AIMC Topic: Child

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Transfer learning in spirometry: CNN models for automated flow-volume curve quality control in paediatric populations.

Computers in biology and medicine
PROBLEM: Current spirometers face challenges in evaluating acceptability criteria, often requiring manual visual inspection by trained specialists. Automating this process could improve diagnostic workflows and reduce variability in test assessments.

Deep learning-based automated measurement of hip key angles and auxiliary diagnosis of developmental dysplasia of the hip.

BMC musculoskeletal disorders
OBJECTIVES: Anteroposterior pelvic radiographs remains the most widely employed method for diagnosing developmental dysplasia of the hip. This study aims to evaluate the accuracy of an artificial intelligence model in measuring angles in pelvic radio...

Predicting paediatric asthma exacerbations with machine learning: a systematic review with meta-analysis.

European respiratory review : an official journal of the European Respiratory Society
BACKGROUND: Asthma exacerbations in children pose a significant burden on healthcare systems and families. While traditional risk assessment tools exist, artificial intelligence (AI) offers the potential for enhanced prediction models.

Predicting craniofacial fibrous dysplasia growth status: an exploratory study of a hybrid radiomics and deep learning model based on computed tomography images.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVE: This study aimed to develop 3 models based on computed tomography (CT) images of patients with craniofacial fibrous dysplasia (CFD): a radiomics model (Model Rad), a deep learning (DL) model (Model DL), and a hybrid radiomics and DL model ...

Enhancing Spanish Patient Education Materials: Comparing the Readability of Artificial Intelligence-Generated Spanish Patient Education Materials to the Society of Pediatric Dermatology Spanish Patient Brochures.

Pediatric dermatology
Patient education materials (PEMs) are crucial for improving patient adherence and outcomes; however, they may not be accessible due to high reading levels. Our study used seven readability measures to compare the readability of Spanish PEMs from the...

Subjective well-being of children with special educational needs: Longitudinal predictors using machine learning.

Applied psychology. Health and well-being
Children with special educational needs (SEN) are a diverse group facing numerous challenges related to well-being and mental health. Understanding the predictors of well-being in this population requires the incorporation of diverse factors along wi...

Image-based deep learning in diagnosing mycoplasma pneumonia on pediatric chest X-rays.

BMC pediatrics
BACKGROUND: Correctly diagnosing and accurately distinguishing mycoplasma pneumonia in children has consistently posed a challenge in clinical practice, as it can directly impact the prognosis of affected children. To address this issue, we analyzed ...

Precision of artificial intelligence in paediatric cardiology multimodal image interpretation.

Cardiology in the young
Multimodal imaging is crucial for diagnosis and treatment in paediatric cardiology. However, the proficiency of artificial intelligence chatbots, like ChatGPT-4, in interpreting these images has not been assessed. This cross-sectional study evaluates...

Developmental changes in the perceived moral standing of robots.

Cognition
Emerging evidence suggests that children may think of robots-and artificial intelligence, more generally-as having moral standing. In this paper, we trace the developmental trajectory of this belief. Over three developmental studies (combined N = 415...

Height prediction of individuals with osteogenesis imperfecta by machine learning.

Orphanet journal of rare diseases
BACKGROUND: Osteogenesis imperfecta (OI) is a genetic disorder characterized by low bone mass, bone fragility and short stature. There is a significant gap in knowledge regarding the growth patterns across different types of OI, and the prediction of...