Predicting the cognitive impairment with multimodal ophthalmic imaging and artificial neural network for community screening

Zi Jin; Xuhui Chen; Chunxia Jiang; Ximeng Feng; Da Zou; Yanye Lu; Jinying Li; Qiushi Ren; Chuanqing Zhou

doi:10.1136/bjo-2023-323283

Predicting the cognitive impairment with multimodal ophthalmic imaging and artificial neural network for community screening

Br J Ophthalmol. 2024 May 2:bjo-2023-323283. doi: 10.1136/bjo-2023-323283. Online ahead of print.

Authors

Zi Jin^{1

2}, Xuhui Chen³, Chunxia Jiang⁴, Ximeng Feng^{1

2

5

6}, Da Zou^{1

2

5

6}, Yanye Lu^{5

6}, Jinying Li⁷, Qiushi Ren^{1

2

5

6}, Chuanqing Zhou⁸

Affiliations

¹ Department of Biomedical Engineering, Peking University Shenzhen Graduate School, Shenzhen, China.
² Department of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China.
³ Department of Neurology, Peking University Shenzhen Hospital, Shenzhen, China.
⁴ Department of Ophthalmology, Peking University Shenzhen Hospital, Shenzhen, China.
⁵ Department of Biomedical Engineering, Peking University, Beijing, China.
⁶ Institute of Medical Technology, Peking University Health Science Centre, Beijing, China.
⁷ Department of Ophthalmology, Peking University Shenzhen Hospital, Shenzhen, China zhoucq@sumhs.edu.cn ljyszbdyy@163.com.
⁸ College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, China zhoucq@sumhs.edu.cn ljyszbdyy@163.com.

PMID: 38697799
DOI: 10.1136/bjo-2023-323283

Abstract

Background/aims: To investigate the comprehensive prediction ability for cognitive impairment in a general elder population using the combination of the multimodal ophthalmic imaging and artificial neural networks.

Methods: Patients with cognitive impairment and cognitively healthy individuals were recruited. All subjects underwent medical history, blood pressure measurement, the Montreal Cognitive Assessment, medical optometry, intraocular pressure and custom-built multimodal ophthalmic imaging, which integrated pupillary light reaction, multispectral imaging, laser speckle contrast imaging and retinal oximetry. Multidimensional parameters were analysed by Student's t-test. Logistic regression analysis and back-propagation neural network (BPNN) were used to identify the predictive capability for cognitive impairment.

Results: This study included 104 cognitive impairment patients (61.5% female; mean (SD) age, 68.3 (9.4) years), and 94 cognitively healthy age-matched and sex-matched subjects (56.4% female; mean (SD) age, 65.9 (7.6) years). The variation of most parameters including decreased pupil constriction amplitude (CA), relative CA, average constriction velocity, venous diameter, venous blood flow and increased centred retinal reflectance in 548 nm (RC₅₄₈) in cognitive impairment was consistent with previous studies while the reduced flow acceleration index and oxygen metabolism were reported for the first time. Compared with the logistic regression model, BPNN had better predictive performance (accuracy: 0.91 vs 0.69; sensitivity: 93.3% vs 61.70%; specificity: 90.0% vs 68.66%).

Conclusions: This study demonstrates retinal spectral signature alteration, neurodegeneration and angiopathy occur concurrently in cognitive impairment. The combination of multimodal ophthalmic imaging and BPNN can be a useful tool for predicting cognitive impairment with high performance for community screening.

Keywords: Imaging; Public health; Retina.