Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
562-04-007 ISMRM Abstract

Investigating Brain Regional Significance in Explainable AI for Pediatric MRI

Primary: Pediatrics - Prenatal
Secondary: Analysis Methods - Classification and Prediction
562-04-007 · AI/DL in Pediatrics · Wednesday, 13 May, 2:35 PM–3:30 PM · Digital Posters Row C
Keywords: Explainable AI Pediatric brain MRI Statistical analysis Prenatal alcohol exposure
Accepted
Anik Das1, Kaue Duarte2, Catherine Lebel2,3, Mariana Bento1,3,4
1Biomedical Engineering, University of Calgary, Calgary, Canada
2Department of Radiology, University of Calgary, Calgary, Canada
3Alberta Children's Hospital Research Insitute, Calgary, Canada
4Electrical and Software Engineering, University of Calgary, Calgary, Canada
Presenting Author: Arshin Soltan Bayazidi

Synopsis

Motivation:
Goals:
Approach:
Results:
Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

Log in

References

1. Samek W, Wiegand T, Müller KR. Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. arXiv preprint arXiv:1708.08296. 2017 Aug 28. https://doi.org/10.48550/arXiv.1708.08296 [doi]
2. Larkby C, Day N. The effects of prenatal alcohol exposure. Alcohol health and research world. 1997;21(3):192. PMID: 15706768 [pmid]
3. Das A, Duarte K, Lebel C, Bento M. Deep learning for detecting prenatal alcohol exposure in pediatric brain MRI: a transfer learning approach with explainability insights. Frontiers in Computational Neuroscience. 2024 Aug 26;18:1434421. https://doi.org/10.3389/fncom.2024.1434421 [doi]
4. Reynolds JE, Long X, Paniukov D, Bagshawe M, Lebel C. Calgary Preschool magnetic resonance imaging (MRI) dataset. Data in brief. 2020 Apr 1;29:105224. https://doi.org/10.1016/j.dib.2020.105224 [doi]
5. Fischl B. FreeSurfer. Neuroimage. 2012 Aug 15;62(2):774-81. https://doi.org/10.1016/j.neuroimage.2012.01.021 [doi]
6. Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM. Fsl. Neuroimage. 2012 Aug 15;62(2):782-90. https://doi.org/10.1016/j.neuroimage.2011.09.015 [doi]
7. Gong W, Beckmann CF, Vedaldi A, Smith SM, Peng H. Optimising a simple fully convolutional network for accurate brain age prediction in the PAC 2019 challenge. Frontiers in Psychiatry. 2021 May 10;12:627996. https://doi.org/10.3389/fpsyt.2021.627996 [doi]
8. Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-cam: Visual explanations from deep networks via gradient-based localization. InProceedings of the IEEE international conference on computer vision 2017 (pp. 618-626).
9. West RM. Best practice in statistics: Use the Welch t-test when testing the difference between two groups. Annals of clinical biochemistry. 2021 Jul;58(4):267-9. https://doi.org/10.1177/0004563221992088 [doi]
10. Mitoma H, Manto M, Shaikh AG. Alcohol Toxicity in the Developing Cerebellum. Diagnostics. 2024 Jul 2;14(13):1415. https://doi.org/10.3390/diagnostics14131415 [doi]
11. Leung EC, Jain P, Michealson MA, Choi H, Ellsworth-Kopkowski A, Valenzuela CF. Recent breakthroughs in understanding the cerebellum's role in fetal alcohol spectrum disorder: A systematic review. Alcohol. 2024 Sep 1;119:37-71. https://doi.org/10.1016/j.alcohol.2023.12.003 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/562-04-007