AIMC Topic: Child, Preschool

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Machine-learning based prediction of future outcome using multimodal MRI during early childhood.

Seminars in fetal & neonatal medicine
The human brain undergoes rapid changes from the fetal stage to two years postnatally, during which proper structural and functional maturation lays the foundation for later cognitive and behavioral development. Multimodal magnetic resonance imaging ...

A critical comparative study of the performance of three AI-assisted programs for bone age determination.

European radiology
OBJECTIVES: To date, AI-supported programs for bone age (BA) determination for medical use in Europe have almost only been validated separately, according to Greulich and Pyle (G&P). Therefore, the current study aimed to compare the performance of th...

Developing an AI-based application for caries index detection on intraoral photographs.

Scientific reports
This study evaluates the effectiveness of an Artificial Intelligence (AI)-based smartphone application designed for decay detection on intraoral photographs, comparing its performance to that of junior dentists. Conducted at The Aga Khan University H...

Detection of Right and Left Ventricular Dysfunction in Pediatric Patients Using Artificial Intelligence-Enabled ECGs.

Journal of the American Heart Association
BACKGROUND: Early detection of left and right ventricular systolic dysfunction (LVSD and RVSD respectively) in children can lead to intervention to reduce morbidity and death. Existing artificial intelligence algorithms can identify LVSD and RVSD in ...

Identification of novel markers for neuroblastoma immunoclustering using machine learning.

Frontiers in immunology
BACKGROUND: Due to the unique heterogeneity of neuroblastoma, its treatment and prognosis are closely related to the biological behavior of the tumor. However, the effect of the tumor immune microenvironment on neuroblastoma needs to be investigated,...

Machine learning model identifies genetic predictors of cisplatin-induced ototoxicity in CERS6 and TLR4.

Computers in biology and medicine
BACKGROUND: Cisplatin-induced ototoxicity remains a significant concern in pediatric cancer treatment due to its permanent impact on quality of life. Previously, genetic association analyses have been performed to detect genetic variants associated w...

Screening biomarkers for autism spectrum disorder using plasma proteomics combined with machine learning methods.

Clinica chimica acta; international journal of clinical chemistry
BACKGROUND AND AIMS: Autism spectrum disorder (ASD) is a common neurodevelopmental disorder in children. Early intervention is effective. Investigation of novel blood biomarkers of ASD facilitates early detection and intervention.

Clinical and socioeconomic predictors of hospital use and emergency department visits among children with medical complexity: A machine learning approach using administrative data.

PloS one
OBJECTIVES: The primary objective of this study was to identify clinical and socioeconomic predictors of hospital and ED use among children with medical complexity within 1 and 5 years of an initial discharge between 2010 and 2013. A secondary object...

Effects of interval treadmill training on spatiotemporal parameters in children with cerebral palsy: A machine learning approach.

Journal of biomechanics
Quantifying individualized rehabilitation responses and optimizing therapy for each person is challenging. For interventions like treadmill training, there are multiple parameters, such as speed or incline, that can be adjusted throughout sessions. T...

A deep-learning-based histopathology classifier for focal cortical dysplasia (FCD) unravels a complex scenario of comorbid FCD subtypes.

Epilepsia
OBJECTIVE: Recently, we developed a first artificial intelligence (AI)-based digital pathology classifier for focal cortical dysplasia (FCD) as defined by the ILAE classification. Herein, we tested the usefulness of the classifier in a retrospective ...