The Expanding Frontier: The Role of Artificial Intelligence in Pediatric Neuroradiology.
Artificial intelligence (AI) is revolutionarily shaping the entire landscape of medicine and particularly the privileged field of radiology, since it produces a significant amount of data, namely, images. Currently, AI implementation in radiology is continuously increasing, from automating image ana...
| Published in: | Children Vol. 12; no. 9; pp. 1127 - 1150 |
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| Main Authors: | , , , , , , , , |
| Format: | diagnostic images review Journal Article |
| Published: |
MDPI
Sep2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=188331329&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 188331329 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 22279067 LW6K jtl: Children issn: 22279067 maglogo: N pubinfo: dt: Sep2025 vid: 12 iid: 9 pid: 97109 pub: MDPI artinfo: ui: 188331329 188331329 188331329 10.3390/children12091127 188331329 ppf: 1127 ppct: 23 formats: tig: atl: The Expanding Frontier: The Role of Artificial Intelligence in Pediatric Neuroradiology. aug: au: Guarnera, Alessia Napolitano, Antonio Liporace, Flavia Marconi, Fabio Rossi-Espagnet, Maria Camilla Gandolfo, Carlo Romano, Andrea Bozzao, Alessandro Longo, Daniela affil: Functional and Interventional Neuroradiology Unit, Bambino Gesù Children's Hospital, IRCCS (Istituto di Ricovero e Cura a Carattere Scientifico), 00165 Rome, Italy sug: subj: Artificial Intelligence Utilization Neuroradiography Pediatrics Digital Health Diagnostic Imaging Diagnosis, Computer Assisted Decision Support Systems, Clinical Technology, Medical Ethics, Medical Medical Informatics Focal Cortical Dysplasia Workflow Deep Learning Machine Learning Magnetic Resonance Imaging Forecasting Neoplasms Diagnosis Brain Injuries Brain Abnormalities Epilepsy Diagnosis ab: Artificial intelligence (AI) is revolutionarily shaping the entire landscape of medicine and particularly the privileged field of radiology, since it produces a significant amount of data, namely, images. Currently, AI implementation in radiology is continuously increasing, from automating image analysis to enhancing workflow management, and specifically, pediatric neuroradiology is emerging as an expanding frontier. Pediatric neuroradiology presents unique opportunities and challenges since neonates' and small children's brains are continuously developing, with age-specific changes in terms of anatomy, physiology, and disease presentation. By enhancing diagnostic accuracy, reducing reporting times, and enabling earlier intervention, AI has the potential to significantly impact clinical practice and patients' quality of life and outcomes. For instance, AI reduces MRI and CT scanner time by employing advanced deep learning (DL) algorithms to accelerate image acquisition through compressed sensing and undersampling, and to enhance image reconstruction by denoising and super-resolving low-quality datasets, thereby producing diagnostic-quality images with significantly fewer data points and in a shorter timeframe. Furthermore, as healthcare systems become increasingly burdened by rising demands and limited radiology workforce capacity, AI offers a practical solution to support clinical decision-making, particularly in institutions where pediatric neuroradiology is limited. For example, the MELD (Multicenter Epilepsy Lesion Detection) algorithm is specifically designed to help radiologists find focal cortical dysplasias (FCDs), which are a common cause of drug-resistant epilepsy. It works by analyzing a patient's MRI scan and comparing a wide range of features—such as cortical thickness and folding patterns—to a large database of scans from both healthy individuals and epilepsy patients. By identifying subtle deviations from normal brain anatomy, the MELD graph algorithm can highlight potential lesions that are often missed by the human eye, which is a critical step in identifying patients who could benefit from life-changing epilepsy surgery. On the other hand, the integration of AI into pediatric neuroradiology faces technical and ethical challenges, such as data scarcity and ethical and legal restrictions on pediatric data sharing, that complicate the development of robust and generalizable AI models. Moreover, many radiologists remain sceptical of AI's interpretability and reliability, and there are also important medico-legal questions around responsibility and liability when AI systems are involved in clinical decision-making. Future promising perspectives to overcome these concerns are represented by federated learning and collaborative research and AI development, which require technological innovation and multidisciplinary collaboration between neuroradiologists, data scientists, ethicists, and pediatricians. The paper aims to address: (1) current applications of AI in pediatric neuroradiology; (2) current challenges and ethical considerations related to AI implementation in pediatric neuroradiology; and (3) future opportunities in the clinical and educational pediatric neuroradiology field. AI in pediatric neuroradiology is not meant to replace neuroradiologists, but to amplify human intellect and extend our capacity to diagnose, prognosticate, and treat with unprecedented precision and speed. pubtype: Academic Journal doctype: diagnostic images review Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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