Predicting the severity of white matter lesions among patients with cerebrovascular risk factors based on retinal images and clinical laboratory data: a deep learning study

Liming Shu; Kaiyi Zhong; Nanya Chen; Wenxin Gu; Wenjing Shang; Jiahui Liang; Jiangtao Ren; Hua Hong

doi:10.3389/fneur.2023.1168836

Predicting the severity of white matter lesions among patients with cerebrovascular risk factors based on retinal images and clinical laboratory data: a deep learning study

Front Neurol. 2023 Jul 10:14:1168836. doi: 10.3389/fneur.2023.1168836. eCollection 2023.

Authors

Liming Shu^{1

2}, Kaiyi Zhong³, Nanya Chen⁴, Wenxin Gu⁴, Wenjing Shang³, Jiahui Liang^{5

6}, Jiangtao Ren⁴, Hua Hong³

Affiliations

¹ Department of Neurology, Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China.
² Department of Neurology, Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
³ Department of Neurology, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
⁴ School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China.
⁵ Department of Medical Imaging, Sun Yat-sen University Cancer Center, Guangzhou, China.
⁶ Guangdong Key Laboratory of Non-human Primate Research, Guangdong-Hongkong-Macau Institute of CNS Regeneration, Jinan University, Guangzhou, China.

Abstract

Background and purpose: As one common feature of cerebral small vascular disease (cSVD), white matter lesions (WMLs) could lead to reduction in brain function. Using a convenient, cheap, and non-intrusive method to detect WMLs could substantially benefit to patient management in the community screening, especially in the settings of availability or contraindication of magnetic resonance imaging (MRI). Therefore, this study aimed to develop a useful model to incorporate clinical laboratory data and retinal images using deep learning models to predict the severity of WMLs.

Methods: Two hundred fifty-nine patients with any kind of neurological diseases were enrolled in our study. Demographic data, retinal images, MRI, and laboratory data were collected for the patients. The patients were assigned to the absent/mild and moderate-severe WMLs groups according to Fazekas scoring system. Retinal images were acquired by fundus photography. A ResNet deep learning framework was used to analyze the retinal images. A clinical-laboratory signature was generated from laboratory data. Two prediction models, a combined model including demographic data, the clinical-laboratory signature, and the retinal images and a clinical model including only demographic data and the clinical-laboratory signature, were developed to predict the severity of WMLs.

Results: Approximately one-quarter of the patients (25.6%) had moderate-severe WMLs. The left and right retinal images predicted moderate-severe WMLs with area under the curves (AUCs) of 0.73 and 0.94. The clinical-laboratory signature predicted moderate-severe WMLs with an AUC of 0.73. The combined model showed good performance in predicting moderate-severe WMLs with an AUC of 0.95, while the clinical model predicted moderate-severe WMLs with an AUC of 0.78.

Conclusion: Combined with retinal images from conventional fundus photography and clinical laboratory data are reliable and convenient approach to predict the severity of WMLs and are helpful for the management and follow-up of WMLs patients.

Keywords: cerebrovascular disease; deep learning; prediction model; retinal images; white matter lesions.