From Static Diagnosis to Dynamic Guidance : Evolution of Artificial Intelligence in Pediatric Neuroimaging

Comprehensive Summary

The field of pediatric neuroimaging has evolved in its reliance on artificial intelligence (AI) from it being a static to now a more dynamic tool. Image preprocessing steps such as bias-field correction, intensity normalization, and skull stripping enabled an early analytical approach to neuroimaging. Age-specific atlases emerged which were representative of pediatric and infant developmental stages. Progress in diffusion tensor imaging and resting-state functional MRI allowed for visualizing tracts and networks rather than discrete slices and signals. Automated ventricle segmentation allowed for prediction with greater accuracy. Current advancements in deep learning include Convolutional Neural Networks and Vision Transformers. Variational auto encoders and memory-augmented networks improve the ability to detect anomalies on neuroimaging. Generative AI frameworks like generative adversarial networks and diffusion-based frameworks aid in addressing fundamental constraints such as data scarcity and limitations in modality by various methods such as harmonizing datasets and supplementing for missing imaging sequences. AI tools such as Neuro-GPT use an autoencoder approach to review EEG data which is especially helpful for seizure classification. AI allows for immersive visualization techniques such as virtual reality-based rehearsals and mixed reality systems along with computer vision and robotic platforms to enhance and optimize surgical performance. As AI continues to advance so too will it expand its reach in the field of pediatric neuroimaging.

Outcomes and Implications

The advancements in artificial intelligence (AI) create a cognitive shift where AI takes on a dynamic nature that allows for integration in the neuroimaging and surgical workflow. Human review is still required when implementing AI technology due to concerns such as hallucination and bias. Integration of AI in pediatric neuroimaging promotes supported decision making and greater precision in surgical practice.