AI Medical Compendium Journal:
Pediatric radiology

Showing 11 to 20 of 58 articles

Deeplasia: deep learning for bone age assessment validated on skeletal dysplasias.

Pediatric radiology
BACKGROUND: Skeletal dysplasias collectively affect a large number of patients worldwide. Most of these disorders cause growth anomalies. Hence, evaluating skeletal maturity via the determination of bone age (BA) is a useful tool. Moreover, consecuti...

A narrative review of radiomics and deep learning advances in neuroblastoma: updates and challenges.

Pediatric radiology
Neuroblastoma is an extremely heterogeneous tumor that commonly occurs in children. The diagnosis and treatment of this tumor pose considerable challenges due to its varied clinical presentations and intricate genetic aberrations. Presently, various ...

Imaging biomarkers and radiomics in pediatric oncology: a view from the PRIMAGE (PRedictive In silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers) project.

Pediatric radiology
This review paper presents the practical development of imaging biomarkers in the scope of the PRIMAGE (PRedictive In silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers) project, as a n...

Artificial intelligence to identify fractures on pediatric and young adult upper extremity radiographs.

Pediatric radiology
BACKGROUND: Pediatric fractures are challenging to identify given the different response of the pediatric skeleton to injury compared to adults, and most artificial intelligence (AI) fracture detection work has focused on adults.

The unintended consequences of artificial intelligence in paediatric radiology.

Pediatric radiology
Over the past decade, there has been a dramatic rise in the interest relating to the application of artificial intelligence (AI) in radiology. Originally only 'narrow' AI tasks were possible; however, with increasing availability of data, teamed with...

Current state of radiomics in pediatric neuro-oncology practice: a systematic review.

Pediatric radiology
BACKGROUND: Radiomics is the process of converting radiological images into high-dimensional data that may be used to create machine learning models capable of predicting clinical outcomes, such as disease progression, treatment response and survival...

An automatic and accurate deep learning-based neuroimaging pipeline for the neonatal brain.

Pediatric radiology
BACKGROUND: Accurate segmentation of neonatal brain tissues and structures is crucial for studying normal development and diagnosing early neurodevelopmental disorders. However, there is a lack of an end-to-end pipeline for automated segmentation and...