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Detection of neurologic changes in critically ill infants using deep learning on video data: a retrospective single center cohort study.
EClinicalMedicine
December 2024
Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Background: Infant alertness and neurologic changes can reflect life-threatening pathology but are assessed by physical exam, which can be intermittent and subjective. Reliable, continuous methods are needed. We hypothesized that our computer vision method to track movement, pose artificial intelligence (AI), could predict neurologic changes in the neonatal intensive care unit (NICU).
View Article and Find Full Text PDFComparative analysis of gut microbiota in metabolic syndrome and obese children from Southeastern China.
Front Microbiol
December 2024
Department of Pediatrics, Ningde Municipal Hospital of Ningde Normal University, Ningde, China.
The prevalence of childhood obesity is rising globally, with some obese children progressing to develop metabolic syndrome (MS). However, the specific differences between these groups remain unclear. To investigate the differences in gut microbiota, we conducted physiological and biochemical assessments, alongside 16S rRNA sequencing, in a cohort of 32 children from Southeastern China, which included 4 normal-weight children, 5 with mild obesity, 9 with moderate obesity, 9 with severe obesity, and 5 with metabolic syndrome.
View Article and Find Full Text PDFIntroduction The pediatric intensive care unit (PICU) is a specialized area for treating critically ill infants and children. However, some of these children may experience poor outcomes, including death. However, it is necessary to predict the prognosis for critically ill patients as early as possible to commence triage as well as an early and effective intervention to prevent mortality.
View Article and Find Full Text PDFMulti-modal prediction of extracorporeal support-a resource intensive therapy, utilizing a large national database.
JAMIA Open
February 2025
Artificial Intelligence (AI) for Health Institute (AIHealth), Washington University in St Louis, St Louis, MO 63130, United States.
Objective: Extracorporeal membrane oxygenation (ECMO) is among the most resource-intensive therapies in critical care. The COVID-19 pandemic highlighted the lack of ECMO resource allocation tools. We aimed to develop a continuous ECMO risk prediction model to enhance patient triage and resource allocation.
View Article and Find Full Text PDFBackground: Alport syndrome (AS) is a multifaceted condition that primarily affects the basement membranes of the kidneys, ears, and eyes. AS is considered the second most common cause of hereditary renal failure, exhibiting varied clinical manifestations across different lifespans. The aim of this study is to investigate the clinical features and genetic profile of AS and to elucidate the genotype-phenotype correlation of AS.
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