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World J Clin Cases. Jun 6, 2023; 11(16): 3725-3735
Published online Jun 6, 2023. doi: 10.12998/wjcc.v11.i16.3725
Review of deep learning and artificial intelligence models in fetal brain magnetic resonance imaging
Farzan Vahedifard, Jubril O Adepoju, Mark Supanich, Hua Asher Ai, Xuchu Liu, Mehmet Kocak, Kranthi K Marathu, Sharon E Byrd
Farzan Vahedifard, Jubril O Adepoju, Xuchu Liu, Mehmet Kocak, Kranthi K Marathu, Sharon E Byrd, Department of Diagnostic Radiology and Nuclear Medicine, Rush Medical College, Chicago, IL 606012, United States
Mark Supanich, Hua Asher Ai, Division for Diagnostic Medical Physics, Department of Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, IL 606012, United States
Author contributions: Byrd SE, Vahedifard F, Supanich M, and Kocak M designed the research study; Vahedifard F, Marathu KK, and Adepoju JO performed the literature review; Liu X contributed analytic tools; Vahedifard F, Supanich M, and Ai HA wrote the manuscript; Byrd SE performed the funding support; All authors have read and approve the final manuscript.
Supported by Colonel Robert R McCormick Professorship of Diagnostic Imaging Fund at Rush University Medical Center (The Activity Number is 1233-161-84), No. 8410152-03.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (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: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Farzan Vahedifard, MD, Research Fellow, Department of Diagnostic Radiology and Nuclear Medicine, Rush Medical College, 1620 W Harrison St, Jelke Building, Unit 169, Chicago, IL 606012, United States. farzan_vahedifard@rush.edu
Received: January 4, 2023
Peer-review started: January 4, 2023
First decision: January 20, 2023
Revised: January 30, 2023
Accepted: May 6, 2023
Article in press: May 6, 2023
Published online: June 6, 2023
Core Tip

Core Tip: The manual detection and segmentation of fetal brain magnetic resonance imaging (MRI) may be time-consuming, and susceptible to interpreter experience. During the past decade, artificial intelligence (AI) algorithms, particularly deep learning, have made impressive progress in image recognition tasks. A machine learning approach may help detect these problems early and improve the diagnosis and follow-up process. This narrative review paper investigates the role of AI and machine learning methods in fetal brain MRI.