Siderakis M, Velonakis G, Arkoudis NA. Machine learning in neuroradiology: Recent developments and applications. World J Radiol 2026; 18(8): 123022 [DOI: 10.4329/wjr.123022]
Corresponding Author of This Article
Nikolaos-Achilleas Arkoudis, MD, PhD, Lecturer, Researcher, Research Unit of Radiology and Medical Imaging, National and Kapodistrian University of Athens, Papadiamantopoulou 19, Athens 115 28, Attikí, Greece. nick.arkoudis@gmail.com
Research Domain of This Article
Radiology, Nuclear Medicine & Medical Imaging
Article-Type of This Article
review-article
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Manos Siderakis, Department of Radiology, Bioiatriki Healthcare Group, Kifisia 115 26, Attikí, Greece
Georgios Velonakis, Nikolaos-Achilleas Arkoudis, Research Unit of Radiology and Medical Imaging, National and Kapodistrian University of Athens, Athens 115 28, Attikí, Greece
Georgios Velonakis, Nikolaos-Achilleas Arkoudis, 2nd Department of Radiology, Attikon University General Hospital, National and Kapodistrian University of Athens, Athens 124 62, Attikí, Greece
Author contributions: Siderakis M, Velonakis G, and Arkoudis NA contributed substantially to the conceptualization, design and preparation of the review and drafting of the manuscript; Velonakis G and Arkoudis NA provided supervision; and all authors reviewed and approved the final manuscript.
AI contribution statement: AI tools (GPT-5.6), were used solely for language polishing and formatting assistance. No AI tool was used to generate research data, interpret results, or formulate conclusions. All AI-assisted content was critically reviewed and revised by the authors, who take full responsibility for the accuracy, originality, and integrity of the manuscript.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Nikolaos-Achilleas Arkoudis, MD, PhD, Lecturer, Researcher, Research Unit of Radiology and Medical Imaging, National and Kapodistrian University of Athens, Papadiamantopoulou 19, Athens 115 28, Attikí, Greece. nick.arkoudis@gmail.com
Received: May 7, 2026 Revised: July 20, 2026 Accepted: July 30, 2026 Published online: August 28, 2026 Processing time: 114 Days and 3.5 Hours
Abstract
Radiology, particularly neuroradiology, has become a major focus of research and industrial investment in artificial intelligence and machine learning (ML). These technologies may help address increasing imaging volumes, workforce shortages, and the need for faster and more consistent interpretation. This article summarizes recent developments in ML applications across neuroradiology. In acute ischemic stroke, ML supports early lesion detection, automated Alberta Stroke Program Early Computed Tomography Score assessment, large-vessel-occlusion detection, infarct core and penumbra estimation, collateral evaluation, workflow prioritization, and outcome prediction. Further applications include cerebral aneurysm detection and prediction of intracerebral hemorrhage expansion and prognosis. In neuro-oncology, current uses include tumor segmentation and classification, molecular-marker prediction, treatment-response assessment, surgical and radiotherapy planning, differentiation of recurrence from pseudoprogression, and prognostication. Additional advances involve image reconstruction, automated quantification, diagnostic classification, and outcome prediction in spine imaging; lesion detection and segmentation in demyelinating disease; and identification and characterization of neurodegenerative disorders. Despite this progress, limited generalizability, insufficient external validation, and poor interpretability remain major barriers to clinical adoption. Explainable artificial intelligence, federated learning, and robust multicenter validation are likely to be central to future clinical implementation.
Core Tip: In this article, we focus on the most recent (post 2023) developments and applications of machine learning (ML) in neuroradiology. The ML models presented here attempt to assist radiologists in stroke imaging, cerebral aneurysm and hemorrhage detection, neuro-oncology, spine imaging, and demyelinating and degenerative diseases. Despite the potential of ML to become an essential player in modern neuroradiology departments, the complexity of ML, especially deep learning models, remains an obstacle to their broad clinical application. The framework of explainable artificial intelligence attempts to address this issue and increase transparency in the model structure and decision-making process.
