Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
562-04-004 ISMRM Abstract

Neonatal Brain-Age Prediction and Interpretability Analysis Using CNN- and ViT-Based Models

Primary: Pediatrics - Neonatal
Secondary: Pediatrics - Neuro
562-04-004 · AI/DL in Pediatrics · Wednesday, 13 May, 2:35 PM–3:30 PM · Digital Posters Row C
Keywords: Pediatric Brain age prediction Model interpretability Neonatal brain MRI
Accepted
Dayeon Bak 1, Junghwa Kang1, Hyun Gi Kim2,3, Yoonho Nam1
1Department of Biomedical Engineering, Hankuk university of Foreign Studies, gyeonggi-do, Korea, Republic of
2Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea, Republic of
3Department of Radiology, Eunpyeong St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea, Republic of
Presenting Author: Dayeon Bak

Synopsis

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References

1. Ji L, Menu I, Majbri A, Bhatia T, Trentacosta CJ, Thomason ME. Trajectories of human brain functional connectome maturation across the birth transition. Kennedy H, editor. PLOS Biol. 2024 Nov 19;22(11):e3002909.
2. Miller SP, Ferriero DM, Leonard C, Piecuch R, Glidden DV, Partridge JC, et al. Early Brain Injury in Premature Newborns Detected with Magnetic Resonance Imaging is Associated with Adverse Early Neurodevelopmental Outcome. J Pediatr. 2005 Nov;147(5):609–16.
3. Chefer H, Gur S, Wolf L. Transformer Interpretability Beyond Attention Visualization. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) [Internet]. Nashville, TN, USA: IEEE; 2021 [cited 2025 Sept 5]. p. 782–91. Available from: https://ieeexplore.ieee.org/document/9577970/
4. Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-CAM: Visual Explanations From Deep Networks via Gradient-Based Localization.
5. Yun HJ, Lee HJ, You S, Lee JY, Aguirre-Chavez J, Vasung L, et al. Deep Learning–based Brain Age Prediction Using MRI to Identify Fetuses with Cerebral Ventriculomegaly. Radiol Artif Intell. 2025 Mar 1;7(2):e240115.
6. Sun H, Mehta S, Khaitova M, Cheng B, Hao X, Spann M, et al. Brain age prediction and deviations from normative trajectories in the neonatal connectome. Nat Commun. 2024 Nov 26;15(1):10251.
7. Peng H, Gong W, Beckmann CF, Vedaldi A, Smith SM. Accurate brain age prediction with lightweight deep neural networks. Med Image Anal. 2021 Feb;68:101871.
8. Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, et al. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale [Internet]. arXiv; 2021 [cited 2025 Oct 30]. Available from: http://arxiv.org/abs/2010.11929

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/562-04-004