AIMC Topic: Aged

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Facilitators and barriers to using telepresence robots in aged care settings: a scoping review protocol.

BMJ open
INTRODUCTION: Social isolation is a significant issue in aged care settings (eg, long-term care (LTC) and hospital) and is associated with adverse outcomes such as reduced well-being and loneliness. Loneliness is linked with depression, anxiety, cogn...

Machine Learning Models for Survival and Neurological Outcome Prediction of Out-of-Hospital Cardiac Arrest Patients.

BioMed research international
BACKGROUND: Out-of-hospital cardiac arrest (OHCA) is a major health problem worldwide, and neurologic injury remains the leading cause of morbidity and mortality among survivors of OHCA. The purpose of this study was to investigate whether a machine ...

Multi-step validation of a deep learning-based system for the quantification of bowel preparation: a prospective, observational study.

The Lancet. Digital health
BACKGROUND: Inadequate bowel preparation is associated with a decrease in adenoma detection rate (ADR). A deep learning-based bowel preparation assessment system based on the Boston bowel preparation scale (BBPS) has been previously established to ca...

Automated Collateral Flow Assessment in Patients with Acute Ischemic Stroke Using Computed Tomography with Artificial Intelligence Algorithms.

World neurosurgery
BACKGROUND: Collateral circulation is associated with improved functional outcome in patients with large vessel occlusion acute ischemic stroke (AIS) who undergo reperfusion therapy. Assessment of collateral flow can be time consuming, subjective, an...

Automated bone mineral density prediction and fracture risk assessment using plain radiographs via deep learning.

Nature communications
Dual-energy X-ray absorptiometry (DXA) is underutilized to measure bone mineral density (BMD) and evaluate fracture risk. We present an automated tool to identify fractures, predict BMD, and evaluate fracture risk using plain radiographs. The tool pe...

Decision making on vestibular schwannoma treatment: predictions based on machine-learning analysis.

Scientific reports
Decision making on the treatment of vestibular schwannoma (VS) is mainly based on the symptoms, tumor size, patient's preference, and experience of the medical team. Here we provide objective tools to support the decision process by answering two que...

Weakly supervised learning on unannotated H&E-stained slides predicts BRAF mutation in thyroid cancer with high accuracy.

The Journal of pathology
Deep neural networks (DNNs) that predict mutational status from H&E slides of cancers can enable inexpensive and timely precision oncology. Although expert knowledge is reliable for annotating regions informative of malignancy and other known histolo...

Predicting clinical outcomes in COVID-19 using radiomics on chest radiographs.

The British journal of radiology
OBJECTIVES: For optimal utilization of healthcare resources, there is a critical need for early identification of COVID-19 patients at risk of poor prognosis as defined by the need for intensive unit care and mechanical ventilation. We tested the fea...

A Soft Robotic Intervention for Gait Enhancement in Older Adults.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Falls continue to be a major safety and health concern for older adults. Researchers reported that increased gait variability was associated with increased fall risks. In the present study, we proposed a novel wearable soft robotic intervention and e...

Automatic Assessment of Mitral Regurgitation Severity Using the Mask R-CNN Algorithm with Color Doppler Echocardiography Images.

Computational and mathematical methods in medicine
Accurate assessment of mitral regurgitation (MR) severity is critical in clinical diagnosis and treatment. No single echocardiographic method has been recommended for MR quantification thus far. We sought to define the feasibility and accuracy of the...