Alzheimer's Disease

Deep learning algorithms for magnetic resonance imaging of inflammatory sacroiliitis in axial spondyloarthritis.

OBJECTIVE: The aim of this study was to develop a deep learning algorithm for detection of active inflammatory sacroiliitis in short tau inversion recovery (STIR) sequence MRI.

Deep-SMOLM: deep learning resolves the 3D orientations and 2D positions of overlapping single molecules with optimal nanoscale resolution.

Dipole-spread function (DSF) engineering reshapes the images of a microscope to maximize the sensitivity of measuring the 3D orientations of dipole-like emitters. However, severe Poisson shot noise, overlapping images, and simultaneously fitting high-dimensional information-both orientation and position-greatly complicates image analysis in single-molecule orientation-localization microscopy (SMOL...

Clinical significance of hepatic function in Graves disease with type 2 diabetic mellitus: A single-center retrospective cross-sectional study in Taiwan.

Graves disease (GD) and type 2 diabetes mellitus (T2DM) both impair liver function; we therefore explored the possibility of a relationship among diab...

Same-Day Discharge Protocol for Robot-Assisted Radical Prostatectomy: Experience of a High-Volume Referral Center.

As the coronavirus disease 2019 (COVID-19) global pandemic continues, there is increased value in performing same-day discharge (SDD) protocols to mi...

A novel deep learning approach using AlexNet for the classification of electroencephalograms in Alzheimer's Disease and Mild Cognitive Impairment.

Alzheimer's Disease (AD) is the most common form of dementia. Mild Cognitive Impairment (MCI) is the term given to the stage describing prodromal AD a...

Probing the link between the APOE-ε4 allele and whole-brain gray matter using deep learning.

The APOE-ε4 allele is a known genetic risk for Alzheimer's disease (AD). Thus, it can be reasoned that the APOE-ε4 allele would also impact neurodegen...

Use of Robots to Support Those Living with Dementia and Their Caregivers.

Dementia and other related diseases causing symptoms of mild cognitive impairment are being increasingly diagnosed. These diseases are placing a signi...

Explainable Artificial Intelligence in Ambulatory Digital Dementia Screenings.

Recently, digital apps have entered the market to enable the early diagnosis of dementia by offering digital dementia screenings. Some of these apps u...

GPT-D: Inducing Dementia-related Linguistic Anomalies by Deliberate Degradation of Artificial Neural Language Models.

Deep learning (DL) techniques involving fine-tuning large numbers of model parameters have delivered impressive performance on the task of discriminat...

Deep learning-based identification of genetic variants: application to Alzheimer's disease classification.

Deep learning is a promising tool that uses nonlinear transformations to extract features from high-dimensional data. Deep learning is challenging in ...

Differential Role for Hippocampal Subfields in Alzheimer's Disease Progression Revealed with Deep Learning.

Mild cognitive impairment (MCI) is often considered the precursor of Alzheimer's disease. However, MCI is associated with substantially variable progr...

Advances in Deep Neuropathological Phenotyping of Alzheimer Disease: Past, Present, and Future.

Alzheimer disease (AD) is a neurodegenerative disorder characterized pathologically by the presence of neurofibrillary tangles and amyloid beta (Aβ) p...

The Use of Robotics in Dementia Care: An Ethical Perspective.

Dementia and other related diseases are becoming increasingly diagnosed and are placing a serious strain on the healthcare system. Robotic technology ...

Identifying Mild Cognitive Impairment by Using Human-Robot Interactions.

BACKGROUND: Mild cognitive impairment (MCI), which is common in older adults, is a risk factor for dementia. Rapidly growing health care demand associ...

Utility of Machine Learning Approach with Neuropsychological Tests in Predicting Functional Impairment of Alzheimer's Disease.

BACKGROUND: In assessing the levels of clinical impairment in dementia, a summary index of neuropsychological batteries has been widely used in descri...

Chronic Beneficial Effect of Makeup Therapy on Cognitive Function of Dementia and Facial Appearance Analyzed by Artificial Intelligence Software.

BACKGROUND: Makeup greatly impacts normal social lives but can also be a non-pharmacological form of therapy for dementia.

A Machine Learning-Based Holistic Approach to Predict the Clinical Course of Patients within the Alzheimer's Disease Spectrum.

BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative condition driven by multifactorial etiology. Mild cognitive impairment (MCI) is a transiti...

The Use of Machine Learning in MicroRNA Diagnostics: Current Perspectives.

MicroRNAs constitute small non-coding RNAs that play a pivotal role in regulating the translation and degradation of mRNA and have been associated wit...

Effects of Exercise-Diet Therapy on Cognitive Function in Healthy Elderly People Evaluated by Deep Learning Based on Basic Blood Test Data.

BACKGROUND: Recent studies reported that vascular cognitive impairment in the elderly caused by arteriosclerosis plays an important role in cognitive ...

Applications and Challenges of Machine Learning Methods in Alzheimer's Disease Multi-Source Data Analysis.

BACKGROUND: Recent development in neuroimaging and genetic testing technologies have made it possible to measure pathological features associated with...