Pediatrics

Artificial intelligence for the diagnosis of pediatric appendicitis: A systematic review.

BACKGROUND: While acute appendicitis is the most frequent surgical emergency in children, its diagnosis remains complex. Artificial intelligence (AI) and machine learning (ML) tools have been employed to improve the accuracy of various diagnoses, including appendicitis. The purpose of this study was to systematically review the current body of evidence regarding the efficacy of AL and ML approache...

Application of deep learning for evaluation of the growth rate of Daphnia magna.

For the safe use of chemicals widely used in human activities, it is crucial to assess their ecological impacts when released into the environment. Daphnia, a well-established environmental indicator species, is commonly used to evaluate the biological effects of chemicals and testing methods have been established. Among various indicators, the growth rate is one of the important parameters, but i...

Artificial Intelligence in Commercial Industry: Serving the End-to-End Patient Experience Across the Digital Ecosystem.

The purpose of this article is to evaluate the application of artificial intelligence (AI) from the perspective of the orthopaedic industry with respe...

Predicting preterm birth using machine learning methods.

Preterm birth is a significant public health concern, given its correlation with neonatal mortality and morbidity. The aetiology of preterm birth is c...

An explainable and accurate transformer-based deep learning model for wheeze classification utilizing real-world pediatric data.

Auscultation is a method that involves listening to sounds from the patient's body, mainly using a stethoscope, to diagnose diseases. The stethoscope ...

AI and early diagnostics: mapping fetal facial expressions through development, evolution, and 4D ultrasound.

The development of facial musculature and expressions in the human fetus represents a critical intersection of developmental biology, neurology, and e...

Differentiating adolescent suicidal and nonsuicidal self-harm with artificial intelligence: Beyond suicidal intent and capability for suicide.

Clinical differentiation between adolescent suicidal self-harm (SSH) and nonsuicidal self-harm (NSSH) is a significant challenge for mental health pro...

Automated pediatric TMJ articular disk identification and displacement classification in MRI with machine learning.

OBJECTIVE: To evaluate the performance of an automated two-step model interpreting pediatric temporomandibular joint (TMJ) magnetic resonance imaging ...

[Digital innovations in vaccination communication].

Despite the significant success of vaccinations, increasing vaccine hesitancy poses a threat to public health, making effective vaccination communicat...

Tailoring ventilation and respiratory management in pediatric critical care: optimizing care with precision medicine.

PURPOSE OF REVIEW: Critically ill children admitted to the intensive care unit frequently need respiratory care to support the lung function. Mechanic...

Identifying Adolescent Depression and Anxiety Through Real-World Data and Social Determinants of Health: Machine Learning Model Development and Validation.

BACKGROUND: The prevalence of adolescent mental health conditions such as depression and anxiety has significantly increased. Despite the potential of...

Prediction of adverse pregnancy outcomes using machine learning techniques: evidence from analysis of electronic medical records data in Rwanda.

BACKGROUND: Despite substantial progress in maternal and neonatal health, Rwanda's mortality rates remain high, necessitating innovative approaches to...

A novel framework to predict ADHD symptoms using irritability in adolescents and young adults with and without ADHD.

BACKGROUND: Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder in children and adolescents characterized by persi...

Predictive modeling of methadone poisoning outcomes in children ≤ 5 years: utilizing machine learning and the National Poison Data System for improved clinical decision-making.

UNLABELLED: The escalating therapeutic use of methadone has coincided with an increase in accidental ingestions, particularly among children ≤ 5 years...

Machine learning approaches for predicting fetal macrosomia at different stages of pregnancy: a retrospective study in China.

BACKGROUND: Macrosomia presents significant risks to both maternal and neonatal health, however, accurate antenatal prediction remains a major challen...

One size fits all: Enhanced zero-shot text classification for patient listening on social media.

Patient-focused drug development (PFDD) represents a transformative approach that is reshaping the pharmaceutical landscape by centering on patients t...

Machine learning-enhanced surface-enhanced spectroscopic detection of polycyclic aromatic hydrocarbons in the human placenta.

The detection and identification of polycyclic aromatic hydrocarbons (PAHs) and their derivatives, polycyclic aromatic compounds (PACs), are essential...

SleepECG-Net: Explainable Deep Learning Approach With ECG for Pediatric Sleep Apnea Diagnosis.

Obstructive sleep apnea (OSA) in children is a prevalent and serious respiratory condition linked to cardiovascular morbidity. Polysomnography, the st...

Ultrasonic-Based Radiomics Signature With Machine Learning for Differentiating Prognostic Subsets of Pediatric Peripheral Neuroblastic Tumors: A Retrospective Study.

OBJECTIVE: To construct and select a better model based on ultrasonic-based radiomics features and clinical characteristics for prognostic subsets of ...

Geometric deep learning with adaptive full-band spatial diffusion for accurate, efficient, and robust cortical parcellation.

Cortical parcellation delineates the cerebral cortex into distinct regions according to their distinctiveness in anatomy and/or function, which is a f...