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Disease Progression

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Exploring Longitudinal Cough, Breath, and Voice Data for COVID-19 Progression Prediction via Sequential Deep Learning: Model Development and Validation.

Journal of medical Internet research
BACKGROUND: Recent work has shown the potential of using audio data (eg, cough, breathing, and voice) in the screening for COVID-19. However, these approaches only focus on one-off detection and detect the infection, given the current audio sample, b...

Multimodal deep learning for Alzheimer's disease dementia assessment.

Nature communications
Worldwide, there are nearly 10 million new cases of dementia annually, of which Alzheimer's disease (AD) is the most common. New measures are needed to improve the diagnosis of individuals with cognitive impairment due to various etiologies. Here, we...

DPSSD: Dual-Path Single-Shot Detector.

Sensors (Basel, Switzerland)
Object detection is one of the most important and challenging branches of computer vision. It has been widely used in people's lives, such as for surveillance security and autonomous driving. We propose a novel dual-path multi-scale object detection ...

Development of a deep learning model for the histologic diagnosis of dysplasia in Barrett's esophagus.

Gastrointestinal endoscopy
BACKGROUND AND AIMS: The risk of progression in Barrett's esophagus (BE) increases with development of dysplasia. There is a critical need to improve the diagnosis of BE dysplasia, given substantial interobserver disagreement among expert pathologist...

RA V-Net: deep learning network for automated liver segmentation.

Physics in medicine and biology
Segmenting liver from CT images is the first step for doctors to diagnose a patient's disease. Processing medical images with deep learning models has become a current research trend. Although it can automate segmenting region of interest of medical ...

The promise of artificial intelligence for kidney pathophysiology.

Current opinion in nephrology and hypertension
PURPOSE OF REVIEW: We seek to determine recent advances in kidney pathophysiology that have been enabled or enhanced by artificial intelligence. We describe some of the challenges in the field as well as future directions.

DNL-Net: deformed non-local neural network for blood vessel segmentation.

BMC medical imaging
BACKGROUND: The non-local module has been primarily used in literature to capturing long-range dependencies. However, it suffers from prohibitive computational complexity and lacks the interactions among positions across the channels.

Intracerebral hemorrhage detection on computed tomography images using a residual neural network.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Intracerebral hemorrhage (ICH) is a high mortality rate, critical medical injury, produced by the rupture of a blood vessel of the vascular system inside the skull. ICH can lead to paralysis and even death. Therefore, it is considered a clinically da...

An efficient model of residual based convolutional neural network with Bayesian optimization for the classification of malarial cell images.

Computers in biology and medicine
BACKGROUND: Malaria is a disease caused by the Plasmodium parasite, which results in millions of deaths in the human population worldwide each year. It is therefore considered a major global health issue with a massive disease burden. Accurate and ra...