Interpretable convolutional neural network for autism diagnosis support in children using structural magnetic resonance imaging datasets
We show the potential of interpretable DL models trained on sMRI data to aid in autism diagnosis within a narrowly defined pediatric age group. Our findings contribute to the field of explainable artificial intelligence methods in neurodevelopmental research and may help in clinical decision-making for autism and other neurodevelopmental conditions.
Authors
- Raissa Souza (ORCID: https://orcid.org/0000-0002-7455-3383)
- Emma A. M. Stanley (ORCID: https://orcid.org/0000-0002-7802-6820)
- Nils D. Forkert (ORCID: https://orcid.org/0000-0003-2556-3224)
- Matthias Wilms (ORCID: https://orcid.org/0000-0001-8845-360X)
- Myka L. Estes (ORCID: https://orcid.org/0000-0003-4279-7794)
- Sarah J. MacEachern (ORCID: https://orcid.org/0000-0001-8473-5650)
- Anthony Winder (ORCID: https://orcid.org/0000-0002-9650-5526)
- G. Martinez
Institutions
- University of Calgary (CA)
- Alberta Children's Hospital (CA)
- Hotchkiss Brain Institute (CA)
Publication Details
- Journal
- Journal of Medical Imaging
- Published
- 2025-11-08
- DOI
- https://doi.org/10.1117/1.jmi.12.6.064501
- Primary Topic
- Autism Spectrum Disorder Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00