A preliminary exploration of MRI diagnostic model of perianal fistulizing Crohn′s disease based on deep convolutional neural networks
10.3760/cma.j.cn101480-20230207-00015
- VernacularTitle:基于深度卷积神经网络的克罗恩病肛瘘磁共振成像诊断模型初探
- Author:
Lanlan LI
1
;
Ke DENG
;
Heng ZHANG
;
Donglin REN
;
Wenru LI
Author Information
1. 福州大学物理与信息工程学院，福州 350108
- Publication Type:Journal Article
- Keywords:
Crohn′s disease;
Perianal fistulizing;
Magnetic resonance imaging;
Deep convolutional neural networks;
Artificial intelligence;
Deep learning
- From:
Chinese Journal of Inflammatory Bowel Diseases
2023;07(2):144-150
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To evaluate the efficacy of magnetic resonance imaging (MRI) diagnostic model of perianal fistulizing Crohn′s disease (pfCD) based on deep convolutional neural networks (DCNN) .Methods:A restrospective study was conducted. The patients with pfCD of initial diagnosis ( n = 200) and the patients with cryptoglandular anal fistula (CAF) of initial diagnosis ( n = 200) were enrolled randomly in the Sixth Affiliated Hospital of Sun Yat-sen University from January 2014 to December 2019. The patients were assigned to the training, validation and test sets at a ratio of 8∶1∶1 in each group. The anal MRI images of all the patients were collected and preprocessed to enhance the quality of images. Using the Pytorch deep learning framework and Windows 10 computer operating system, the MRI diagnostic model of pfCD and CAF was constructed based on 4 DCNNs (MobileNetV2, VGG11, ResNet18 and ResNet34) . Each model was divided into transfer learning (T) and untransfer learning (U) types based on whether it incorporated transfer learning strategy. First, the image data of training set (160 pfCD and 160 CAF patients, a total of 78 321 MRI images) was input, and the training was iterated to minimize the loss. Then the best training model was selected based on the results of the validation set (20 pfCD and 20 CAF patients, a total of 9697 MRI images) . Finally, diagnostic efficacy was evaluated on the test set (20 pfCD and 20 CAF patients, a total of 9260 MRI images) . The receiver operating characteristic (ROC) curve for each model was drawn and the area under the curve (AUC) was calculated. The DeLong test was used to compare the difference in AUCs among different models and between models and radiologists with different seniorities. Results:The efficacy of 4 models based on DCNN were MobileNetV2-T (AUC = 0.943, 95% CI: 0.820-0.991) , VGG11-T (AUC = 0.935, 95% CI: 0.810-0.988) , ResNet18-T (AUC = 0.920, 95% CI: 0.789-0.988) , ResNet34-T (AUC = 0.929, 95% CI: 0.801-0.986) , respectively. The AUCs of the 4 models combined with transfer learning strategy were higher than that of junior radiologist (all P<0.05) , and there was no significant difference in AUCs between 4 models with transfer learning strategy and senior radiologist (all P>0.05) . Conclusion:The construction of diagnostic model of pfCD is feasible by using deep learning technology based on DCNN, transfer learning strategy and high-resolution anal MRI images.
