AIMC Topic: Child

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Applications of artificial intelligence in magnetic resonance imaging of primary pediatric cancers: a scoping review and CLAIM score assessment.

Japanese journal of radiology
PURPOSES: To review the uses of AI for magnetic resonance (MR) imaging assessment of primary pediatric cancer and identify common literature topics and knowledge gaps. To assess the adherence of the existing literature to the Checklist for Artificial...

Characteristic analysis of epileptic brain network based on attention mechanism.

Scientific reports
Constructing an efficient and accurate epilepsy detection system is an urgent research task. In this paper, we developed an EEG-based multi-frequency multilayer brain network (MMBN) and an attentional mechanism based convolutional neural network (AM-...

An FDA Guide on Indications for Use and Device Reporting of Artificial Intelligence-Enabled Devices: Significance for Pediatric Use.

Journal of the American College of Radiology : JACR
Radiology has been a pioneer in adopting artificial intelligence (AI)-enabled devices into the clinic. However, initial clinical experience has identified concerns of inconsistent device performance across different patient populations. Medical devic...

Chapter 8: Risk Assessment: Considerations for Coronal Caries.

Monographs in oral science
Caries risk assessment (CRA) is essential to delivering personalized/precision care in caries management. Limited formal evaluation and validation of existing CRA tools affects the ability to accurately predict new lesions. However, this should not p...

Machine learning to predict poor school performance in paediatric survivors of intensive care: a population-based cohort study.

Intensive care medicine
PURPOSE: Whilst survival in paediatric critical care has improved, clinicians lack tools capable of predicting long-term outcomes. We developed a machine learning model to predict poor school outcomes in children surviving intensive care unit (ICU).

Optimal Combination of Mother Wavelet and AI Model for Precise Classification of Pediatric Electroretinogram Signals.

Sensors (Basel, Switzerland)
The continuous advancements in healthcare technology have empowered the discovery, diagnosis, and prediction of diseases, revolutionizing the field. Artificial intelligence (AI) is expected to play a pivotal role in achieving the goals of precision m...

Deep-learning approach to detect childhood glaucoma based on periocular photograph.

Scientific reports
Childhood glaucoma is one of the major causes of blindness in children, however, its diagnosis is of great challenge. The study aimed to demonstrate and evaluate the performance of a deep-learning (DL) model for detecting childhood glaucoma based on ...

Implications of Pediatric Artificial Intelligence Challenges for Artificial Intelligence Education and Curriculum Development.

Journal of the American College of Radiology : JACR
Several radiology artificial intelligence (AI) courses are offered by a variety of institutions and educators. The major radiology societies have developed AI curricula focused on basic AI principles and practices. However, a specific AI curriculum f...

StethAid: A Digital Auscultation Platform for Pediatrics.

Sensors (Basel, Switzerland)
(1) Background: Mastery of auscultation can be challenging for many healthcare providers. Artificial intelligence (AI)-powered digital support is emerging as an aid to assist with the interpretation of auscultated sounds. A few AI-augmented digital s...