Neuroimage Reports · Published 2026-06-23 · DOI 10.1016/j.ynirp.2026.100375
Naoki Takahashi, Yoshihiro Sato, Mitsuhiro Kato, Atsuko Yamaguchi
Background: This paper reports a genetic identification task using 3D convolutional neural network (3D-CNN) models applied to a proprietary 3D magnetic resonance imaging (MRI) dataset of patients with lissencephaly. Lissencephaly is a neuronal migration disorder caused by genetic mutations or deletions in which specific causative genes result in distinct morphological alterations in brain structure. Objective: The objective of this study was to identify causative genes through image classification by analysing three-dimensional structural features of brain MRI using deep learning. Methods: In our experiments, we extended representative CNN architectures to handle three-dimensional inputs and performed three-class classification targeting the primary causative genes, LIS1 and DCX, along with a category for other genetic variations. Results: Our results demonstrated that the 3D-ResNet18 model achieved a mean classification accuracy of over 78%. Furthermore, to enhance the precision for primary genes, we introduced a decision-making process based on prediction probability thresholds. Conclusions: This approach yielded an average precision improvement of 4.67% for DCX and 4.84% for LIS1 across all the evaluated models.
Abstract from DOAJ. Public domain (CC0 1.0).
Read the article at the publisher →
Takahashi, N., Sato, Y., Kato, M., et al. (2026). Deep learning-based identification of causative genes in lissencephaly using 3D-MRI volumetric datasets. Neuroimage Reports. https://doi.org/10.1016/j.ynirp.2026.100375