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Predicting the cognitive impairment with multimodal ophthalmic imaging and artificial neural network for community screening
  1. Zi Jin1,2,
  2. Xuhui Chen3,
  3. Chunxia Jiang4,
  4. Ximeng Feng1,2,5,6,
  5. Da Zou1,2,5,6,
  6. Yanye Lu5,6,
  7. Jinying Li4,
  8. Qiushi Ren1,2,5,6,
  9. Chuanqing Zhou7
  1. 1Department of Biomedical Engineering, Peking University Shenzhen Graduate School, Shenzhen, China
  2. 2Department of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
  3. 3Department of Neurology, Peking University Shenzhen Hospital, Shenzhen, China
  4. 4Department of Ophthalmology, Peking University Shenzhen Hospital, Shenzhen, China
  5. 5Department of Biomedical Engineering, Peking University, Beijing, China
  6. 6Institute of Medical Technology, Peking University Health Science Centre, Beijing, China
  7. 7College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, China
  1. Correspondence to Dr Chuanqing Zhou, College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, China; zhoucq{at}sumhs.edu.cn; Jinying Li, Department of Ophthalmology, Peking University Shenzhen Hospital, Shenzhen, China; ljyszbdyy{at}163.com

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.

  • Retina
  • Imaging
  • Public health

Data availability statement

Data are available on reasonable request. The datasets used and/or analysed in this study are available from the corresponding author on reasonable request.

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Data availability statement

Data are available on reasonable request. The datasets used and/or analysed in this study are available from the corresponding author on reasonable request.

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Footnotes

  • ZJ and XC are joint first authors.

  • Contributors CZ, JL, ZJ and XC conceived and designed the study. ZJ, CJ, DZ and XF captured, analysed and interpreted the data. ZJ and XC wrote the main manuscript text. CZ, JL, YL and QR reviewed the manuscript. All authors read and approved the final manuscript. ZJ and XC contributed equally to this work. CZ and JL are guarantors.

  • Funding The study was supported in part by research grants from National Natural Science Foundation of China (61875123, 82301270); Natural Science Foundation of Beijing (Z210008); Shenzhen Science and Technology Innovation Program (1210318663); National Biomedical Imaging Facility Grant; Shenzhen Nanshan Innovation and Business Development Grant; Guangdong Basic and Applied Basic Research Foundation (2021A1515110747); Medical and Science & Technology Project of Zhejiang Province (2024KY162).

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.