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Infant

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Generalizability and Bias in a Deep Learning Pediatric Bone Age Prediction Model Using Hand Radiographs.

Radiology
Background Although deep learning (DL) models have demonstrated expert-level ability for pediatric bone age prediction, they have shown poor generalizability and bias in other use cases. Purpose To quantify generalizability and bias in a bone age DL ...

The three horizons model applied to medical science.

Postgraduate medicine
The three horizons model is a framework that helps manage an organization's innovation strategy. This model considers three aspects (horizons) that should be present in the institution and guide the development of new systems. Applied to medical scie...

Deep Relation Learning for Regression and Its Application to Brain Age Estimation.

IEEE transactions on medical imaging
Most deep learning models for temporal regression directly output the estimation based on single input images, ignoring the relationships between different images. In this paper, we propose deep relation learning for regression, aiming to learn diffe...

Single-port robot-assisted laparoscopic pyeloplasty in an infant: A video case report with 9 months follow up.

Journal of pediatric urology
INTRODUCTION: Single-port robot-assisted laparoscopic surgery is a breakthrough in the field of minimally invasive surgery. However, it is currently only applicable to older children due to the limitation of operating space. Here, we report a case of...

Using Healthcare Resources Wisely: A Predictive Support System Regarding the Severity of Patient Falls.

Journal of healthcare engineering
BACKGROUND: An injurious fall is one of the main indicators of care quality in healthcare facilities. Despite several fall screen tools being widely used to evaluate a patient's fall risk, they are frequently unable to reveal the severity level of pa...

Health Information Prediction System of Infant Sports Based on Deep Learning Network.

BioMed research international
The sensed data from infant sports and training programs are useful in analyzing their health conditions and forecasting any disorders or abnormalities. The sensed information is processed for providing errorless predictions for infant diseases/disor...

Deep learning imaging features derived from kidney ultrasounds predict chronic kidney disease progression in children with posterior urethral valves.

Pediatric nephrology (Berlin, Germany)
BACKGROUND: We sought to use deep learning to extract anatomic features from postnatal kidney ultrasounds and evaluate their performance in predicting the risk and timing of chronic kidney disease (CKD) progression for boys with posterior urethral va...

Deep Learning to Predict Neonatal and Infant Brain Age from Myelination on Brain MRI Scans.

Radiology
Background Assessment of appropriate brain myelination on T1- and T2-weighted MRI scans is based on gestationally corrected age (GCA) and requires subjective visual inspection of the brain with knowledge of normal myelination milestones. Purpose To d...

Deep learning accurately classifies elbow joint effusion in adult and pediatric radiographs.

Scientific reports
Joint effusion due to elbow fractures are common among adults and children. Radiography is the most commonly used imaging procedure to diagnose elbow injuries. The purpose of the study was to investigate the diagnostic accuracy of deep convolutional ...

Age group prediction with panoramic radiomorphometric parameters using machine learning algorithms.

Scientific reports
The aim of this study is to investigate the relationship of 18 radiomorphometric parameters of panoramic radiographs based on age, and to estimate the age group of people with permanent dentition in a non-invasive, comprehensive, and accurate manner ...