AIMC Topic: Adult

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Thyroid Nodules on Ultrasound in Children and Young Adults: Comparison of Diagnostic Performance of Radiologists' Impressions, ACR TI-RADS, and a Deep Learning Algorithm.

AJR. American journal of roentgenology
In current clinical practice, thyroid nodules in children are generally evaluated on the basis of radiologists' overall impressions of ultrasound images. The purpose of this article is to compare the diagnostic performance of radiologists' overall ...

The evolving use of robotic surgery: a population-based analysis.

Surgical endoscopy
INTRODUCTION: Robotic surgery has integrated into the healthcare system despite limited evidence demonstrating its clinical benefit. Our objectives were (i) to describe secular trends and (ii) patient- and system-level determinants of the receipt of ...

A wearable soft robot that can alleviate the pain and fear of the wearer.

Scientific reports
Social soft robotics may provide a new solution for alleviating human pain and fear. Here, we introduce a hand-held soft robot that can be clenched by the wearer. The robot comprises small airbags that can be inflated to provide the wearer with a fee...

Segmentation of Vestibular Schwannomas on Postoperative Gadolinium-Enhanced T1-Weighted and Noncontrast T2-Weighted Magnetic Resonance Imaging Using Deep Learning.

Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology
OBJECTIVE: Surveillance of postoperative vestibular schwannomas currently relies on manual segmentation and measurement of the tumor by content experts, which is both labor intensive and time consuming. We aimed to develop and validate deep learning ...

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...

Predicting risk of obesity and meal planning to reduce the obese in adulthood using artificial intelligence.

Endocrine
BACKGROUND: An unhealthy diet or excessive amount of food intake creates obesity issues in human beings that further may cause several diseases such as Polycystic Ovary Syndrome (PCOS), Cardiovascular disease, Diabetes, Cancers, etc. Obesity is a maj...

Exploration of Variables Predicting Sense of School Belonging Using the Machine Learning Method-Group Mnet.

Psychological reports
The purpose of this study was to explore variables related to school belonging from a holistic perspective, including a large number of variables in one model, different to the traditional analytical method. Using 2015 data from the Program for Inter...

Machine Learning-Enabled Fully Automated Assessment of Left Ventricular Volume, Ejection Fraction and Strain: Experience in Pediatric and Young Adult Echocardiography.

Pediatric cardiology
BACKGROUND: Left ventricular (LV) volumes, ejection fraction (EF), and myocardial strain have been shown to be predictive of clinical and subclinical heart disease. Automation of LV functional assessment overcomes difficult technical challenges and c...

Energy Expenditure Estimation in Children, Adolescents and Adults by Using a Respiratory Magnetometer Plethysmography System and a Deep Learning Model.

Nutrients
PURPOSE: Energy expenditure is a key parameter in quantifying physical activity. Traditional methods are limited because they are expensive and cumbersome. Additional portable and cheaper devices are developed to estimate energy expenditure to overco...

The Association of Waist Circumference with the Prevalence and Survival of Digestive Tract Cancer in US Adults: A Population Study Based on Machine Learning Methods.

Computational and mathematical methods in medicine
AIMS: This paper aims to investigate the relationship of waist circumference (WC) with digestive tract cancer morbidity and mortality.