AIMC Topic: Disease Progression
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Atrous residual convolutional neural network based on U-Net for retinal vessel segmentation.
PloS one
Aug 22, 2022
Extracting features of retinal vessels from fundus images plays an essential role in computer-aided diagnosis of diseases, such as diabetes, hypertension, and cerebrovascular diseases. Although a number of deep learning-based methods have been used i...
Deep Learning-Based Computed Tomography Features in Evaluating Early Screening and Risk Factors for Chronic Obstructive Pulmonary Disease.
Contrast media & molecular imaging
Aug 18, 2022
This research aimed to investigate the diagnostic effect of computed tomography (CT) images based on a deep learning double residual convolution neural network (DRCNN) model on chronic obstructive pulmonary disease (COPD) and the related risk factors...
Deep learning methods to predict amyotrophic lateral sclerosis disease progression.
Scientific reports
Aug 12, 2022
Amyotrophic lateral sclerosis (ALS) is a highly complex and heterogeneous neurodegenerative disease that affects motor neurons. Since life expectancy is relatively low, it is essential to promptly understand the course of the disease to better target...
Predicting Topographic Disease Progression and Treatment Response of Pegcetacoplan in Geographic Atrophy Quantified by Deep Learning.
Ophthalmology. Retina
Aug 7, 2022
PURPOSE: To identify disease activity and effects of intravitreal pegcetacoplan treatment on the topographic progression of geographic atrophy (GA) secondary to age-related macular degeneration quantified in spectral-domain OCT (SD-OCT) by automated ...
Machine learning algorithms' accuracy in predicting kidney disease progression: a systematic review and meta-analysis.
BMC medical informatics and decision making
Aug 1, 2022
BACKGROUND: Kidney disease progression rates vary among patients. Rapid and accurate prediction of kidney disease outcomes is crucial for disease management. In recent years, various prediction models using Machine Learning (ML) algorithms have been ...
Deep learning imaging features derived from kidney ultrasounds predict chronic kidney disease progression in children with posterior urethral valves.
Pediatric nephrology (Berlin, Germany)
Jul 22, 2022
BACKGROUND: We sought to use deep learning to extract anatomic features from postnatal kidney ultrasounds and evaluate their performance in predicting the risk and timing of chronic kidney disease (CKD) progression for boys with posterior urethral va...
A deep learning model for discriminating true progression from pseudoprogression in glioblastoma patients.
Journal of neuro-oncology
Jul 19, 2022
INTRODUCTION: Glioblastomas (GBMs) are highly aggressive tumors. A common clinical challenge after standard of care treatment is differentiating tumor progression from treatment-related changes, also known as pseudoprogression (PsP). Usually, PsP res...
Classification of multi-lead ECG with deep residual convolutional neural networks.
Physiological measurement
Jul 18, 2022
. Automatic electrocardiogram (ECG) interpretation based on deep learning methods is attracting increasing attention. In this study, we propose a novel method to accurately classify multi-lead ECGs using deep residual neural networks.. ECG recordings...
A Deep Learning Approach for Automated Segmentation of Kidneys and Exophytic Cysts in Individuals with Autosomal Dominant Polycystic Kidney Disease.
Journal of the American Society of Nephrology : JASN
Jun 29, 2022
BACKGROUND: Total kidney volume (TKV) is an important imaging biomarker in autosomal dominant polycystic kidney disease (ADPKD). Manual computation of TKV, particularly with the exclusion of exophytic cysts, is laborious and time consuming.
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
Jun 21, 2022
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...
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