Geriatrics

Supervised deep learning with vision transformer predicts delirium using limited lead EEG.

As many as 80% of critically ill patients develop delirium increasing the need for institutionalization and higher morbidity and mortality. Clinicians detect less than 40% of delirium when using a validated screening tool. EEG is the criterion standard but is resource intensive thus not feasible for widespread delirium monitoring. This study evaluated the use of limited-lead rapid-response EEG and...

E2EFP-MIL: End-to-end and high-generalizability weakly supervised deep convolutional network for lung cancer classification from whole slide image.

Efficient and accurate distinction of histopathological subtype of lung cancer is quite critical for the individualized treatment. So far, artificial intelligence techniques have been developed, whose performance yet remained debatable on more heterogenous data, hindering their clinical deployment. Here, we propose an end-to-end, well-generalized and data-efficient weakly supervised deep learning-...

PPSW-SHAP: Towards Interpretable Cell Classification Using Tree-Based SHAP Image Decomposition and Restoration for High-Throughput Bright-Field Imaging.

Advancements in high-throughput microscopy imaging have transformed cell analytics, enabling functionally relevant, rapid, and in-depth bioanalytics w...

Development and validation of an automatic classification algorithm for the diagnosis of Alzheimer's disease using a high-performance interpretable deep learning network.

OBJECTIVES: To develop and validate an automatic classification algorithm for diagnosing Alzheimer's disease (AD) or mild cognitive impairment (MCI).

Major hepatectomy in elderly patients: possible benefit from robotic platform utilization.

INTRODUCTION: Robotic surgery has been increasingly utilized, yet its application for hepato-pancreato-biliary (HPB) procedures remains low due to tec...

An accelerated end-to-end method for solving routing problems.

The application of neural network models to solve combinatorial optimization has recently drawn much attention and shown promising results in dealing ...

In-Sensor Artificial Intelligence and Fusion With Electronic Medical Records for At-Home Monitoring.

This work presents an artificial intelligence (AI) framework for real-time, personalized sepsis prediction four hours before onset through fusion of e...

Explainable semi-supervised deep learning shows that dementia is associated with small, avocado-shaped clocks with irregularly placed hands.

The clock drawing test is a simple and inexpensive method to screen for cognitive frailties, including dementia. In this study, we used the relevance ...

Deep-Learning-Based Detection of Vertebral Fracture and Osteoporosis Using Lateral Spine X-Ray Radiography.

Osteoporosis and vertebral fractures (VFs) remain underdiagnosed. The addition of deep learning methods to lateral spine radiography (a simple, widely...

Wide and deep learning based approaches for classification of Alzheimer's disease using genome-wide association studies.

The increasing incidence of Alzheimer's disease (AD) has been leading towards a significant growth in socioeconomic challenges. A reliable prediction ...

Decoding Silent Speech Based on High-Density Surface Electromyogram Using Spatiotemporal Neural Network.

Finer-grained decoding at a phoneme or syllable level is a key technology for continuous recognition of silent speech based on surface electromyogram ...

Implementing PainChek and PARO to Support Pain Assessment and Management in Residents with Dementia: A Qualitative Study.

BACKGROUND: Pain is a common problem but often undiagnosed and untreated in people with dementia.

Assessment of Awareness, Perceptions, and Opinions towards Artificial Intelligence among Healthcare Students in Riyadh, Saudi Arabia.

: The role of the pharmacist in healthcare society is unique, since they are providers of health information and medication counseling to patients. He...

Revolutionizing the Early Detection of Alzheimer's Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning.

Alzheimer's disease (AD) is now classified as a silent pandemic due to concerning current statistics and future predictions. Despite this, no effectiv...

High performance for bone age estimation with an artificial intelligence solution.

PURPOSE: The purpose of this study was to compare the performance of an artificial intelligence (AI) solution to that of a senior general radiologist ...

XAI-enabled neural network analysis of metabolite spatial distributions.

We used deep neural networks to process the mass spectrometry imaging (MSI) data of mouse muscle (young vs aged) and human cancer (tumor vs normal adj...

Deep learning methods for medical image fusion: A review.

The image fusion methods based on deep learning has become a research hotspot in the field of computer vision in recent years. This paper reviews thes...

The effect of variable labels on deep learning models trained to predict breast density.

. High breast density is associated with reduced efficacy of mammographic screening and increased risk of developing breast cancer. Accurate and relia...

Aging-related volume changes in the brain and cerebrospinal fluid using artificial intelligence-automated segmentation.

OBJECTIVES: To verify the reliability of the volumes automatically segmented using a new artificial intelligence (AI)-based application and evaluate c...

Experimental Study: Deep Learning-Based Fall Monitoring among Older Adults with Skin-Wearable Electronics.

Older adults are more vulnerable to falling due to normal changes due to aging, and their falls are a serious medical risk with high healthcare and so...