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Disease Progression

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Value of CT quantification in progressive fibrosing interstitial lung disease: a deep learning approach.

European radiology
OBJECTIVES: To evaluate the relationship of changes in the deep learning-based CT quantification of interstitial lung disease (ILD) with changes in forced vital capacity (FVC) and visual assessments of ILD progression, and to investigate their progno...

Analyses of Factors Associated with Acute Exacerbations of Chronic Obstructive Pulmonary Disease: A Review.

International journal of chronic obstructive pulmonary disease
Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) is the exacerbation of a range of respiratory symptoms during the stable phase of chronic obstructive pulmonary disease (COPD). AECOPD is thus a dangerous stage and key event in th...

Machine learning algorithms for the prognostication of abdominal aortic aneurysm progression: a systematic review.

Minerva surgery
INTRODUCTION: Abdominal aortic aneurysm (AAA), often characterized by an abdominal aortic diameter over 3.0 cm, is managed through screening, surveillance, and surgical intervention. AAA growth can be heterogeneous and rupture carries a high mortalit...

Predicting glaucoma progression using deep learning framework guided by generative algorithm.

Scientific reports
Glaucoma is a slowly progressing optic neuropathy that may eventually lead to blindness. To help patients receive customized treatment, predicting how quickly the disease will progress is important. Structural assessment using optical coherence tomog...

U-net convolutional neural network applied to progressive fibrotic interstitial lung disease: Is progression at CT scan associated with a clinical outcome?

Respiratory medicine and research
BACKGROUND: Computational advances in artificial intelligence have led to the recent emergence of U-Net convolutional neural networks (CNNs) applied to medical imaging. Our objectives were to assess the progression of fibrotic interstitial lung disea...

Algorithmic Fairness of Machine Learning Models for Alzheimer Disease Progression.

JAMA network open
IMPORTANCE: Predictive models using machine learning techniques have potential to improve early detection and management of Alzheimer disease (AD). However, these models potentially have biases and may perpetuate or exacerbate existing disparities.

Multi-Level Ethical Considerations of Artificial Intelligence Health Monitoring for People Living with Parkinson's Disease.

AJOB empirical bioethics
Artificial intelligence (AI) has garnered tremendous attention in health care, and many hope that AI can enhance our health system's ability to care for people with chronic and degenerative conditions, including Parkinson's Disease (PD). This paper r...

Predicting of diabetic retinopathy development stages of fundus images using deep learning based on combined features.

PloS one
The number of diabetic retinopathy (DR) patients is increasing every year, and this causes a public health problem. Therefore, regular diagnosis of diabetes patients is necessary to avoid the progression of DR stages to advanced stages that lead to b...

Distinct subtypes of spatial brain metabolism patterns in Alzheimer's disease identified by deep learning-based FDG PET clusters.

European journal of nuclear medicine and molecular imaging
PURPOSE: Alzheimer's disease (AD) is a heterogeneous disease that presents a broad spectrum of clinicopathologic profiles. To date, objective subtyping of AD independent of disease progression using brain imaging has been required. Our study aimed to...