AIMC Topic: Aged, 80 and over

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Brain Age Prediction: A Comparison between Machine Learning Models Using Brain Morphometric Data.

Sensors (Basel, Switzerland)
Brain structural morphology varies over the aging trajectory, and the prediction of a person's age using brain morphological features can help the detection of an abnormal aging process. Neuroimaging-based brain age is widely used to quantify an indi...

Robot-assisted laparoscopic surgery with a fluorescent near-infrared ray ureteral catheter for a rectal cancer patient with pelvic kidney: A case report.

Asian journal of endoscopic surgery
An 85-year-old woman presented with a stomachache after a meal and was admitted to the previous clinic. Multi-detector computed tomography (CT) of the abdomen showed wall thickening in the rectum and right ectopic pelvic kidney. Colonoscopy revealed ...

DENSEN: a convolutional neural network for estimating chronological ages from panoramic radiographs.

BMC bioinformatics
BACKGROUND: Age estimation from panoramic radiographs is a fundamental task in forensic sciences. Previous age assessment studies mainly focused on juvenile rather than elderly populations (> 25 years old). Most proposed studies were statistical or s...

Real-world Implementation of an eHealth System Based on Artificial Intelligence Designed to Predict and Reduce Emergency Department Visits by Older Adults: Pragmatic Trial.

Journal of medical Internet research
BACKGROUND: Frail older people use emergency services extensively, and digital systems that monitor health remotely could be useful in reducing these visits by earlier detection of worsening health conditions.

The Impact of Engagement with the PARO Therapeutic Robot on the Psychological Benefits of Older Adults with Dementia.

Clinical gerontologist
OBJECTIVES: This study aimed to examine the effect of 8-weeks of a 60-minute PARO intervention to reduce depressive symptoms and loneliness in older adults with dementia and investigated changes in their emotional or behavioral expressions and level ...

Deep Relation Learning for Regression and Its Application to Brain Age Estimation.

IEEE transactions on medical imaging
Most deep learning models for temporal regression directly output the estimation based on single input images, ignoring the relationships between different images. In this paper, we propose deep relation learning for regression, aiming to learn diffe...

Deep learning of ECG waveforms for diagnosis of heart failure with a reduced left ventricular ejection fraction.

Scientific reports
The performance and clinical implications of the deep learning aided algorithm using electrocardiogram of heart failure (HF) with reduced ejection fraction (DeepECG-HFrEF) were evaluated in patients with acute HF. The DeepECG-HFrEF algorithm was trai...

Successful Use of a 5G-Based Robot-Assisted Remote Ultrasound System in a Care Center for Disabled Patients in Rural China.

Frontiers in public health
BACKGROUND: Disability has become a global population health challenge. Due to difficulties in self-care or independent living, patients with disability mainly live in community-based care centers or institutions for long-term care. Nonetheless, thes...

External validation of the deep learning system "SpineNet" for grading radiological features of degeneration on MRIs of the lumbar spine.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
BACKGROUND: Magnetic resonance imaging (MRI) is used to detect degenerative changes of the lumbar spine. SpineNet (SN), a computer vision-based system, performs an automated analysis of degenerative features in MRI scans aiming to provide high accura...

Deep learning accurately classifies elbow joint effusion in adult and pediatric radiographs.

Scientific reports
Joint effusion due to elbow fractures are common among adults and children. Radiography is the most commonly used imaging procedure to diagnose elbow injuries. The purpose of the study was to investigate the diagnostic accuracy of deep convolutional ...