Deep learning-based identification of causative genes in lissencephaly using 3D-MRI volumetric datasets.

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.