AIMC Topic: Aged, 80 and over

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Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone.

BMC medical informatics and decision making
BACKGROUND: Cardiovascular diseases kill approximately 17 million people globally every year, and they mainly exhibit as myocardial infarctions and heart failures. Heart failure (HF) occurs when the heart cannot pump enough blood to meet the needs of...

Real-time colorectal cancer diagnosis using PR-OCT with deep learning.

Theranostics
Prior reports have shown optical coherence tomography (OCT) can differentiate normal colonic mucosa from neoplasia, potentially offering an alternative technique to endoscopic biopsy - the current gold-standard colorectal cancer screening and surveil...

Deep Neural Networks for Chronological Age Estimation From OPG Images.

IEEE transactions on medical imaging
Chronological age estimation is crucial labour in many clinical procedures, where the teeth have proven to be one of the best estimators. Although some methods to estimate the age from tooth measurements in orthopantomogram (OPG) images have been dev...

Evaluating the sit-to-stand transfer assistance from a smart walker in older adults with motor impairments.

Geriatrics & gerontology international
AIM: To evaluate the effectiveness and user satisfaction with the sit-to-stand (STS) assistance system of a smart walker (SW), and to identify factors associated with them in potential users.

Machine learning to predict early recurrence after oesophageal cancer surgery.

The British journal of surgery
BACKGROUND: Early cancer recurrence after oesophagectomy is a common problem, with an incidence of 20-30 per cent despite the widespread use of neoadjuvant treatment. Quantification of this risk is difficult and existing models perform poorly. This s...

Development and Evaluation of a Deep Learning System for Screening Retinal Hemorrhage Based on Ultra-Widefield Fundus Images.

Translational vision science & technology
PURPOSE: To develop and evaluate a deep learning (DL) system for retinal hemorrhage (RH) screening using ultra-widefield fundus (UWF) images.

Machine Learning for Predicting Complications in Head and Neck Microvascular Free Tissue Transfer.

The Laryngoscope
OBJECTIVES/HYPOTHESIS: Machine learning (ML) is a type of artificial intelligence wherein a computer learns patterns and associations between variables to correctly predict outcomes. The objectives of this study were to 1) use a ML platform to identi...

Assessment of utilization efficiency using machine learning techniques: A study of heterogeneity in preoperative healthcare utilization among super-utilizers.

American journal of surgery
INTRODUCTION: In the United States, 5% of patients represent up to 55% of all health care costs. This study sought to define healthcare utilization patterns among super-utilizers, as well as assess possible variation in patient outcomes.

A Comparative Classification Analysis of Abdominal Aortic Aneurysms by Machine Learning Algorithms.

Annals of biomedical engineering
The objective of this work was to perform image-based classification of abdominal aortic aneurysms (AAA) based on their demographic, geometric, and biomechanical attributes. We retrospectively reviewed existing demographics and abdominal computed tom...