Pediatrics

Machine learning for the identification of decision boundaries during the transition from radial to vertical growth phase superficial spreading melanomas.

The objective of this study was to compute threshold values for the diameter of superficial spreading melanomas (SSMs) at which the radial growth phase (RGP) evolves into an invasive vertical growth phase (VGP). We examined reports from 1995 to 2019 of 834 primary SSMs. All the patients underwent complete surgical removal of the tumor and the diagnosis was confirmed after histologic examination. M...

The Role of Robotic Rehabilitation in Children with Neurodevelopmental Disorders.

In the last years, traditional treatments have been combined with innovative therapies, such as robot-assisted training, an interesting new rehabilitation tool for children with neurologic impairment. The robots deliver a high dose of training and intensity, critical factors for the activation of neuronal plasticity. Despite their increasing use, the effectiveness of robotic devices in the rehabil...

Applications of Artificial Intelligence in Pediatric Oncology: A Systematic Review.

PURPOSE: There is a need for an improved understanding of clinical and biologic risk factors in pediatric cancer to improve patient outcomes. Machine ...

A Machine Learning Driven Pipeline for Automated Photoplethysmogram Signal Artifact Detection.

Recent advances in Critical Congenital Heart Disease (CCHD) research using Photoplethysmography (PPG) signals have yielded an Internet of Things (IoT)...

Predicting Malignancy in Pediatric Thyroid Nodules: Early Experience With Machine Learning for Clinical Decision Support.

OBJECTIVE: To develop a machine learning tool to integrate clinical data for the prediction of non-benign thyroid cytology and histology.

[Study on robot-assisted pedicle screw implantation in adolescent idiopathic scoliosis surgery].

OBJECTIVE: To investigate the safety and accuracy of robot-assisted pedicle screw implantation in the adolescent idiopathic scoliosis (AIS) surgery.

Estimating leaf area index using unmanned aerial vehicle data: shallow vs. deep machine learning algorithms.

Measuring leaf area index (LAI) is essential for evaluating crop growth and estimating yield, thereby facilitating high-throughput phenotyping of maiz...

Classification of fNIRS data with LDA and SVM: a proof-of-concept for application in infant studies.

This study presents the implementation of a within-subject classification method, based on the use of Linear Discriminant Analysis (LDA) and Support V...

Towards Deeper Neural Networks for Neonatal Seizure Detection.

Machine learning and more recently deep learning have become valuable tools in clinical decision making for neonatal seizure detection. This work prop...

Schizophrenia Detection in Adolescents from EEG Signals using Symmetrically weighted Local Binary Patterns.

Schizophrenia is one of the most complex of all mental diseases. In this paper, we propose a symmetrically weighted local binary patterns (SLBP)-based...

Asymmetric Three-dimensional Convolutions For Preterm Infants' Pose Estimation.

Computer-assisted tools for preterm infants' movement monitoring in neonatal intensive care unit (NICU) could support clinicians in highlighting prete...

Unsupervised Deep Learning based Longitudinal Follicular Growth Tracking during IVF Cycle using 3D Transvaginal Ultrasound in Assisted Reproduction.

Longitudinal follicle tracking is needed in clinical practice for diagnosis and management in assisted reproduction. Follicles are tracked over the in...

Artificial Neural Network for Identification of Infant Feeding Tracking Using the Smart Bottle System.

In this work, we present the results of a comparison of simple artificial neural network (FFNN) designs intended to identify infant bottle-feeding eve...

Autonomous Systems and Artificial Intelligence - Hype or Prerequisite for P5 Medicine?

For meeting the challenge of aging, multi-diseased societies, cost containment, workforce development and consumerism by improved care quality and pat...

Research Progress on Developmental Biology of Sarcosaprophagous Insects.

Forensic entomology provides a feasible way to estimate postmortem interval (PMI), of which the growth and development of sarcosaprophagous insects is...

Artificial intelligence in the diagnosis and management of arrhythmias.

The field of cardiac electrophysiology (EP) had adopted simple artificial intelligence (AI) methodologies for decades. Recent renewed interest in deep...

Deep learning analysis of electrocardiogram for risk prediction of drug-induced arrhythmias and diagnosis of long QT syndrome.

AIMS: Congenital long-QT syndromes (cLQTS) or drug-induced long-QT syndromes (diLQTS) can cause torsade de pointes (TdP), a life-threatening ventricul...

Bioinformatics and machine learning methodologies to identify the effects of central nervous system disorders on glioblastoma progression.

Glioblastoma (GBM) is a common malignant brain tumor which often presents as a comorbidity with central nervous system (CNS) disorders. Both CNS disor...

Prediction of Prolonged Opioid Use After Surgery in Adolescents: Insights From Machine Learning.

BACKGROUND: Long-term opioid use has negative health care consequences. Patients who undergo surgery are at risk for prolonged opioid use after surger...

Special issue: Informatics & data-driven medicine.

The current state of the development of Medicine today is changing dramatically. Previously, data of the patient's health were collected only during a...