Article Information
PubMed
Published By
Print ISSN
Online ISSN
History
- Received August 12, 2015
- Accepted after revision November 19, 2015
- Published online June 13, 2016.
Article Versions
- Latest version (February 18, 2016 - 07:06).
- You are viewing the most recent version of this article.
Copyright & Usage
© 2016 by American Journal of Neuroradiology Indicates open access to non-subscribers at www.ajnr.org
Author Information
- S. Mikia,
- N. Hayashia,
- Y. Masutanic,
- Y. Nomuraa,
- T. Yoshikawaa,
- S. Hanaokab,
- M. Nemotoa and
- K. Ohtomob
- aFrom the Departments of Computational Diagnostic Radiology and Preventive Medicine (S.M., N.H., Y.N., T.Y., M.N.)
- bRadiology (S.H., K.O.), The University of Tokyo Hospital, Tokyo, Japan
- cFaculty of Information Sciences and Graduate School of Information Sciences (Y.M.), Hiroshima City University, Hiroshima, Japan.
- Please address correspondence to Soichiro Miki, MD, PhD, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan; e-mail: smiki-tky{at}umin.ac.jp
Altmetrics
Cited By...
Jump to section
Related Articles
Cited By...
- Automation Bias in AI-Assisted Detection of Cerebral Aneurysms on Time-of-Flight MR-Angiography
- Detection of cerebral aneurysms using artificial intelligence: a systematic review and meta-analysis
- Deep Learning-Based Software Improves Clinicians' Detection Sensitivity of Aneurysms on Brain TOF-MRA
- Detection of clustered anomalies in single-voxel morphometry as a rapid automated method for identifying intracranial aneurysms
- Artificial Intelligence in the Management of Intracranial Aneurysms: Current Status and Future Perspectives
- Deep Learning-Based Detection of Intracranial Aneurysms in 3D TOF-MRA
- Regarding "Computer-Assisted Detection of Cerebral Aneurysms in MR Angiography in a Routine Image-Reading Environment: Effects on Diagnosis by Radiologists"
- Reply: