Geriatrics

Hybrid of DSR-GAN and CNN for Alzheimer disease detection based on MRI images.

In this paper, we propose a deep super-resolution generative adversarial network (DSR-GAN) combined with a convolutional neural network (CNN) model designed to classify four stages of Alzheimer's disease (AD): Mild Dementia (MD), Moderate Dementia (MOD), Non-Demented (ND), and Very Mild Dementia (VMD). The proposed DSR-GAN is implemented using a PyTorch library and uses a dataset of 6,400 MRI imag...

Deep learning enabled liquid-based cytology model for cervical precancer and cancer detection.

Deep learning (DL) enabled liquid-based cytology has potential for cervical cancer screening or triage. Here, we develop a DL model using whole cytology slides from 17,397 women and test it on 10,826 additional cases through a three-stage process. The DL model achieves robust performance across nine hospitals. In a multi-reader, multi-case study, it outperforms cytopathologists' sensitivity by 9%....

IT: An interpretable transformer model for Alzheimer's disease prediction based on PET/MR images.

Alzheimer's disease (AD) represents a significant challenge due to its progressive neurodegenerative impact, particularly within an aging global demog...

Exploring the perceptions and experiences of caregivers with the application of socially assistive robots in dementia care: A systematic review of qualitative studies.

BACKGROUND: The use of socially assistive robots in dementia care is growing, aimed at improving patient well-being and alleviating caregiver burden. ...

Evaluating a customised large language model (DELSTAR) and its ability to address medication-related questions associated with delirium: a quantitative exploratory study.

BACKGROUND: A customised large language model (LLM) could serve as a next-generation clinical pharmacy research assistant to prevent medication-associ...

The Role of ChatGPT in osteoporosis management: a comparative analysis with clinical expertise.

UNLABELLED: This study evaluates the role of ChatGPT in osteoporosis management, demonstrating 91% diagnostic accuracy and significantly faster respon...

Machine learning approach for the prediction of 30-day mortality in patients with sepsis-associated delirium.

This study aimed to develop models for predicting the 30-day mortality of sepsis-associated delirium (SAD) by multiple machine learning (ML) algorithm...

Optimization of shunting operation plan in large freight train depot based on DQN algorithm.

Shunting operation plan is the main daily work of the freight train depot, the optimization of shunting operation plan is of great significance to imp...

The Cognivue Amyloid Risk Measure (CARM): A Novel Method to Predict the Presence of Amyloid with Cognivue Clarity.

INTRODUCTION: At the present time, clinical detection of individuals who have amyloid in their brain is not possible without expensive biomarkers. The...

Validation of body composition parameters extracted via deep learning-based segmentation from routine computed tomographies.

Sarcopenia and body composition metrics are strongly associated with patient outcomes. In this study, we developed and validated a flexible, open-acce...

Ensemble deep learning for Alzheimer's disease diagnosis using MRI: Integrating features from VGG16, MobileNet, and InceptionResNetV2 models.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by the accumulation of amyloid plaques and neurofibrillary tangles in the brain...

Unsupervised brain MRI tumour segmentation via two-stage image synthesis.

Deep learning shows promise in automated brain tumour segmentation, but it depends on costly expert annotations. Recent advances in unsupervised learn...

Design of an Interactive Exercise and Leisure System for the Elderly Integrating Artificial Intelligence and Motion-Sensing Technology.

In response to the global trend of population aging, the issue of providing elderly individuals suitable leisure and entertainment has become increasi...

An isodose-constrained automatic treatment planning strategy using a multicriteria predicted dose rating.

BACKGROUND: Previous knowledge-based planning studies have demonstrated the feasibility of predicting three-dimensional photon dose distributions and ...

The impact of Alzheimer's disease on cortical complexity and its underlying biological mechanisms.

BACKGROUND: Alzheimer's disease (AD) might impact the complexity of cerebral cortex, and the underlying biological mechanisms responsible for cortical...

A fine-tuned convolutional neural network model for accurate Alzheimer's disease classification.

Alzheimer's disease (AD) is one of the primary causes of dementia in the older population, affecting memories, cognitive levels, and the ability to ac...

Deep Geometric Learning With Monotonicity Constraints for Alzheimer's Disease Progression.

Alzheimer's disease (AD) is a devastating neurodegenerative condition that precedes progressive and irreversible dementia; thus, predicting its progre...

A Novel Hierarchical Cross-Stream Aggregation Neural Network for Semantic Segmentation of 3-D Dental Surface Models.

Accurate teeth delineation on 3-D dental models is essential for individualized orthodontic treatment planning. Pioneering works like PointNet suggest...

LGG-NeXt: A Next Generation CNN and Transformer Hybrid Model for the Diagnosis of Alzheimer's Disease Using 2D Structural MRI.

Incurable Alzheimer's disease (AD) plagues many elderly people and families. It is important to accurately diagnose and predict it at an early stage. ...

TransformDDI: The Transformer-Based Joint Multi-Task Model for End-to-End Drug-Drug Interaction Extraction.

Drug-Drug Interactions (DDI) identification is a part of the drug safety process, that focuses at avoiding potential adverse drug effects that can lea...