Geriatrics

Fully end-to-end deep-learning-based diagnosis of pancreatic tumors.

Artificial intelligence can facilitate clinical decision making by considering massive amounts of medical imaging data. Various algorithms have been implemented for different clinical applications. Accurate diagnosis and treatment require reliable and interpretable data. For pancreatic tumor diagnosis, only 58.5% of images from the First Affiliated Hospital and the Second Affiliated Hospital, Zhej...

Computer-Aided Diagnosis of Alzheimer's Disease through Weak Supervision Deep Learning Framework with Attention Mechanism.

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease causing dementia and poses significant health risks to middle-aged and elderly people. Brain magnetic resonance imaging (MRI) is the most widely used diagnostic method for AD. However, it is challenging to collect sufficient brain imaging data with high-quality annotations. Weakly supervised learning (WSL) is a machine learni...

ARDD 2020: from aging mechanisms to interventions.

Aging is emerging as a druggable target with growing interest from academia, industry and investors. New technologies such as artificial intelligence ...

Deep learning detection of informative features in tau PET for Alzheimer's disease classification.

BACKGROUND: Alzheimer's disease (AD) is the most common type of dementia, typically characterized by memory loss followed by progressive cognitive dec...

Fused Sparse Network Learning for Longitudinal Analysis of Mild Cognitive Impairment.

Alzheimer's disease (AD) is a neurodegenerative disease with an irreversible and progressive process. To understand the brain functions and identify t...

Individualised Responsible Artificial Intelligence for Home-Based Rehabilitation.

Socioeconomic reasons post-COVID-19 demand unsupervised home-based rehabilitation and, specifically, artificial ambient intelligence with individualis...

Identification of Alzheimer's disease using a convolutional neural network model based on T1-weighted magnetic resonance imaging.

The classification of Alzheimer's disease (AD) using deep learning methods has shown promising results, but successful application in clinical setting...

Hardware-Based Hopfield Neuromorphic Computing for Fall Detection.

With the popularity of smart wearable systems, sensor signal processing poses more challenges to machine learning in embedded scenarios. For example, ...

EEG-Based Emotion Classification for Alzheimer's Disease Patients Using Conventional Machine Learning and Recurrent Neural Network Models.

As the number of patients with Alzheimer's disease (AD) increases, the effort needed to care for these patients increases as well. At the same time, a...

Transfer learning for predicting conversion from mild cognitive impairment to dementia of Alzheimer's type based on a three-dimensional convolutional neural network.

Dementia of Alzheimer's type (DAT) is associated with devastating and irreversible cognitive decline. Predicting which patients with mild cognitive im...

Early initiation of PD therapy in elderly patients is associated with increased risk of death.

BACKGROUND: The effect of early initiation of dialysis on outcomes of patients with end-stage renal disease (ESRD) remains controversial. We conducted...

Deep multi-kernel auto-encoder network for clustering brain functional connectivity data.

In this study, we propose a deep-learning network model called the deep multi-kernel auto-encoder clustering network (DMACN) for clustering functional...

PsychoAge and SubjAge: development of deep markers of psychological and subjective age using artificial intelligence.

Aging clocks that accurately predict human age based on various biodata types are among the most important recent advances in biogerontology. Since 20...

A Robust Feature Extraction Model for Human Activity Characterization Using 3-Axis Accelerometer and Gyroscope Data.

Human Activity Recognition (HAR) using embedded sensors in smartphones and smartwatch has gained popularity in extensive applications in health care m...

The realization of robotic neurorehabilitation in clinical: use of computational intelligence and future prospects analysis.

: Although there is a need for rehabilitation treatment with the increase in the aging population, the shortage of skilled physicians frustrates this ...

An end-to-end breast tumour classification model using context-based patch modelling - A BiLSTM approach for image classification.

Researchers working on computational analysis of Whole Slide Images (WSIs) in histopathology have primarily resorted to patch-based modelling due to l...

End-to-End Deep Learning Model for Predicting Treatment Requirements in Neovascular AMD From Longitudinal Retinal OCT Imaging.

Neovascular age-related macular degeneration (nAMD) is nowadays successfully treated with anti-VEGF substances, but inter-individual treatment require...

Task-Driven Learned Hyperspectral Data Reduction Using End-to-End Supervised Deep Learning.

An important challenge in hyperspectral imaging tasks is to cope with the large number of spectral bins. Common spectral data reduction methods do not...

Development of machine learning models to predict RT-PCR results for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in patients with influenza-like symptoms using only basic clinical data.

BACKGROUND: Reverse Transcription-Polymerase Chain Reaction (RT-PCR) for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) diagnosis curren...

Systematic Identification of Molecular Targets and Pathways Related to Human Organ Level Toxicity.

The mechanisms leading to organ level toxicities are poorly understood. In this study, we applied an integrated approach to deduce the molecular targe...