Retinal Imaging Techniques Based on Machine Learning Models in Recognition and Prediction of Mild Cognitive Impairment

Abstract

Qian Zhang,1,2,* Jun Li,3,* Minjie Bian,1,2 Qin He,1,2 Yuxian Shen,1,2 Yue Lan,4 Dongfeng Huang1,2 1Department of Rehabilitation Medicine, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, People’s Republic of China; 2Guangdong Engineering and Technology Research Center for Rehabilitation Medicine and Translation, Guangzhou, People’s Republic of China; 3Department of Urology, Kidney and Urology Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, People’s Republic of China; 4Department of Rehabilitation Medicine, Guangzhou First People’s Hospital, Guangzhou Medical University, Guangzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yue LanDepartment of Rehabilitation Medicine, Guangzhou First People’s Hospital, Guangzhou Medical University, 151 Yanjiang Road, Guangzhou, 510030, People’s Republic of ChinaTel [phone]Email [email protected] HuangDepartment of Rehabilitation Medicine, The Seventh Affiliated Hospital, Sun Yat-sen University, 628 Zhenyuan Road, Shenzhen, 518107, People’s Republic of ChinaTel [phone]Fax [phone]Email [email protected] and Purpose: Mild Cognitive Impairment (MCI) is thought to be the signal of many progressive diseases but is easily ignored. Therefore, a simple and easy screening method for recognizing and predicting MCI is urgently needed. The study aimed to establish machine learning models of retinal vascular features to categorize and predict MCI.Patients and Methods: Subjects enrolled underwent cognitive function assessment and were divided into a normal group, an MCI group, and a dementia group, and fundus photography was performed. MATLAB 2019b was used for fundus image preprocessing and vascular segmentation. Via the Green channel, adaptive histogram equalization (AHE), image binarization, and median filtering, we obtained the original and segmentation retinal vessel images. Afterwards, the histogram of oriented gradient (HOG) was used for image feature extraction. Support vector machine (SVM) and extreme learning machine (ELM) were selected for training models in the fundus original images and fundus vascular segmentation images, respectively. Among the three cognitive groups, sensitivity, specificity, the receiver operating characteristic (ROC) curves, and the area under the curve (AUC) were used to evaluate and compare the predictive performance of the two models in the fundus original and vascular segmentation images, respectively.Results: A total of 86 eligible subjects were enrolled in the study. After a clinical cognitive assessment, the participants were divided into the normal group (N = 38), the MCI group (N = 26), and the dementia group (N = 22). A total of 332 qualified fundus images were adopted after screening. Comparing the models among the three groups showed that the SVM model had more advantages than the ELM model in the fundus original images and vascular segmentation images. Meanwhile, we found that the original images performed better than the segmentation images in the same prediction model. Among the three groups, the SVM model of the fundus original images had the best performance.Conclusion: The establishment of a predictive model based on vascular-related feature extraction from fundus images has high recognition and prediction abilities for cognitive function and can be used as a screening method for MCI.Clinical Trial Registration: ChiCTR.org.cn (ChiCTR1900027404), Registered on Nov 12, 2019.Keywords: retinal imaging techniques, mild cognitive impairment, machine learning, support vector machine, extreme learning machine

Keywords

• retinal imaging techniques
• mild cognitive impairment
• machine learning
• support vector machine
• extreme learning machine