Pediatrics

Healthcare leaders' experiences of implementing artificial intelligence for medical history-taking and triage in Swedish primary care: an interview study.

BACKGROUND: Artificial intelligence (AI) holds significant promise for enhancing the efficiency and safety of medical history-taking and triage within primary care. However, there remains a dearth of knowledge concerning the practical implementation of AI systems for these purposes, particularly in the context of healthcare leadership. This study explores the experiences of healthcare leaders rega...

Green finance growth prediction model based on time-series conditional generative adversarial networks.

Climate change mitigation necessitates increased investment in green sectors. This study proposes a methodology to predict green finance growth across various countries, aiming to encourage such investments. Our approach leverages time-series Conditional Generative Adversarial Networks (CT-GANs) for data augmentation and Nonlinear Autoregressive Neural Networks (NARNNs) for prediction. The green f...

Machine learning-enhanced HRCT analysis for diagnosis and severity assessment in pediatric asthma.

OBJECTIVES: Chest high-resolution computed tomography (HRCT) is conditionally recommended to rule out conditions that mimic or coexist with severe ast...

Amino acid metabolomics and machine learning-driven assessment of future liver remnant growth after hepatectomy in livers of various backgrounds.

Accurate assessment of future liver remnant growth after partial hepatectomy (PH) in patients with different liver backgrounds is a pressing clinical ...

From Web to RheumaLpack: Creating a Linguistic Corpus for Exploitation and Knowledge Discovery in Rheumatology.

This study introduces RheumaLinguisticpack (RheumaLpack), the first specialised linguistic web corpus designed for the field of musculoskeletal disord...

Valuing good health care: How medical doctors, scientists and patients relate ethical challenges with artificial intelligence decision-making support tools in prostate cancer diagnostics to good health care.

Artificial intelligence (AI) is increasingly used in health care to improve diagnostics and treatment. Decision-making tools intended to help professi...

Deep learning classification of pediatric spinal radiographs for use in large scale imaging registries.

PURPOSE: The purpose of this study is to develop and apply an algorithm that automatically classifies spine radiographs of pediatric scoliosis patient...

Deep learning based detection and classification of fetal lip in ultrasound images.

OBJECTIVES: Fetal cleft lip is a common congenital defect. Considering the delicacy and difficulty of observing fetal lips, we have utilized deep lear...

Quantifying massively parallel microbial growth with spatially mediated interactions.

Quantitative understanding of microbial growth is an essential prerequisite for successful control of pathogens as well as various biotechnology appli...

Application of a machine learning and optimization method to predict patellofemoral instability risk factors in children and adolescents.

PURPOSE: Conservative treatment remains the standard approach for first-time patellar dislocations. While risk factors for patellofemoral instability,...

Artificial Intelligence and Pediatric Otolaryngology.

Artificial intelligence (AI) studies show how to program computers to simulate human intelligence and perform data interpretation, learning, and adapt...

Pediatric cardiac surgery: machine learning models for postoperative complication prediction.

PURPOSE: Managing children undergoing cardiac surgery with cardiopulmonary bypass (CPB) presents a significant challenge for anesthesiologists. Machin...

Estimating infant age from skull X-ray images using deep learning.

This study constructed deep learning models using plain skull radiograph images to predict the accurate postnatal age of infants under 12 months. Util...

A preliminary prediction model of pediatric Mycoplasma pneumoniae pneumonia based on routine blood parameters by using machine learning method.

BACKGROUND: The prevalence and severity of pediatric Mycoplasma pneumoniae pneumonia (MPP) poses a significant threat to the health and lives of child...

Predicting Post-surgery Discharge Time in Pediatric Patients Using Machine Learning.

BACKGROUND: Prolonged hospital stays after pediatric surgeries, such as tonsillectomy and adenoidectomy, pose significant concerns regarding cost and ...

Diagnostic performance of an AI algorithm for the detection of appendicular bone fractures in pediatric patients.

PURPOSE: To evaluate the diagnostic performance of an Artificial Intelligence (AI) algorithm, previously trained using both adult and pediatric patien...

Host-derived protein profiles of human neonatal meconium across gestational ages.

Meconium, a non-invasive biomaterial reflecting prenatal substance accumulation, could provide valuable insights into neonatal health. However, the co...

Code-mixing unveiled: Enhancing the hate speech detection in Arabic dialect tweets using machine learning models.

Technological developments over the past few decades have changed the way people communicate, with platforms like social media and blogs becoming vita...

The emerging paradigm in pediatric rheumatology: harnessing the power of artificial intelligence.

Artificial intelligence algorithms, with roots extending into the past but experiencing a resurgence and evolution in recent years due to their superi...

A machine learning artefact detection method for single-channel infant event-related potential studies.

. Automated detection of artefact in stimulus-evoked electroencephalographic (EEG) data recorded in neonates will improve the reproducibility and spee...