AIMC Topic: Infant

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Electroencephalographic abnormalities in children with type 1 diabetes mellitus: a prospective study.

Turkish journal of medical sciences
BACKGROUND/AIM: The aim herein was to investigate epileptiform discharges on electroencephalogram (EEG), their correlation with glutamic acid decarboxylase 65 autoantibody (GAD-ab) in newly diagnosed pediatric type 1 diabetes mellitus (T1DM) patients...

AI Model Versus Clinician Otoscopy in the Operative Setting for Otitis Media Diagnosis.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
Prior work has demonstrated improved accuracy in otitis media diagnosis based on otoscopy using artificial intelligence (AI)-based approaches compared to clinician evaluation. However, this difference in accuracy has not been shown in a setting resem...

Walking and falling: Using robot simulations to model the role of errors in infant walking.

Developmental science
What is the optimal penalty for errors in infant skill learning? Behavioral analyses indicate that errors are frequent but trivial as infants acquire foundational skills. In learning to walk, for example, falling is commonplace but appears to incur o...

Deep Learning to Optimize Magnetic Resonance Imaging Prediction of Motor Outcomes After Hypoxic-Ischemic Encephalopathy.

Pediatric neurology
BACKGROUND: Magnetic resonance imaging (MRI) is the gold standard for outcome prediction after hypoxic-ischemic encephalopathy (HIE). Published scoring systems contain duplicative or conflicting elements.

Deep learning-based segmentation of whole-body fetal MRI and fetal weight estimation: assessing performance, repeatability, and reproducibility.

European radiology
OBJECTIVES: To develop a deep-learning method for whole-body fetal segmentation based on MRI; to assess the method's repeatability, reproducibility, and accuracy; to create an MRI-based normal fetal weight growth chart; and to assess the sensitivity ...

Infant death prediction using machine learning: A population-based retrospective study.

Computers in biology and medicine
BACKGROUND: Despite declines in infant death rates in recent decades in the United States, the national goal of reducing infant death has not been reached. This study aims to predict infant death using machine-learning approaches.

Development of a Radiomics-Based Model to Predict Graft Fibrosis in Liver Transplant Recipients: A Pilot Study.

Transplant international : official journal of the European Society for Organ Transplantation
Liver Transplantation is complicated by recurrent fibrosis in 40% of recipients. We evaluated the ability of clinical and radiomic features to flag patients at risk of developing future graft fibrosis. CT scans of 254 patients at 3-6 months post-live...

Correlation of urinary continence recovery with various factors after Robot assisted radical prostatectomy.

Urologia
BACKGROUND: In addition to ensuring cancer control, prevention of incontinence which significantly impact patients' quality of life, is also an important issue in robot-assisted radical prostatectomy (RARP) operations. In this study, we aimed to find...

Does previous endoscopic subureteric injection (STING) effect the outcomes of robot-assisted laparoscopic ureteral reimplantation surgery (RALUR) in children?

Journal of pediatric urology
BACKGROUND: There is lack of evidence on the success of robot-assisted laparoscopic ureteral reimplantation (RALUR) for the treatment of vesicoureteral reflux (VUR) who had prior intervention.