AIMC Topic: Adolescent

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Using Explainable Artificial Intelligence to Discover Interactions in an Ecological Model for Obesity.

International journal of environmental research and public health
Ecological theories suggest that environmental, social, and individual factors interact to cause obesity. Yet, many analytic techniques, such as multilevel modeling, require manual specification of interacting factors, making them inept in their abil...

Prediction of fluid intelligence from T1-w MRI images: A precise two-step deep learning framework.

PloS one
The Adolescent Brain Cognitive Development (ABCD) Neurocognitive Prediction Challenge (ABCD-NP-Challenge) is a community-driven competition that challenges competitors to develop algorithms to predict fluid intelligence scores from T1-w MRI images. I...

Surgical and functional outcomes of robot-assisted laparoscopic partial nephrectomy for renal cell carcinoma in adolescents and young adults: a propensity score matching study.

International journal of clinical oncology
BACKGROUND: Cancer development in adolescents and young adults (AYAs) has elicited recent interest. We investigated the surgical and functional outcomes of robot-assisted laparoscopic partial nephrectomy (RAPN) for renal cell carcinoma (RCC) in AYAs.

IoMT-Based Mitochondrial and Multifactorial Genetic Inheritance Disorder Prediction Using Machine Learning.

Computational intelligence and neuroscience
A genetic disorder is a serious disease that affects a large number of individuals around the world. There are various types of genetic illnesses, however, we focus on mitochondrial and multifactorial genetic disorders for prediction. Genetic illness...

Four-Class Classification of Neuropsychiatric Disorders by Use of Functional Near-Infrared Spectroscopy Derived Biomarkers.

Sensors (Basel, Switzerland)
Diagnosis of most neuropsychiatric disorders relies on subjective measures, which makes the reliability of final clinical decisions questionable. The aim of this study was to propose a machine learning-based classification approach for objective diag...

Systematic evaluation of machine learning algorithms for neuroanatomically-based age prediction in youth.

Human brain mapping
Application of machine learning (ML) algorithms to structural magnetic resonance imaging (sMRI) data has yielded behaviorally meaningful estimates of the biological age of the brain (brain-age). The choice of the ML approach in estimating brain-age i...

Pedicle Screw Placement in Adolescent Idiopathic Scoliosis: A Comparison between Robotics Coupled with Navigation versus the Freehand Technique.

Sensors (Basel, Switzerland)
(1) Background: Robotics coupled with navigation (RAN) is a modern surgical platform shown to increase screw placement accuracy during pediatric scoliosis surgery. Our institution uses a technique which combines the RAN platform for apical pedicle sc...

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 ...

Age group prediction with panoramic radiomorphometric parameters using machine learning algorithms.

Scientific reports
The aim of this study is to investigate the relationship of 18 radiomorphometric parameters of panoramic radiographs based on age, and to estimate the age group of people with permanent dentition in a non-invasive, comprehensive, and accurate manner ...

Electronic Health Record-Based Deep Learning Prediction of Death or Severe Decompensation in Heart Failure Patients.

JACC. Heart failure
BACKGROUND: Surgical mechanical ventricular assistance and cardiac replacement therapies, although life-saving in many heart failure (HF) patients, remain high-risk. Despite this, the difficulty in timely identification of medical therapy nonresponde...