AIMC Topic: Deep Learning

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A deep-learning-based unsupervised model on esophageal manometry using variational autoencoder.

Artificial intelligence in medicine
High-resolution manometry (HRM) is the primary method for diagnosing esophageal motility disorders and its interpretation and classification are based on variables (features) from data of each swallow. Modeling and learning the semantics directly fro...

Deep learning trained algorithm maintains the quality of half-dose contrast-enhanced liver computed tomography images: Comparison with hybrid iterative reconstruction: Study for the application of deep learning noise reduction technology in low dose.

European journal of radiology
PURPOSE: This study compares the image and diagnostic qualities of a DEep Learning Trained Algorithm (DELTA) for half-dose contrast-enhanced liver computed tomography (CT) with those of a commercial hybrid iterative reconstruction (HIR) method used f...

Osteoarthritis year in review 2020: imaging.

Osteoarthritis and cartilage
This narrative "Year in Review" highlights a selection of articles published between January 2019 and April 2020, to be presented at the OARSI World Congress 2020 within the field of osteoarthritis (OA) imaging. Articles were obtained from a PubMed s...

Discovering and Visualizing Disease-Specific Electrocardiogram Features Using Deep Learning: Proof-of-Concept in Phospholamban Gene Mutation Carriers.

Circulation. Arrhythmia and electrophysiology
BACKGROUND: ECG interpretation requires expertise and is mostly based on physician recognition of specific patterns, which may be challenging in rare cardiac diseases. Deep neural networks (DNNs) can discover complex features in ECGs and may facilita...

Mask R-CNN and OBIA Fusion Improves the Segmentation of Scattered Vegetation in Very High-Resolution Optical Sensors.

Sensors (Basel, Switzerland)
Vegetation generally appears scattered in drylands. Its structure, composition and spatial patterns are key controls of biotic interactions, water, and nutrient cycles. Applying segmentation methods to very high-resolution images for monitoring chang...

Identification of Children at Risk of Schizophrenia via Deep Learning and EEG Responses.

IEEE journal of biomedical and health informatics
The prospective identification of children likely to develop schizophrenia is a vital tool to support early interventions that can mitigate the risk of progression to clinical psychosis. Electroencephalographic (EEG) patterns from brain activity and ...

Open-Source Automatic Segmentation of Ocular Structures and Biomarkers of Microbial Keratitis on Slit-Lamp Photography Images Using Deep Learning.

IEEE journal of biomedical and health informatics
We propose a fully-automatic deep learning-based algorithm for segmentation of ocular structures and microbial keratitis (MK) biomarkers on slit-lamp photography (SLP) images. The dataset consisted of SLP images from 133 eyes with manual annotations ...

Prediction of Obstructive Lung Disease from Chest Radiographs via Deep Learning Trained on Pulmonary Function Data.

International journal of chronic obstructive pulmonary disease
BACKGROUND: Chronic obstructive pulmonary disease (COPD), the third leading cause of death worldwide, is often underdiagnosed.

Transfer Learning for COVID-19 cases and deaths forecast using LSTM network.

ISA transactions
In this paper, Transfer Learning is used in LSTM networks to forecast new COVID cases and deaths. Models trained in data from early COVID infected countries like Italy and the United States are used to forecast the spread in other countries. Single a...

The prognostic value of automated coronary calcium derived by a deep learning approach on non-ECG gated CT images from Rb-PET/CT myocardial perfusion imaging.

International journal of cardiology
BACKGROUND: Assessment of both coronary artery calcium(CAC) scores and myocardial perfusion imaging(MPI) in patients suspected of coronary artery disease(CAD) provides incremental prognostic information. We used an automated method to determine CAC s...