AIMC Topic: Child

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Automatic bone age assessment: a Turkish population study.

Diagnostic and interventional radiology (Ankara, Turkey)
PURPOSE: Established methods for bone age assessment (BAA), such as the Greulich and Pyle atlas, suffer from variability due to population differences and observer discrepancies. Although automated BAA offers speed and consistency, limited research e...

The Touch of Artificial Intelligence in Social Work: Analysis of Social Investigation Reports in Child Welfare with ChatGPT.

Journal of evidence-based social work (2019)
PURPOSE: This research aimed to examine the responses given by ChatGPT to seven social investigation reports prepared in the field of children regarding deficiencies, risk factors and protective factors, social work intervention plan and institution ...

Attributional patterns toward students with and without learning disabilities: Artificial intelligence models vs. trainee teachers.

Research in developmental disabilities
This study explored differences in the attributional patterns of four advanced artificial intelligence (AI) Large Language Models (LLMs): ChatGPT3.5, ChatGPT4, Claude, and Gemini) by focusing on feedback, frustration, sympathy, and expectations of fu...

Effectiveness of machine learning methods in detecting grooming: a systematic meta-analytic review.

Scientific reports
This study presents a systematic review (SR) and meta-analysis (MA) on the use of machine learning (ML) methods for detecting online grooming, a form of manipulation and child sexual abuse. The SR identified 33 studies from IEEE, Web of Science, Scop...

Optimized attention-enhanced U-Net for autism detection and region localization in MRI.

Psychiatry research. Neuroimaging
Autism spectrum disorder (ASD) is a neurodevelopmental condition that affects a child's cognitive and social skills, often diagnosed only after symptoms appear around age 2. Leveraging MRI for early ASD detection can improve intervention outcomes. Th...

Explainable artificial intelligence to quantify adenoid hypertrophy-related upper airway obstruction using 3D Shape Analysis.

Journal of dentistry
OBJECTIVES: To develop and validate an explainable Artificial Intelligence (AI) model for classifying and quantifying upper airway obstruction related to adenoid hypertrophy using three-dimensional (3D) shape analysis of cone-beam computed tomography...

Semi-Automated Multi-Label Classification of Autistic Mannerisms by Machine Learning on Post Hoc Skeletal Tracking.

Autism research : official journal of the International Society for Autism Research
Mannerisms describe repetitive or unconventional body movements like arm flapping. These movements are early markers of restricted and repetitive behaviors (RRBs) in autism spectrum disorder (ASD). However, assessing mannerisms reliably is challengin...

Deep learning algorithm classification of tympanostomy tube images from a heterogenous pediatric population.

International journal of pediatric otorhinolaryngology
IMPORTANCE: The ability to augment routine post-operative tube check appointments with at-home digital otoscopes and deep learning AI could improve health care access as well as reduce financial and time burden on families.

Three-dimensional markerless surface topography approach with convolutional neural networks for adolescent idiopathic scoliosis screening.

Scientific reports
Adolescent idiopathic scoliosis (AIS) is a three-dimensional lateral and torsional deformity of the spine, affecting up to 5% of the population. Traditional scoliosis screening methods exhibit limited accuracy, leading to unnecessary referrals and ex...

SSAT-Swin: Deep Learning-Based Spinal Ultrasound Feature Segmentation for Scoliosis Using Self-Supervised Swin Transformer.

Ultrasound in medicine & biology
OBJECTIVE: Scoliosis, a 3-D spinal deformity, requires early detection and intervention. Ultrasound curve angle (UCA) measurement using ultrasound images has emerged as a promising diagnostic tool. However, calculating the UCA directly from ultrasoun...