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Publications

Journal Article Ahead of print

PubMed: 42031665 DOI: 10.3174/ajnr.A9363

NeuroAIHub: An AI-Driven Framework for Automated Curation and Discovery of Neuroradiology Datasets.

AJNR Am J Neuroradiol

Authors: Benyamin Gheiji, Sina Moradi, Mahsa Vatanparast, Sana Shokrani, Mohammad Aref Bagherzadeh, Danial Elyassirad, Oluwatobi Iyanuoluwa Akinmuleya, Sina Goodarzi, Mahsa Heidari-Foroozan, Mana Moassefi, Jeffrey D Rudie, Evan Calabrese, R Jain, Shahriar Faghani

Affiliations

  • From the Mashhad University of Medical Sciences (B.G. M.V. D.E.), Mashhad, Iran; Zumud, Zumud inc. (S.M.), Twickenham, London, England, UK; Tehran University of Medical Sciences (S.S.), Tehran, Iran; Research Center for Noncommunicable Diseases (M.A.B.), Department of Immunology (M.A.B.), Jahrom University of Medical Sciences, Jahrom, Iran; Department of Medicine (O.I.A.), Shenyang Medical College, Liaoning, Shenyang, China; Lorestan University of Medical Sciences (S.G.), Khorramabad, Iran; Shahid Beheshti University of Medical Sciences (M.H.-F.), Tehran, Iran; Department of Radiology (J.D.R.), University of California San Diego, San Diego CA, USA; Department of Radiology (J.D.R.), Scripps Clinic Medical Group, San DIego CA, USA; Department of Radiology (E.C.), Duke University Medical Center, Durham, NC, USA; Department of Radiology and Neurosurgery (R.J.), NYU Grossman School of Medicine, New York, NY, USA; Radiology Informatics Lab (M. M.), Department of Radiology, Mayo Clinic, Rochester, MN, USA and Department of Radiology (S.F.), University of Pennsylvania, Philadelphia, PA, USA.
  • From the Mashhad University of Medical Sciences (B.G. M.V. D.E.), Mashhad, Iran; Zumud, Zumud inc. (S.M.), Twickenham, London, England, UK; Tehran University of Medical Sciences (S.S.), Tehran, Iran; Research Center for Noncommunicable Diseases (M.A.B.), Department of Immunology (M.A.B.), Jahrom University of Medical Sciences, Jahrom, Iran; Department of Medicine (O.I.A.), Shenyang Medical College, Liaoning, Shenyang, China; Lorestan University of Medical Sciences (S.G.), Khorramabad, Iran; Shahid Beheshti University of Medical Sciences (M.H.-F.), Tehran, Iran; Department of Radiology (J.D.R.), University of California San Diego, San Diego CA, USA; Department of Radiology (J.D.R.), Scripps Clinic Medical Group, San DIego CA, USA; Department of Radiology (E.C.), Duke University Medical Center, Durham, NC, USA; Department of Radiology and Neurosurgery (R.J.), NYU Grossman School of Medicine, New York, NY, USA; Radiology Informatics Lab (M. M.), Department of Radiology, Mayo Clinic, Rochester, MN, USA and Department of Radiology (S.F.), University of Pennsylvania, Philadelphia, PA, USA.

Abstract

Neuroradiology datasets hold significant potential for advancing neuroimaging research, yet identifying relevant and up-to-date resources remains challenging. NeuroAIHub is an artificial intelligence (AI)-driven framework designed to automate dataset discovery, improve accessibility, and support structured exploration of neuroradiology datasets. A foundational database was constructed through a multi-reviewer extraction process with standardized metadata harmonization. To maintain and expand the registry, an AI-based updating workflow performs monthly web searches, extracts structured metadata from heterogeneous sources using large language models (LLMs), and submits candidate entries for developer validation before integration. An LLM-powered conversational agent enables natural-language dataset retrieval, analytical queries, and visualizations, while a structured web interface supports reproducible filtering. NeuroAIHub currently hosts 180 datasets across six diagnostic domains and is available as a web application (https://neuroai.streamlit.app/) and open-source Python package (https://github.com/NeuroAIHub-Registry/NeuroAIHub; https://pypi.org/project/neuroaihub). The platform provides a continuously curated resource designed to improve reproducibility, transparency, and efficiency in neuroradiology dataset discovery.

MeSH

Language, Artificial Intelligence, Metadata, Radionuclide Imaging