AIMC Topic: Adolescent

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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...

Influence of Voice Interactive Educational Robot Combined with Artificial Intelligence for the Development of Adolescents.

Computational intelligence and neuroscience
In the context of multicultural information, to explore and analyze the use effect of voice interactive educational robot in the classroom of adolescent students, and the physical and mental impact of movie characters on adolescent students, and to l...

Validity of an artificial intelligence, human pose estimation model for measuring single-leg squat kinematics.

Journal of biomechanics
Few studies have investigated the validity of 2D pose estimation models to evaluate kinematics throughout a motion and none have included adolescents. Adolescent athletes completed single-leg squats while 3D kinematic data and 2D sagittal and frontal...

Robot-assisted all-epiphyseal anterior cruciate ligament reconstruction in skeletally immature patients: a retrospective study.

International orthopaedics
PURPOSE: To review a series of adolescent patients with anterior cruciate ligament (ACL) injuries surgically treated with robot-assisted all-epiphyseal ACL reconstruction (ACLR), and to compare with the traditional freehand group.

Assessment of an artificial intelligence aid for the detection of appendicular skeletal fractures in children and young adults by senior and junior radiologists.

Pediatric radiology
BACKGROUND: As the number of conventional radiographic examinations in pediatric emergency departments increases, so, too, does the number of reading errors by radiologists.

Comparison of three machine learning models to predict suicidal ideation and depression among Chinese adolescents: A cross-sectional study.

Journal of affective disorders
BACKGROUND: Machine learning (ML) algorithms based on various clinicodemographic, psychometric, and biographic factors have been used to predict depression, suicidal ideation, and suicide attempt in adolescents, but there is still a need for more acc...

Performance comparison of three deep learning models for impacted mesiodens detection on periapical radiographs.

Scientific reports
This study aimed to develop deep learning models that automatically detect impacted mesiodens on periapical radiographs of primary and mixed dentition using the YOLOv3, RetinaNet, and EfficientDet-D3 algorithms and to compare their performance. Peria...

Artificial intelligence system for training diagnosis and differentiation with molar incisor hypomineralization (MIH) and similar pathologies.

Clinical oral investigations
OBJECTIVES: Molar incisor hypomineralization (MIH) is a difficult-to-diagnose developmental disorder of the teeth, mainly in children and adolescents. Due to the young age of the patients, problems typically occur with the diagnosis of MIH. The aim o...

A Deep Learning Approach to Classify Sitting and Sleep History from Raw Accelerometry Data during Simulated Driving.

Sensors (Basel, Switzerland)
Prolonged sitting and inadequate sleep can impact driving performance. Therefore, objective knowledge of a driver's recent sitting and sleep history could help reduce safety risks. This study aimed to apply deep learning to raw accelerometry data col...