Diagnostic performance evaluation of adult Chiari malformation type I based on convolutional neural networks. Issue 151 (June 2022)

• Record Type: Journal Article
• Authors: Lin, Wei-Wei; Liu, Tian-Jian; Dai, Wen-Li; Wang, Qiang-Wei; Hu, Xin-Ben; Gu, Zhao-Wen; Zhu, Yong-Jian
• Is Part Of: European journal of radiology. Issue 151(2022)
• Publication Date: 2022-06
• Subjects: CMI/Chiari malformation type I -- CNN/convolutional neural networks -- Adult -- Diagnostic model; CMI Chiari malformation type I -- CNN Convolutional neural network -- CVJ Craniovertebral junction -- CSF Cerebrospinal fluid; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2022.110287
• Languages: English
• ISSNs: 0720-048X

Abstract

Abstract: Purpose: This study aimed to evaluate the diagnostic performance of convolutional neural network (CNN) models in Chiari malformation type I (CMI) and to verify whether CNNs can identify the morphological features of the craniocervical junction region between patients with CMI and healthy controls (HCs). To date, numerous indicators based on manual measurements are used for the diagnosis of CMI. However, the corresponding postoperative efficacy and prognostic evaluations have remained inconsistent. From a diagnostic perspective, CNN models may be used to explore the relationship between the clinical features and image morphological parameters. Methods: This study included a total of 148 patients diagnosed with CMI at our institution and 205 HCs were included. T1-weighted sagittal magnetic resonance imaging (MRI) images were used for the analysis. A total of 220 and 355 slices were acquired from 98 patients with CMI and 155 HCs, respectively, to train and validate the CNN models. In addition, median sagittal images obtained from 50 patients with CMI and 50 HCs were selected to test the models. We applied original cervical MRI images (CI) and images of posterior cranial fossa and craniocervical junction area (CVI) to train the CI- and CVI-based CNN models. Transfer learning and data augmentation were used for model construction and each model was retrained 10 times. Results: Both the CI- and CVI-based CNN models achieved high diagnostic accuracy. In the validation dataset, the models had diagnostic accuracy of 100% and 97% ( p = 0.005), sensitivity of 100% and 98% ( p = 0.016), and specificity of 100% ( p = 0.929), respectively. In the test dataset, the accuracy was 97% and 96% ( p = 0.25), sensitivity was 97% and 92% ( p = 0.109), and specificity was 100% ( p = 0.123), respectively. For patients with cerebellar subungual herniation less than 5 mm, three out of the 10 CVI-based retrained models reached 100% sensitivity. Conclusions: Our results revealed that the CNN models demonstrated excellent diagnostic performance for CMI. The models had higher sensitivity than the application of cerebellar tonsillar herniation alone and could identify features in the posterior cranial fossa and craniocervical junction area of patients. Our preliminary experiments provided a feasible method for the diagnosis and study of CMI using CNN models. However, further studies are needed to identify the morphologic characteristics of patients with different clinical outcomes, as well as patients who may benefit from surgery.