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

编号：

0BFC5FDC18089302FAA7A8F3FEB7FA2C
作者：

Jin, Zi#^[1,2]Chen, Xuhui(陈旭辉)#^[3]Jiang, Chunxia^[4];Feng, Ximeng^[1,2,5,6];Zou, Da^[1,2,5,6];Lu, Yanye(卢闫晔)^[5,6]Li, Jinying(李金瑛)*^[4]Ren, Qiushi(任秋实)^[1,2,5,6]Zhou, Chuanqing(周传清)*^[7]
语种：

英文
期刊：

BRITISH JOURNAL OF OPHTHALMOLOGY ISSN：0007-1161 2024 年 108 卷 12 期 (1737 - 1742) ; DEC
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关键词：

Retina Imaging Public health
摘要：

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.
推荐引用方式
GB/T 7714：

Jin Zi,Chen Xuhui,Jiang Chunxia, et al. Predicting the cognitive impairment with multimodal ophthalmic imaging and artificial neural network for community screening [J].BRITISH JOURNAL OF OPHTHALMOLOGY,2024,108(12):1737-1742.
APA：

Jin Zi,Chen Xuhui,Jiang Chunxia,Feng Ximeng,&Zhou Chuanqing.(2024).Predicting the cognitive impairment with multimodal ophthalmic imaging and artificial neural network for community screening .BRITISH JOURNAL OF OPHTHALMOLOGY,108(12):1737-1742.
MLA：

Jin Zi, et al. "Predicting the cognitive impairment with multimodal ophthalmic imaging and artificial neural network for community screening" .BRITISH JOURNAL OF OPHTHALMOLOGY 108,12(2024):1737-1742.
入库时间：

6/2/2024 6:52:52 PM
更新时间：

4/8/2026 9:20:48 PM
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