the form of extracts. Following administration, NCs typic...

Deep learning-based lesion subtyping and prediction of clinical outcomes in COVID-19 pneumonia using chest CT.

Scientific reports
The main objective of this work is to develop and evaluate an artificial intelligence system based on deep learning capable of automatically identifying, quantifying, and characterizing COVID-19 pneumonia patterns in order to assess disease severity ...

The Use of Deep Learning Model for Effect Analysis of Conventional Friction Power Confinement.

Computational and mathematical methods in medicine
Nonlinear friction could affect the high-precision motion system, resulting in poor tracking accuracy in the end. This is due to the fact that the Lugre friction model's parameter identification process comprises both static and dynamic parameter ide...