AIMC Topic: Middle Aged

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XGBoost based machine learning approach to predict the risk of fall in older adults using gait outcomes.

Scientific reports
This study aimed to identify the optimal features of gait parameters to predict the fall risk level in older adults. The study included 746 older adults (age: 63-89 years). Gait tests (20 m walkway) included speed modification (slower, preferred, and...

Deep learning-based identification of acute ischemic core and deficit from non-contrast CT and CTA.

Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism
The accurate identification of irreversible infarction and salvageable tissue is important in planning the treatments for acute ischemic stroke (AIS) patients. Computed tomographic perfusion (CTP) can be used to evaluate the ischemic core and deficit...

A machine learning approach to predicting risk of myelodysplastic syndrome.

Leukemia research
BACKGROUND: Early myelodysplastic syndrome (MDS) diagnosis can allow physicians to provide early treatment, which may delay advancement of MDS and improve quality of life. However, MDS often goes unrecognized and is difficult to distinguish from othe...

Longitudinal cohorts for harnessing the electronic health record for disease prediction in a US population.

BMJ open
PURPOSE: The depth and breadth of clinical data within electronic health record (EHR) systems paired with innovative machine learning methods can be leveraged to identify novel risk factors for complex diseases. However, analysing the EHR is challeng...

UNRAQ-A Questionnaire for the Use of a Social Robot in Care for Older Persons. A Multi-Stakeholder Study and Psychometric Properties.

International journal of environmental research and public health
(1) Background: while there exist validated measures to assess the needs of older people, there are comparatively few validated tools to assess needs and requirements for the use of robots. Henceforth, the aim of the study is to present and validate ...

Cardiothoracic ratio measurement using artificial intelligence: observer and method validation studies.

BMC medical imaging
BACKGROUND: Artificial Intelligence (AI) is a promising tool for cardiothoracic ratio (CTR) measurement that has been technically validated but not clinically evaluated on a large dataset. We observed and validated AI and manual methods for CTR measu...

COVID-19 pneumonia on chest X-rays: Performance of a deep learning-based computer-aided detection system.

PloS one
Chest X-rays (CXRs) can help triage for Coronavirus disease (COVID-19) patients in resource-constrained environments, and a computer-aided detection system (CAD) that can identify pneumonia on CXR may help the triage of patients in those environment ...

Radiology "forensics": determination of age and sex from chest radiographs using deep learning.

Emergency radiology
PURPOSE: To develop and test the performance of deep convolutional neural networks (DCNNs) for automated classification of age and sex on chest radiographs (CXR).

Machine Learning-Guided Prediction of Central Anterior Chamber Depth Using Slit Lamp Images from a Portable Smartphone Device.

Biosensors
There is currently no objective portable screening modality for narrow angles in the community. In this prospective, single-centre image validation study, we used machine learning on slit lamp images taken with a portable smartphone device (MIDAS) to...