Use of deep learning in the MRI diagnosis of Chiari malformation type I.

ABSTRACT:

Purpose

To train deep learning convolutional neural network (CNN) models for classification of clinically significant Chiari malformation type I (CM1) on MRI to assist clinicians in diagnosis and decision making.

Methods

A retrospective MRI dataset of patients diagnosed with CM1 and healthy individuals with normal brain MRIs from the period January 2010 to May 2020 was used to train ResNet50 and VGG19 CNN models to automatically classify images as CM1 or normal. A total of 101 patients diagnosed with CM1 requiring surgery and 111 patients with normal brain MRIs were included (median age 30 with an interquartile range of 23-43; 81 women with CM1). Isotropic volume transformation, image cropping, skull stripping, and data augmentation were employed to optimize model accuracy.

SUBMITTER: Tanaka KW 

PROVIDER: S-EPMC9271110 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature