Current Perspectives of Artificial Intelligence in Pediatric Neuroradiology: An Overview

Abstract

Artificial Intelligence, Machine Learning, and myriad related techniques are becoming ever more commonplace throughout industry and society, and radiology is by no means an exception. It is essential for every radiologists of every subspecialty to gain familiarity and confidence with these techniques as they become increasingly incorporated into the routine practice in both academic and private practice settings. In this article, we provide a brief review of several definitions and techniques that are commonly used in AI, and in particular machine vision, and examples of how they are currently being applied to the setting of clinical neuroradiology. We then review the unique challenges that the adoption and application of faces within the subspecialty of pediatric neuroradiology, and how these obstacles may be overcome. We conclude by presenting specific examples of how AI is currently being applied within the field of pediatric neuroradiology and the potential opportunities that are available for future applications.

Automated tumor segmentation and brain tissue extraction from multiparametric MRI of pediatric brain tumors: A multi-institutional study

“…Availability of a pediatric-specific model is preferred over applying existing the pre-trained models generated based on adult brain tumors, as the structure and MR image signal intensities vary largely in pediatric cohorts with developing brains. 16 …”

“… 9–14 Such models do not generalize well to PBTs 15 due to the different radiological appearance of tumors compared to adult brain tumors, and the anatomical differences as a result of the developing brain in children. 16 A few studies have proposed different DL solutions to the PBT segmentation problem, with whole tumor Dice scores ranging between 0.68 and 0.76. 17–20 These approaches show lower performance than the models proposed for adult brain tumor segmentation, are often only designed for a particular histology, only segment the whole tumor without subregions, or segment the tumors based on one or two MRI sequences.…”

Automated Tumor Segmentation and Brain Tissue Extraction from Multiparametric MRI of Pediatric Brain Tumors: A Multi-Institutional Study

“…The proposed brain extraction model achieved Dice score of ≥0.97 on all data subsets, confirming its reproducible and generalizable performance to the unseen data from internal and external cohorts. Availability of a pediatric-specific model is preferred over applying existing the pre-trained models generated based on adult brain tumors, as the structure and MR image signal intensities vary largely in pediatric cohorts with developing brains 16 .…”

“…The current state-of-the-art approach for automated brain tumor segmentation is deep learning (DL); however, most available auto-segmentation tools have been trained and made available for use in adult cancers only [9][10][11][12][13][14] . Such models do not generalize well to PBTs 15 due to the different radiological appearance of tumors compared to adult brain tumors, and the anatomical differences as a result of the developing brain in children 16 . A few studies have proposed different DL solutions to the PBT segmentation problem, with whole tumor Dice scores ranging between 0.68-0.76 [17][18][19][20] .…”

A Siamese U-Transformer for change detection on MRI brain for multiple sclerosis, a model development and external validation study

“…Indeed, lack of new lesions in the CNS is a key indicator of medication effectiveness 5 . Monitoring these lesions, however, is often monotonous and repetitive for neuroradiologists 6 , and this combined with radiology’s supply-demand challenges 7 has led to increased interest in methods for automating lesion detection 8 . Over the past two decades, research has heavily focused on computer-assisted segmentation methods 8 , with a recent surge in AI methodologies 9 .…”