A Convolutional Neural Network Using Multimodal Retinal Imaging for Differentiation of Mild Cognitive Impairment from Normal Cognition

C Ellis Wisely; Alexander Richardson; Ricardo Henao; Cason B Robbins; Justin P Ma; Dong Wang; Kim G Johnson; Andy J Liu; Dilraj S Grewal; Sharon Fekrat

doi:10.1016/j.xops.2023.100355

A Convolutional Neural Network Using Multimodal Retinal Imaging for Differentiation of Mild Cognitive Impairment from Normal Cognition

Ophthalmol Sci. 2023 Jun 25;4(1):100355. doi: 10.1016/j.xops.2023.100355. eCollection 2024 Jan-Feb.

Authors

C Ellis Wisely^{1

2}, Alexander Richardson^{1

2

3

4}, Ricardo Henao^{2

3

4}, Cason B Robbins^{1

2}, Justin P Ma^{1

2}, Dong Wang^{2

3

4}, Kim G Johnson⁵, Andy J Liu⁵, Dilraj S Grewal^{1

2}, Sharon Fekrat^{1

2

5}

Affiliations

¹ Department of Ophthalmology, Duke University School of Medicine, Durham, North Carolina.
² iMIND Study Group, Duke University School of Medicine, Durham, North Carolina.
³ Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina.
⁴ Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina.
⁵ Department of Neurology, Duke University School of Medicine, Durham, North Carolina.

Abstract

Purpose: To develop a machine learning tool capable of differentiating eyes of subjects with normal cognition from those with mild cognitive impairment (MCI) using OCT and OCT angiography (OCTA).

Design: Evaluation of a diagnostic technology.

Participants: Subjects with normal cognition were compared to subjects with MCI.

Methods: A multimodal convolutional neural network (CNN) was built to predict likelihood of MCI from ganglion cell-inner plexiform layer (GC-IPL) thickness maps, OCTA images, and quantitative data including patient characteristics.

Main outcome measures: Area under the receiver operating characteristic curve (AUC) and summaries of the confusion matrix (sensitivity and specificity) were used as performance metrics for the prediction outputs of the CNN.

Results: Images from 236 eyes of 129 cognitively normal subjects and 154 eyes of 80 MCI subjects were used for training, validating, and testing the CNN. When applied to the independent test set using inputs including GC-IPL thickness maps, OCTA images, and quantitative OCT and OCTA data, the AUC value for the CNN was 0.809 (95% confidence interval [CI]: 0.681-0.937). This model achieved a sensitivity of 79% and specificity of 83%. The AUC value for GC-IPL thickness maps alone was 0.681 (95% CI: 0.529-0.832), for OCTA images alone was 0.625 (95% CI: 0.466-0.784) and for both GC-IPL maps and OCTA images was 0.693 (95% CI: 0.543-0.843). Models using quantitative data alone were also tested, with a model using quantitative data derived from images, 0.960 (95% CI: 0.902-1.00), outperforming a model using demographic data alone, 0.580 (95% CI: 0.417-0.742).

Conclusions: This novel CNN was able to identify an MCI diagnosis using an independent test set comprised of OCT and OCTA images and quantitative data. The GC-IPL thickness maps provided more useful decision support than the OCTA images. The addition of quantitative data inputs also provided significant decision support to the CNN to identify individuals with MCI. Quantitative imaging metrics provided superior decision support than demographic data.

Financial disclosures: Proprietary or commercial disclosure may be found after the references.

Keywords: Convolutional neural network; Machine learning; Mild cognitive impairment; OCT; OCT angiography.