Geriatrics

Diagnosis of Thyroid Nodule Malignancy Using Peritumoral Region and Artificial Intelligence: Results of Hand-Crafted, Deep Radiomics Features and Radiologists' Assessment in Multicenter Cohorts.

OBJECTIVE: To develop, test, and externally validate a hybrid artificial intelligence (AI) model based on hand-crafted and deep radiomics features extracted from B-mode ultrasound images in differentiating benign and malignant thyroid nodules compared to senior and junior radiologists.

Reducing inference cost of Alzheimer's disease identification using an uncertainty-aware ensemble of uni-modal and multi-modal learners.

While multi-modal deep learning approaches trained using magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG PET) data have shown promise in the accurate identification of Alzheimer's disease, their clinical applicability is hindered by the assumption that both modalities are always available during model inference. In practice, clinicians adjust diagnostic te...

Comparison of logistic regression and machine learning methods for predicting depression risks among disabled elderly individuals: results from the China Health and Retirement Longitudinal Study.

BACKGROUND: Given the accelerated aging population in China, the number of disabled elderly individuals is increasing, and depression is a common ment...

Exploring Potential Medications for Alzheimer's Disease with Psychosis by Integrating Drug Target Information into Deep Learning Models: A Data-Driven Approach.

Approximately 50% of Alzheimer's disease (AD) patients develop psychotic symptoms, leading to a subtype known as psychosis in AD (AD + P), which is as...

Data Reconstruction Methods in Multi-Feature Fusion CNN Model for Enhanced Human Activity Recognition.

BACKGROUND: Human activity recognition (HAR) plays a pivotal role in digital healthcare, enabling applications such as exercise monitoring and elderly...

Hybrid-RViT: Hybridizing ResNet-50 and Vision Transformer for Enhanced Alzheimer's disease detection.

Alzheimer's disease (AD) is a leading cause of disability worldwide. Early detection is critical for preventing progression and formulating effective ...

Taylor-dingo optimized RP-net for segmentation toward Alzheimer's disease detection and classification using deep learning.

Alzheimer's Disease (AD) is a significant cause of mortality in elderly people. The diagnosing and classification of AD using conventional manual oper...

A quantitatively interpretable model for Alzheimer's disease prediction using deep counterfactuals.

Deep learning (DL) for predicting Alzheimer's disease (AD) has provided timely intervention in disease progression yet still demands attentive interpr...

Functional MRI-based machine learning strategy for prediction of postoperative delirium in cardiac surgery patients: A secondary analysis of a prospective observational study.

STUDY OBJECTIVE: Delirium is a common complication after cardiac surgery and is associated with poor prognosis. An effective delirium prediction model...

Prediction of cognitive conversion within the Alzheimer's disease continuum using deep learning.

BACKGROUND: Early diagnosis and accurate prognosis of cognitive decline in Alzheimer's disease (AD) is important to timely assignment to optimal treat...

Artificial intelligence for opportunistic osteoporosis screening with a Hounsfield Unit in chronic obstructive pulmonary disease patients.

INTRODUCTION: To investigate the accuracy of an artificial intelligence (AI) prototype in determining bone mineral density (BMD) in chronic obstructiv...

ZF-QDCNN: ZFNet and quantum dilated convolutional neural network based Alzheimer's disease detection using MRI images.

Alzheimer's disease (AD) is a severe neurological disorder that leads to irreversible memory loss. In the previous research, the early-stage Alzheimer...

Chat GPT vs. Clinical Decision Support Systems in the Analysis of Drug-Drug Interactions.

The current standard method for the analysis of potential drug-drug interactions (pDDIs) is time-consuming and includes the use of multiple clinical d...

Alzheimer's Disease detection and classification using optimized neural network.

Alzheimer's disease (AD) is a degenerative neurological condition characterized by a progressive decline in cognitive abilities, resulting in memory i...

Interpretable deep learning of single-cell and epigenetic data reveals novel molecular insights in aging.

Deep learning (DL) and explainable artificial intelligence (XAI) have emerged as powerful machine-learning tools to identify complex predictive data p...

Prediction of Multimorbidity Network Evolution in Middle-Aged and Elderly Population Based on CE-GCN.

PURPOSE: With the evolving disease spectrum, chronic diseases have emerged as a primary burden and a leading cause of mortality. Due to the aging popu...

Mapping the learning curves of deep learning networks.

There is an important challenge in systematically interpreting the internal representations of deep neural networks (DNNs). Existing techniques are of...

Alzheimer's disease classification using hybrid loss Psi-Net segmentation and a new hybrid network model.

Alzheimer's disease (AD) is a type of brain disorder that is becoming more prevalent worldwide. It is a progressive and irreversible condition that gr...

SH: Long-tailed classification via spatial constraint sampling, scalable network, and hybrid task.

Long-tailed classification is a significant yet challenging vision task that aims to making the clearest decision boundaries via integrating semantic ...

EEGConvNeXt: A novel convolutional neural network model for automated detection of Alzheimer's Disease and Frontotemporal Dementia using EEG signals.

BACKGROUND AND OBJECTIVE: Deep learning models have gained widespread adoption in healthcare for accurate diagnosis through the analysis of brain sign...