AIMC Topic: Middle Aged

Clear Filters Showing 10081 to 10090 of 17155 articles

Using chatbots to screen for heritable cancer syndromes in patients undergoing routine colonoscopy.

Journal of medical genetics
BACKGROUND: Hereditary colorectal cancer (HCRC) syndromes account for 10% of colorectal cancers but remain underdiagnosed. This feasibility project tested the utility of an artificial intelligence-based chatbot deployed to patients scheduled for colo...

Effects of Robotic Exoskeleton-Aided Gait Training in the Strength, Body Balance, and Walking Speed in Individuals With Multiple Sclerosis: A Single-Group Preliminary Study.

Archives of physical medicine and rehabilitation
OBJECTIVE: To assess effects of 15 exoskeleton-assisted gait training sessions, reflected by the muscle strength of the lower limbs and by walking speed immediately after the training sessions and at the 6-week follow-up.

A fast and fully-automated deep-learning approach for accurate hemorrhage segmentation and volume quantification in non-contrast whole-head CT.

Scientific reports
This project aimed to develop and evaluate a fast and fully-automated deep-learning method applying convolutional neural networks with deep supervision (CNN-DS) for accurate hematoma segmentation and volume quantification in computed tomography (CT) ...

Prediction of age and brachial-ankle pulse-wave velocity using ultra-wide-field pseudo-color images by deep learning.

Scientific reports
This study examined whether age and brachial-ankle pulse-wave velocity (baPWV) can be predicted with ultra-wide-field pseudo-color (UWPC) images using deep learning (DL). We examined 170 UWPC images of both eyes of 85 participants (40 men and 45 wome...

Cross-site transportability of an explainable artificial intelligence model for acute kidney injury prediction.

Nature communications
Artificial intelligence (AI) has demonstrated promise in predicting acute kidney injury (AKI), however, clinical adoption of these models requires interpretability and transportability. Non-interoperable data across hospitals is a major barrier to mo...

An Easy-to-Use Machine Learning Model to Predict the Prognosis of Patients With COVID-19: Retrospective Cohort Study.

Journal of medical Internet research
BACKGROUND: Prioritizing patients in need of intensive care is necessary to reduce the mortality rate during the COVID-19 pandemic. Although several scoring methods have been introduced, many require laboratory or radiographic findings that are not a...

Circulating Neutrophil Extracellular Traps Signature for Identifying Organ Involvement and Response to Glucocorticoid in Adult-Onset Still's Disease: A Machine Learning Study.

Frontiers in immunology
Adult-onset Still's disease (AOSD) is an autoinflammatory disease with multisystem involvement. Early identification of patients with severe complications and those refractory to glucocorticoid is crucial to improve therapeutic strategy in AOSD. Exag...

Major colorectal resection is feasible using a new robotic surgical platform: the first report of a case series.

Techniques in coloproctology
BACKGROUND: The number of abdominal procedures performed via a robotic-assisted approach is increasing as potential advantages of the modality are recognised. We report the first in human case series of major colorectal resection performed using a ne...

Multimodal magnetic resonance imaging correlates of motor outcome after stroke using machine learning.

Neuroscience letters
This study applied machine learning regression to predict motor function after stroke based on multimodal magnetic resonance imaging. Fifty-four stroke patients, who underwent T1 weighted, diffusion tensor, and resting state functional magnetic reson...

Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation.

Journal of medical Internet research
BACKGROUND: COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, ...