AIMC Topic: Disease Progression
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A parallel attention-augmented bilinear network for early magnetic resonance imaging-based diagnosis of Alzheimer's disease.
Human brain mapping
Oct 22, 2021
Structural magnetic resonance imaging (sMRI) can capture the spatial patterns of brain atrophy in Alzheimer's disease (AD) and incipient dementia. Recently, many sMRI-based deep learning methods have been developed for AD diagnosis. Some of these met...
Deep-learning-based synthesis of post-contrast T1-weighted MRI for tumour response assessment in neuro-oncology: a multicentre, retrospective cohort study.
The Lancet. Digital health
Oct 20, 2021
BACKGROUND: Gadolinium-based contrast agents (GBCAs) are widely used to enhance tissue contrast during MRI scans and play a crucial role in the management of patients with cancer. However, studies have shown gadolinium deposition in the brain after r...
Neural Networks, Computer
Algorithms
Brain
Prognosis
Humans
Disease Progression
Deep Learning
Contrast Media
Magnetic Resonance Imaging
Glioblastoma
Germany
Radiology
Gadolinium
Retrospective Studies
Feasibility Studies
Diffusion Magnetic Resonance Imaging
Brain Neoplasms
Middle Aged
Tumor Burden
Neoplasms
A deep learning method for predicting knee osteoarthritis radiographic progression from MRI.
Arthritis research & therapy
Oct 18, 2021
BACKGROUND: The identification of patients with knee osteoarthritis (OA) likely to progress rapidly in terms of structure is critical to facilitate the development of disease-modifying drugs.
Artificial intelligence approach towards assessment of condition of COVID-19 patients - Identification of predictive biomarkers associated with severity of clinical condition and disease progression.
Computers in biology and medicine
Sep 14, 2021
BACKGROUND AND OBJECTIVES: Although ML has been studied for different epidemiological and clinical issues as well as for survival prediction of COVID-19, there is a noticeable shortage of literature dealing with ML usage in prediction of disease seve...
Performance improvement of weakly supervised fully convolutional networks by skip connections for brain structure segmentation.
Medical physics
Sep 13, 2021
PURPOSE: For the planning and navigation of neurosurgery, we have developed a fully convolutional network (FCN)-based method for brain structure segmentation on magnetic resonance (MR) images. The capability of an FCN depends on the quality of the tr...
Machine Learning-Based Individualized Survival Prediction Model for Total Knee Replacement in Osteoarthritis: Data From the Osteoarthritis Initiative.
Arthritis care & research
Aug 26, 2021
OBJECTIVE: By using machine learning, our study aimed to build a model to predict risk and time to total knee replacement (TKR) of an osteoarthritic knee.
DeepAtrophy: Teaching a neural network to detect progressive changes in longitudinal MRI of the hippocampal region in Alzheimer's disease.
NeuroImage
Aug 24, 2021
Measures of change in hippocampal volume derived from longitudinal MRI are a well-studied biomarker of disease progression in Alzheimer's disease (AD) and are used in clinical trials to track therapeutic efficacy of disease-modifying treatments. Howe...
Imaging and artificial intelligence for progression of age-related macular degeneration.
Experimental biology and medicine (Maywood, N.J.)
Aug 18, 2021
Age-related macular degeneration (AMD) is a leading cause of severe vision loss. With our aging population, it may affect 288 million people globally by the year 2040. AMD progresses from an early and intermediate dry form to an advanced one, which m...
Chronic kidney disease diagnosis using decision tree algorithms.
BMC nephrology
Aug 9, 2021
BACKGROUND: Chronic Kidney Disease (CKD), i.e., gradual decrease in the renal function spanning over a duration of several months to years without any major symptoms, is a life-threatening disease. It progresses in six stages according to the severit...
Using normative modelling to detect disease progression in mild cognitive impairment and Alzheimer's disease in a cross-sectional multi-cohort study.
Scientific reports
Aug 3, 2021
Normative modelling is an emerging method for quantifying how individuals deviate from the healthy populational pattern. Several machine learning models have been implemented to develop normative models to investigate brain disorders, including regre...
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