Geriatrics

Understanding the Impact of Aging on Attractiveness Using a Machine Learning Model of Facial Age Progression.

Advances in machine learning age progression technology offer the unique opportunity to better understand the public's perception on the aging face. To compare how observers perceive attractiveness and traditional gender traits in faces created with a machine learning model. Eight surveys were developed, each with 10 sets of photographs that were progressively aged with a machine learning model...

Stereology neuron counts correlate with deep learning estimates in the human hippocampal subregions.

Hippocampal subregions differ in specialization and vulnerability to cell death. Neuron death and hippocampal atrophy have been a marker for the progression of Alzheimer's disease. Relatively few studies have examined neuronal loss in the human brain using stereology. We characterize an automated high-throughput deep learning pipeline to segment hippocampal pyramidal neurons, generate pyramidal ne...

A unique color-coded visualization system with multimodal information fusion and deep learning in a longitudinal study of Alzheimer's disease.

PURPOSE: Automated diagnosis and prognosis of Alzheimer's Disease remain a challenging problem that machine learning (ML) techniques have attempted to...

Machine and deep learning-based clinical characteristics and laboratory markers for the prediction of sarcopenia.

BACKGROUND: Sarcopenia is an age-related progressive skeletal muscle disorder involving the loss of muscle mass or strength and physiological function...

Artificial intelligence assisted identification of potential tau aggregation inhibitors: ligand- and structure-based virtual screening, in silico ADME, and molecular dynamics study.

Alzheimer's disease (AD) is a severe, growing, multifactorial disorder affecting millions of people worldwide characterized by cognitive decline and n...

Comparison of End-to-End Neural Network Architectures and Data Augmentation Methods for Automatic Infant Motility Assessment Using Wearable Sensors.

Infant motility assessment using intelligent wearables is a promising new approach for assessment of infant neurophysiological development, and where ...

Investigating the robustness of a deep learning-based method for quantitative phase retrieval from propagation-based x-ray phase contrast measurements under laboratory conditions.

Quantitative phase retrieval (QPR) in propagation-based x-ray phase contrast imaging of heterogeneous and structurally complicated objects is challeng...

Age-specific biomechanical challenges and engagement in dynamic balance training with robotic or virtual real-time visual feedback.

Challenging balance training that targets age-related neuromuscular and motor coordination deficits is needed for effective fall prevention therapy. G...

An explainable artificial intelligence approach to spatial navigation based on hippocampal circuitry.

Learning to navigate a complex environment is not a difficult task for a mammal. For example, finding the correct way to exit a maze following a seque...

Deep learning-based EEG analysis to classify normal, mild cognitive impairment, and dementia: Algorithms and dataset.

For automatic EEG diagnosis, this paper presents a new EEG data set with well-organized clinical annotations called Chung-Ang University Hospital EEG ...

Artificial intelligence velocimetry reveals in vivo flow rates, pressure gradients, and shear stresses in murine perivascular flows.

Quantifying the flow of cerebrospinal fluid (CSF) is crucial for understanding brain waste clearance and nutrient delivery, as well as edema in pathol...

Artificial Intelligence Technologies in the Microsurgical Operating Room (Review).

Surgery performed by a novice neurosurgeon under constant supervision of a senior surgeon with the experience of thousands of operations, able to hand...

X-CHAR: A Concept-based Explainable Complex Human Activity Recognition Model.

End-to-end deep learning models are increasingly applied to safety-critical human activity recognition (HAR) applications, e.g., healthcare monitoring...

Visual deep learning of unprocessed neuroimaging characterises dementia subtypes and generalises across non-stereotypic samples.

BACKGROUND: Dementia's diagnostic protocols are mostly based on standardised neuroimaging data collected in the Global North from homogeneous samples....

Natural language processing techniques for studying language in pathological ageing: A scoping review.

BACKGROUND: In the past few years there has been a growing interest in the employment of verbal productions as digital biomarkers, namely objective, q...

Automatic implant shape design for minimally invasive repair of pectus excavatum using deep learning and shape registration.

Minimally invasive repair of pectus excavatum (MIRPE) is an effective method for correcting pectus excavatum (PE), a congenital chest wall deformity c...

Anti-inflammatory therapy enables robot-actuated regeneration of aged muscle.

Robot-actuated mechanical loading (ML)-based therapies ("mechanotherapies") can promote regeneration after severe skeletal muscle injury, but the effe...

Automated, calibration-free quantification of cortical bone porosity and geometry in postmenopausal osteoporosis from ultrashort echo time MRI and deep learning.

BACKGROUND: Assessment of cortical bone porosity and geometry by imaging in vivo can provide useful information about bone quality that is independent...

AMLnet, A deep-learning pipeline for the differential diagnosis of acute myeloid leukemia from bone marrow smears.

Acute myeloid leukemia (AML) is a deadly hematological malignancy. Cellular morphology detection of bone marrow smears based on the French-American-Br...

Influence of artificial intelligence in education on adolescents' social adaptability: The mediatory role of social support.

Artificial intelligence (AI) is widely used in the field of education at present, but people know little about its possible impacts, especially on the...