Infant

Predicting stunting status among under-5 children in Rwanda using neural network model: Evidence from 2020 Rwanda demographic and health survey.

F1000Research 39981107

BACKGROUND: Stunting is a serious public health concern in Rwanda, affecting around 33.3% of children under five in 2020. The researchers have employed machine learning algorithms to predict stunting in Rwanda; however, few studies used ANNs, despite...

Child, Preschool Female Growth Disorders Health Surveys Humans Infant Male Neural Networks, Computer Rwanda

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Exploring the achievements and forecasting of SDG 3 using machine learning algorithms: Bangladesh perspective.

PloS one 40036282

BACKGROUND: Sustainable Development Goal 3 (SDG 3), focusing on ensuring healthy lives and well-being for all, holds global significance and is particularly vital for Bangladesh. Neonatal Mortality Rate (NMR), Under-5 Mortality Rate (U5MR), Maternal ...

Algorithms Bangladesh Child Mortality Child, Preschool Female Forecasting Humans Infant Infant Mortality Infant, Newborn Machine Learning Maternal Mortality Neural Networks, Computer Sustainable Development

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Development of an artificial intelligence-based multimodal diagnostic system for early detection of biliary atresia.

BMC medicine 40016769

BACKGROUND: Early diagnosis of biliary atresia (BA) is crucial for improving patient outcomes, yet remains a significant global challenge. This challenge may be ameliorated through the application of artificial intelligence (AI). Despite the promise ...

Artificial Intelligence Biliary Atresia Deep Learning Early Diagnosis Female Humans Infant Infant, Newborn Male Prospective Studies Retrospective Studies Ultrasonography

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Machine learning for early diagnosis of Kawasaki disease in acute febrile children: retrospective cross-sectional study in China.

Scientific reports 40000757

Early diagnosis of Kawasaki disease (KD) allows timely treatment to be initiated, thereby preventing coronary artery aneurysms in children. However, it is challenging due to the subjective nature of the diagnostic criteria. This study aims to develop...

Child Child, Preschool China Cross-Sectional Studies Early Diagnosis Female Fever Humans Infant Machine Learning Male Mucocutaneous Lymph Node Syndrome Retrospective Studies ROC Curve

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AI-MET: A deep learning-based clinical decision support system for distinguishing multisystem inflammatory syndrome in children from endemic typhus.

Computers in biology and medicine 39987695

The COVID-19 pandemic brought several diagnostic challenges, including the post-infectious sequelae multisystem inflammatory syndrome in children (MIS-C). Some of the clinical features of this syndrome can be found in other pathologies such as Kawasa...

Child Child, Preschool COVID-19 Decision Support Systems, Clinical Deep Learning Diagnosis, Differential Female Humans Infant Male SARS-CoV-2 Systemic Inflammatory Response Syndrome Typhus, Endemic Flea-Borne

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Machine Learning-Based Pediatric Early Warning Score: Patient Outcomes in a Pre- Versus Post-Implementation Study, 2019-2023.

Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies 39982152

OBJECTIVES: To describe the deployment of pediatric Calculated Assessment of Risk and Triage (pCART), a machine learning (ML) model to predict the risk of the direct ward to the ICU transfer within 12 hours, and the associated improved outcomes among...

Adolescent Child Child, Preschool Early Warning Score Female Hospital Mortality Humans Infant Infant, Newborn Intensive Care Units, Pediatric Machine Learning Male Patient Transfer Respiration, Artificial Risk Assessment Triage

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SegFormer3D: Improving the Robustness of Deep Learning Model-Based Image Segmentation in Ultrasound Volumes of the Pediatric Hip.

Ultrasound in medicine & biology 39979183

Developmental dysplasia of the hip (DDH) is a painful orthopedic malformation diagnosed at birth in 1-3% of all newborns. Left untreated, DDH can lead to significant morbidity including long-term disability. Currently the condition is clinically diag...

Deep Learning Developmental Dysplasia of the Hip Female Humans Imaging, Three-Dimensional Infant Infant, Newborn Male Ultrasonography

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Development and Validation of an Electronic Health Record-Based, Pediatric Acute Respiratory Distress Syndrome Subphenotype Classifier Model.

Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies 40047501

OBJECTIVE: To determine if hyperinflammatory and hypoinflammatory pediatric acute respiratory distress syndrome (PARDS) subphenotypes defined using serum biomarkers can be determined solely from electronic health record (EHR) data using machine learn...

Adolescent Biomarkers Child Child, Preschool Electronic Health Records Female Humans Infant Intensive Care Units, Pediatric Machine Learning Male Phenotype Respiratory Distress Syndrome Retrospective Studies ROC Curve

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Development and validation of a deep learning algorithm for prediction of pediatric recurrent intussusception in ultrasound images and radiographs.

BMC medical imaging 40033220

PURPOSES: To develop a predictive model for recurrent intussusception based on abdominal ultrasound (US) images and abdominal radiographs.

Algorithms Child Child, Preschool Deep Learning Female Humans Infant Intussusception Male Radiography, Abdominal Recurrence Retrospective Studies ROC Curve Ultrasonography

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Identifying Risk Factors for Graft Failure due to Chronic Rejection < 15 Years Post-Transplant in Pediatric Kidney Transplants Using Random Forest Machine-Learning Techniques.

Pediatric transplantation 39981772

BACKGROUND: Chronic rejection forms the leading cause of late graft loss in pediatric kidney transplant recipients. Despite improvement in short-term graft outcomes, chronic rejection impedes comparable progress in long-term graft outcomes.

Adolescent Child Child, Preschool Chronic Disease Female Graft Rejection Humans Infant Kidney Transplantation Machine Learning Male Random Forest Risk Factors

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AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Predicting stunting status among under-5 children in Rwanda using neural network model: Evidence from 2020 Rwanda demographic and health survey.

Exploring the achievements and forecasting of SDG 3 using machine learning algorithms: Bangladesh perspective.

Development of an artificial intelligence-based multimodal diagnostic system for early detection of biliary atresia.

Machine learning for early diagnosis of Kawasaki disease in acute febrile children: retrospective cross-sectional study in China.

AI-MET: A deep learning-based clinical decision support system for distinguishing multisystem inflammatory syndrome in children from endemic typhus.

Machine Learning-Based Pediatric Early Warning Score: Patient Outcomes in a Pre- Versus Post-Implementation Study, 2019-2023.

SegFormer3D: Improving the Robustness of Deep Learning Model-Based Image Segmentation in Ultrasound Volumes of the Pediatric Hip.

Development and Validation of an Electronic Health Record-Based, Pediatric Acute Respiratory Distress Syndrome Subphenotype Classifier Model.

Development and validation of a deep learning algorithm for prediction of pediatric recurrent intussusception in ultrasound images and radiographs.

Identifying Risk Factors for Graft Failure due to Chronic Rejection < 15 Years Post-Transplant in Pediatric Kidney Transplants Using Random Forest Machine-Learning Techniques.

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