AIMC Topic: Humans

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Diabetes detection using deep learning techniques with oversampling and feature augmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Diabetes is a chronic pathology which is affecting more and more people over the years. It gives rise to a large number of deaths each year. Furthermore, many people living with the disease do not realize the seriousness of ...

Deep learning-accelerated T2-weighted imaging of the prostate: Reduction of acquisition time and improvement of image quality.

European journal of radiology
PURPOSE: To introduce a novel deep learning (DL) T2-weighted TSE imaging (T2) sequence in prostate MRI and investigate its impact on examination time, image quality, diagnostic confidence, and PI-RADS classification compared to standard T2-weighted T...

A method to screen left ventricular dysfunction through ECG based on convolutional neural network.

Journal of cardiovascular electrophysiology
OBJECTIVE: This study aims to develop an artificial intelligence-based method to screen patients with left ventricular ejection fraction (LVEF) of 50% or lesser using electrocardiogram (ECG) data alone.

Deep learning-based X-ray inpainting for improving spinal 2D-3D registration.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: Two-dimensional (2D)-3D registration is challenging in the presence of implant projections on intraoperative images, which can limit the registration capture range. Here, we investigate the use of deep-learning-based inpainting for removi...

CASSPER is a semantic segmentation-based particle picking algorithm for single-particle cryo-electron microscopy.

Communications biology
Particle identification and selection, which is a prerequisite for high-resolution structure determination of biological macromolecules via single-particle cryo-electron microscopy poses a major bottleneck for automating the steps of structure determ...

Fast and precise single-cell data analysis using a hierarchical autoencoder.

Nature communications
A primary challenge in single-cell RNA sequencing (scRNA-seq) studies comes from the massive amount of data and the excess noise level. To address this challenge, we introduce an analysis framework, named single-cell Decomposition using Hierarchical ...

Machine learning identifies candidates for drug repurposing in Alzheimer's disease.

Nature communications
Clinical trials of novel therapeutics for Alzheimer's Disease (AD) have consumed a large amount of time and resources with largely negative results. Repurposing drugs already approved by the Food and Drug Administration (FDA) for another indication i...

Multimodal temporal-clinical note network for mortality prediction.

Journal of biomedical semantics
BACKGROUND: Mortality prediction is an important task to achieve smart healthcare, especially for the management of intensive care unit. It can provide a reference for doctors to quickly predict the course of disease and customize early intervention ...

Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making.

BMC medical informatics and decision making
BACKGROUND: Machine learning is a broad term encompassing a number of methods that allow the investigator to learn from the data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient-prov...