#pediatric-healthcare News & Analysis

4 articles tagged with #pediatric-healthcare. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AINeutralarXiv – CS AI · Jun 255/10

🧠

Phoneme-Level Mispronunciation Screening in Polish-Speaking Children with an Explainable Assistant

Researchers developed an AI-powered screening tool for detecting speech sound errors in Polish-speaking children, using wav2vec2 technology to identify sibilant substitutions. The system achieves 88.7% accuracy on a test set and demonstrates 72.9% precision with a 2.7% false-alarm rate, designed as a lightweight alternative to specialist evaluation for early intervention.

AINeutralarXiv – CS AI · Jun 196/10

🧠

PSCT-Net: Geometry-Aware Pediatric Skull CT Reconstruction via Differentiable Back-Projection and Attention-Guided Refinement

Researchers introduce PSCT-Net, a novel AI framework that reconstructs 3D pediatric skull CT scans from sparse 2D X-rays using differentiable back-projection and attention mechanisms, reducing radiation exposure to children while maintaining diagnostic accuracy. The team also releases PedSkull-CT, a new pediatric-focused dataset addressing the lack of child-specific medical imaging benchmarks in existing research.

AINeutralarXiv – CS AI · Jun 106/10

🧠

Automated Pronunciation Evaluation for Korean Toddler Speech using Speech Diarization and Self-Supervised Learning

Researchers have developed an automated system for evaluating Korean toddler pronunciation using speaker diarization and self-supervised learning models, addressing a significant gap in speech assessment tools for this demographic. The system achieved balanced accuracies of 0.720 for consonants and 0.845 for vowels by routing predictions through specialized SSL models, offering potential clinical applications for detecting speech sound disorders affecting nearly half of Korean pediatric cases.

AIBullisharXiv – CS AI · May 286/10

🧠

BuddyBench: A Privacy-Constrained Multi-Task Benchmark for Pediatric Social-Communication Personalization

BuddyBench introduces a privacy-protected multi-task benchmark dataset combining clinical assessments, learning trajectories, and treatment outcomes for pediatric social-communication research. The dataset integrates two cohorts (189 observational and 86 randomized controlled trial participants) to enable knowledge tracing, clinical prediction, and causal inference while maintaining pediatric data protection standards.