PubMed, Embase, and Scopus were searched for relevant articles up to January 27, 2021.
Literature search identified 298 records, of which 22 studies were included. The most commonly studied tumors were posterior fossa tumors, including brainstem glioma, ependymoma, medulloblastoma, and pilocytic astrocytoma (15, 68%). Tumor diagnosis was the most frequently performed task (14, 64%), followed by tumor segmentation (3, 14%) and tumor detection (3, 14%). Of the 6 studies comparing AI to clinical experts, 5 demonstrated superiority of AI for tumor diagnosis. Other tasks, including tumor segmentation, attenuation correction of positron emission tomography scans, image registration for patient positioning, and dose calculation for radiotherapy, were performed with high accuracy comparable to clinical experts. No studies described use of the AI tool in routine clinical practice.
AI methods for analysis of pediatric brain tumor imaging have increased exponentially in recent years. However, adoption of these methods in clinical practice requires further characterization of validity and utility. Implementation of these methods may streamline clinical workflows by improving diagnostic accuracy and automating basic imaging analysis tasks.
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