Geriatrics

Established machine learning models to predict readmission for elderly patients with ischemic heart disease.

BACKGROUND: The contribution of clinical features associated with 30-day or 1-year readmission in elderly patients with ischemic heart disease (IHD) and whether these features can be used to predict the readmission risk of patients has not been studied.

AI-based differential diagnosis of dementia etiologies on multimodal data.

Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an artificial intelligence (AI) model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functiona...

Wall-climbing performance of gecko-inspired robot with soft feet and digits enhanced by gravity compensation.

Gravitational forces can induce deviations in body posture from desired configurations in multi-legged arboreal robot locomotion with low leg stiffnes...

Expanding Approaches to Improve Orthopaedic Care Through the Application of Artificial Intelligence: Commentary on an article by Neil P. Sheth, MD, et al.: "Effects of Abaloparatide on Bone Mineral Density in Proximal Femoral Regions Corresponding to Arthroplasty Gruen Zones. A Study of Postmenopausal Women with Osteoporosis".

Structure focused neurodegeneration convolutional neural network for modelling and classification of Alzheimer's disease.

Alzheimer's disease (AD), the predominant form of dementia, is a growing global challenge, emphasizing the urgent need for accurate and early diagnosi...

A systematic literature review on the significance of deep learning and machine learning in predicting Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) is the most prevalent cause of dementia, characterized by a steady decline in mental, behavioral, and social abil...

Optimizing drug therapy for older adults: shifting away from problematic polypharmacy.

INTRODUCTION: The accelerated discovery and production of pharmaceutical products has resulted in many positive outcomes. However, this progress has a...

Fully Automatic Deep Learning Model for Spine Refracture in Patients with OVCF: A Multi-Center Study.

BACKGROUND: The reaserch of artificial intelligence (AI) model for predicting spinal refracture is limited to bone mineral density, X-ray and some con...

Rapid diagnosis of celiac disease based on plasma Raman spectroscopy combined with deep learning.

Celiac Disease (CD) is a primary malabsorption syndrome resulting from the interplay of genetic, immune, and dietary factors. CD negatively impacts da...

Computer Vision for Gait Assessment in Cerebral Palsy: Metric Learning and Confidence Estimation.

Assessing the motor impairments of individuals with neurological disorders holds significant importance in clinical practice. Currently, these clinica...

Identifying factors associated with locomotive syndrome using machine learning methods: The third survey of the research on osteoarthritis/osteoporosis against disability study.

AIM: To identify factors associated with locomotive syndrome (LS) using medical questionnaire data and machine learning.

Machine learning for the prediction of delirium in elderly intensive care unit patients.

PURPOSE: This study aims to develop and validate a prediction model for delirium in elderly ICU patients and help clinicians identify high-risk patien...

Detection and Localization of Spine Disorders from Plain Radiography.

Spine disorders can cause severe functional limitations, including back pain, decreased pulmonary function, and increased mortality risk. Plain radiog...

Cracking AlphaFold2: Leveraging the power of artificial intelligence in undergraduate biochemistry curriculums.

AlphaFold2 is an Artificial Intelligence-based program developed to predict the 3D structure of proteins given only their amino acid sequence at atomi...

STaRNet: A spatio-temporal and Riemannian network for high-performance motor imagery decoding.

Brain-computer interfaces (BCIs), representing a transformative form of human-computer interaction, empower users to interact directly with external e...

Predicting changes in brain metabolism and progression from mild cognitive impairment to dementia using multitask Deep Learning models and explainable AI.

BACKGROUND: The prediction of Alzheimer's disease (AD) progression from its early stages is a research priority. In this context, the use of Artificia...

The Role of AI in Drug Discovery.

The emergence of Artificial Intelligence (AI) in drug discovery marks a pivotal shift in pharmaceutical research, blending sophisticated computational...

Prediction of Alzheimer's disease progression within 6 years using speech: A novel approach leveraging language models.

INTRODUCTION: Identification of individuals with mild cognitive impairment (MCI) who are at risk of developing Alzheimer's disease (AD) is crucial for...

The potential role for artificial intelligence in fracture risk prediction.

Osteoporotic fractures are a major health challenge in older adults. Despite the availability of safe and effective therapies for osteoporosis, these ...

An interpretable machine learning-based cerebrospinal fluid proteomics clock for predicting age reveals novel insights into brain aging.

Machine learning can be used to create "biologic clocks" that predict age. However, organs, tissues, and biofluids may age at different rates from the...