Machine learning concepts, concerns and opportunities for a pediatric radiologist.

Area of Science:

• Artificial intelligence
• Pediatric radiology
• Machine learning

Background:

• Machine learning (ML) is a rapidly advancing field with significant potential to improve pediatric radiology.
• Understanding ML concepts like supervised, unsupervised, and semisupervised learning is crucial.

Purpose of the Study:

• To explore the applications and challenges of implementing ML in pediatric radiology.
• To provide insights into ML techniques and their relevance to children's imaging.

Main Methods:

• Description of ML types: supervised, unsupervised, and semisupervised learning.
• Explanation of core ML concepts: data partitioning, underfitting, and overfitting.
• Review of ML applications in radiology: detection, classification, and segmentation.

Main Results:

• ML offers potential to enhance the quality and value of pediatric radiology services.
• Significant challenges exist, including the need for large datasets, accurate image labeling by limited pediatric imagers, and technical/regulatory hurdles.
• The opaque nature of convolution neural networks presents a further implementation challenge.

Conclusions:

• Despite challenges, ML presents promising avenues for development in pediatric radiology.
• Collaboration among pediatric radiologists is essential for identifying future ML applications.
• Addressing data and technical challenges is key to unlocking ML's full potential in children's imaging.