Pediatrics

Lightweight interpretable AI model using multiple blood parameters for emergency diagnosis of acute appendicitis.

BACKGROUND: Acute appendicitis poses diagnostic challenges due to symptom overlap with other abdominal conditions, often leading to misdiagnosis or missed diagnosis. This study aimed to develop and validate an interpretable machine learning model based on routine hematological indicators to facilitate rapid diagnosis. METHODS: A retrospective analysis was conducted on 408 patients with acute abdom...

P300-based support vector machine model for identifying adolescent with first-episode, drug-naïve major depressive disorder.

BACKGROUND: Major depressive disorder (MDD) is diagnosed mainly through clinical interviews, highlighting a need for objective neurophysiological measures. Although P300 abnormalities have been well documented in adults with MDD, evidence in adolescents remains scarce. This study examined P300 alterations in adolescents with first-episode, drug-naïve MDD and evaluated its diagnostic potential. MET...

A deep learning-based tool for rapid and automated detection of Cryptosporidium oocysts: A new approach for veterinary diagnostics and epizootiological surveys.

Cryptosporidiosis, caused by Cryptosporidium spp., is a significant zoonotic parasitic disease impacting neonatal calves, leading to severe diarrhea, ...

Development of a new tool for prediction of hospital length of stay and intensive care needs in trauma patients using Machine Learning.

BACKGROUND: Trauma is a major global health burden leading to significant morbidity, disability, and mortality. Predictive models in trauma care tradi...

Machine Learning-Based Prediction Model for Delayed Chemotherapy-Induced Nausea and Vomiting in Pediatric Cancer: A Prospective Cohort Study.

BACKGROUND: Delayed chemotherapy-induced nausea and vomiting (CINV) in pediatric oncology patients is currently under-recognized. This study aims to d...

Emerging applications of artificial intelligence for obstetric ultrasound: A scoping review.

BACKGROUND: The WHO recommends that all pregnant women receive an ultrasound (US) scan prior to 24 weeks gestation to encourage early identification o...

Machine learning and causal inference applied to the gut metagenome-metabolome axis reveals a link between neonatal jaundice and autism spectrum disorder.

UNLABELLED: Neonatal jaundice (NJ) might increase the risk of autism spectrum disorder (ASD) in children. This study examined whether alterations in t...

Multicenter Evaluation of an Artificial Intelligence System for Automatic Recognition of Fetal Ultrasound Findings Suggestive of Congenital Malformations.

OBJECTIVE: To evaluate the diagnostic performance of an artificial intelligence (AI) system for detecting eight abnormal fetal ultrasound findings acr...

Multi-Architecture deep learning for CBCT segmentation of dental hard tissues and pulp in mixed dentition.

OBJECTIVE: To develop and evaluate deep learning-based 3D models (CNN, Transformer, and Mamba architectures) for automated segmentation of pulp, prima...

Comparative study of the performance of two versions of the same AI tool for pediatric skeletal maturation assessment.

OBJECTIVES: To compare BoneXpert version 3 with version 2 in estimating bone age and bone health index (BHI), focusing on mean absolute error (MAE) an...

Programmable lipid nanoparticles for RNA therapeutics: Design principles and clinical translation.

RNA therapeutics have come of age as clinically validated modalities including mRNA, siRNA, antisense oligonucleotides (ASOs), and in vivo genome edit...

Advances in melanoma immunotherapy: harnessing vaccination as a potential novel immunotherapeutic strategy.

Melanoma, an aggressive form of skin cancer, demands innovative immunotherapeutic strategies to improve patient outcomes. This review explores recent ...

Axial and choroidal changes as key predictors of myopia control in red-light therapy: Evidence from machine learning models.

BACKGROUND: Repeated low-level red-light (RLRL) therapy has emerged as a promising non-invasive intervention for myopia control. However, the predicti...

Engagement, preference and identity: Proposing a developmental engagement framework.

Understanding how adolescents participate in science requires examining the developmental foundations of engagement and identity formation. This theor...

A fully autonomous AI system for accurate and reproducible Cobb angle measurement in adolescent idiopathic scoliosis: a multicenter study.

BACKGROUND CONTEXT: The gold standard diagnostic test for Adolescent idiopathic scoliosis (AIS) involves manually measuring the spinal column deformit...

Advancements in radiation dose reduction for pediatric CT head Imaging: A scoping review of emerging Technologies, Protocols, and optimization strategies.

Computed tomography (CT) is an important imaging modality that provides cross-sectional images, aiding in the detailed visualization of internal struc...

Acute kidney injury: Detection, risk stratification, and predictive biomarkers.

BACKGROUND: Acute kidney injury (AKI) is a complex multifactorial syndrome characterized by a rapid decline in kidney function, frequently observed in...

Artificial intelligence for personalized management of vestibular schwannoma: a multidisciplinary clinical implementation study.

OBJECTIVES: Management of patients with vestibular schwannoma (VS) relies on precise tumor size and growth trend evaluation. We introduce and evaluate...

Unlocking academic gains in smart-classroom settings: A moderated-mediation study among Chinese undergraduates.

China's higher education system has invested heavily in the development of sensor-rich smart classrooms, but how these environments translate into aca...

Using artificial intelligence to rewrite the script on pediatric ENT patient education.