Geriatrics

Attenuative effects of collagen peptide from milkfish () scales on ovariectomy-induced osteoporosis.

Osteoporosis is characterized by low bone mass, bone microarchitecture disruption, and collagen loss, leading to increased fracture risk. In the current study, collagen peptides were extracted from milkfish scales (MS) to develop potential therapeutic candidates for osteoporosis. MS was used to synthesize a crude extract of fish scales (FS), collagen liquid (COL), and hydroxyapatite powder (HA). C...

Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data.

BACKGROUND: Deep learning has shown potential in various scientific domains but faces challenges when applied to complex, high-dimensional multi-omics data. Alzheimer's Disease (AD) is a neurodegenerative disorder that lacks targeted therapeutic options. This study introduces the Circular-Sliding Window Association Test (c-SWAT) to improve the classification accuracy in predicting AD using serum-b...

E2EDA: Protein Domain Assembly Based on End-to-End Deep Learning.

With the development of deep learning, almost all single-domain proteins can be predicted at experimental resolution. However, the structure predictio...

Comparative Effectiveness Analysis of Lumpectomy and Mastectomy for Elderly Female Breast Cancer Patients: A Deep Learning-based Big Data Analysis.

: To evaluate the comparative effectiveness of treatments, a randomized clinical trial remains the gold standard but can be challenged by a high cost,...

Distinct subtypes of spatial brain metabolism patterns in Alzheimer's disease identified by deep learning-based FDG PET clusters.

PURPOSE: Alzheimer's disease (AD) is a heterogeneous disease that presents a broad spectrum of clinicopathologic profiles. To date, objective subtypin...

Establish and validate the reliability of predictive models in bone mineral density by deep learning as examination tool for women.

UNLABELLED: While FRAX with BMD could be more precise in estimating the fracture risk, DL-based models were validated to slightly reduce the number of...

[Artificial intelligence in geriatrics: ChatGPT and AI impact].

Combining Deep Learning and Radiomics for Automated, Objective, Comprehensive Bone Mineral Density Assessment From Low-Dose Chest Computed Tomography.

RATIONALE AND OBJECTIVES: To develop an intelligent diagnostic model for osteoporosis screening based on low-dose chest computed tomography (LDCT). Th...

Comparison between linear regression and four different machine learning methods in selecting risk factors for osteoporosis in a Chinese female aged cohort.

BACKGROUND: Population aging is emerging as an increasingly acute challenge for countries around the world. One particular manifestation of this pheno...

Selecting cardiac magnetic resonance images suitable for annotation of pulmonary arteries using an active-learning based deep learning model.

An increasing and aging patient population poses a growing burden on healthcare professionals. Automation of medical imaging diagnostics holds promise...

Overview of methods and available tools used in complex brain disorders.

Complex brain disorders, including Alzheimer's dementia, sleep disorders, and epilepsy, are chronic conditions that have high prevalence individually ...

FDNet: An end-to-end fusion decomposition network for infrared and visible images.

Infrared and visible image fusion can generate a fusion image with clear texture and prominent goals under extreme conditions. This capability is impo...

Predicting brain age gap with radiomics and automl: A Promising approach for age-Related brain degeneration biomarkers.

The Brain Age Gap (BAG), which refers to the difference between chronological age and predicted neuroimaging age, is proposed as a potential biomarker...

An interpretable deep learning model for time-series electronic health records: Case study of delirium prediction in critical care.

Deep Learning (DL) models have received increasing attention in the clinical setting, particularly in intensive care units (ICU). In this context, the...

Improving Alzheimer Diagnoses With An Interpretable Deep Learning Framework: Including Neuropsychiatric Symptoms.

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder characterized by the progressive cognitive decline. Among the various clinical symp...

Using a dual-stream attention neural network to characterize mild cognitive impairment based on retinal images.

Mild cognitive impairment (MCI) is a critical transitional stage between normal cognition and dementia, for which early detection is crucial for timel...

A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data.

With the advances in technology and data science, machine learning (ML) is being rapidly adopted by the health care sector. However, there is a lack o...

A study on whether deep learning models based on CT images for bone density classification and prediction can be used for opportunistic osteoporosis screening.

UNLABELLED: This study utilized deep learning to classify osteoporosis and predict bone density using opportunistic CT scans and independently tested ...

Surgical scheduling via optimization and machine learning with long-tailed data : Health care management science, in press.

Using data from cardiovascular surgery patients with long and highly variable post-surgical lengths of stay (LOS), we develop a modeling framework to ...

The Use of Artificial Intelligence in the Management of Neurodegenerative Disorders; Focus on Alzheimer's Disease.

Recent advances in artificial intelligence (AI) have shown great promise in the diagnosis, prediction, treatment plans, and monitoring of neurodegener...