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Deep Learning Models for the Screening of Cognitive Impairment Using Multimodal Fundus Images

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收录情况： ◇ ESCI

作者：
Shi Xu Han[1,2,3] * ; Ju Lie[4,5,6] * ; Dong Li[1,2,3] * ; Zhang Rui Heng[1,2,3] ; Shao Lei[1,2,3] ; Yan Yan Ni[1,2,3] ; Wang Ya Xing[7] ; Fu Xue Fei[4] ; Chen Yu Zhong[4] ; Ge Zong Yuan[4,5,6] ; Wei Wen Bin[1,2,3,*1] ;
机构： [1]Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Tongren Hospital, Capital Medical University, Beijing, China [2]Beijing Ophthalmology & Visual Sciences Key Lab, Beijing Tongren Hospital, Capital Medical University, Beijing, China [3]Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China. [4]Beijing Airdoc Technology Co., Ltd., Beijing, China [5]AIM Lab, Faculty of IT, Monash University, Clayton, VIC, Australia [6]Faculty of Engineering, Monash University, Clayton, VIC, Australia. [7]Beijing Ophthalmology and Visual Science Key Laboratory, Beijing Tongren Eye Center, Beijing Tongren Hospital, Beijing Institute of Ophthalmology, Capital Medical University, Beijing, China.
出处：
DOI： 10.1016/j.oret.2024.01.019
ISSN：

关键词： Cognitive impairment deep learning artificial intelligence fundus photograph optical coherence tomography

摘要：
We aimed to develop a deep learning system capable of identifying subjects with cognitive impairment quickly and easily based on multimodal ocular images.Cross-sectional study SUBJECTS: Participants of Beijing Eye Study 2011 and patients attending Beijing Tongren Eye Center and Beijing Tongren Hospital Physical Examination Center.We trained and validated a deep learning algorithm to assess cognitive impairment using retrospectively collected data from the Beijing Eye Study 2011. Cognitive impairment was defined as a Mini-Mental State Examination (MMSE) score <24. Based on fundus photographs and optical coherence tomography (OCT) images, we developed five models based on the following sets of images: macula-centered fundus photographs, optic disc-centered fundus photographs, fundus photographs of both fields, optical coherence tomography (OCT) images, and fundus photographs of both fields with OCT (multi-modal). The performance of the models was evaluated and compared in an external validation dataset, which was collected from patients attending Beijing Tongren Eye Center and Beijing Tongren Hospital Physical Examination Center.Area under the curve (AUC).A total of 9,424 retinal photographs and 4,712 OCT images were used to develop the model. The external validation sets from each center included 1,180 fundus photographs and 590 OCT images. Model comparison revealed that the multi-modal performed best, achieving an AUC of 0.820 in the internal validation set, 0.786 in external validation set 1 and 0.784 in external validation set 2. We evaluated the performance of the multi-model in different sexes and different age groups; there were no significant differences. The heatmap analysis showed that signals around the optic disc in fundus photographs and the retina and choroid around the macular and optic disc regions in OCT images were used by the multi-modal to identify participants with cognitive impairment.Fundus photographs and OCT can provide valuable information on cognitive function. Multi-modal models provide richer information compared to single-mode models. Deep learning algorithms based on multimodal retinal images may be capable of screening cognitive impairment. This technique has potential value for broader implementation in community-based screening or clinic settings.Copyright © 2024. Published by Elsevier Inc.

基金：
This study was supported by National Natural Science Foundation of China (82220108017, 82141128), the Capital Health Research and Development of Special (2020-1-2052) and Science & Technology Project of Beijing Municipal Science & Technology Commission (Z201100005520045, Z181100001818003).
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外文
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出版当年[2022]版：
无
最新[2023]版：
Q1 OPHTHALMOLOGY

影响因子： 最新[2023版] 最新五年平均 出版当年[2022版] 出版当年五年平均 出版前一年[2021版] 出版后一年[2023版]

第一作者：
第一作者机构： [1]Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Tongren Hospital, Capital Medical University, Beijing, China [2]Beijing Ophthalmology & Visual Sciences Key Lab, Beijing Tongren Hospital, Capital Medical University, Beijing, China [3]Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
共同第一作者：
通讯作者：
通讯机构： [1]Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Tongren Hospital, Capital Medical University, Beijing, China [2]Beijing Ophthalmology & Visual Sciences Key Lab, Beijing Tongren Hospital, Capital Medical University, Beijing, China [3]Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China. [*1]1 Dong Jiao Min Lane, Beijing, China 100730.
推荐引用方式(GB/T 7714)：
Shi Xu Han,Ju Lie,Dong Li,et al.Deep Learning Models for the Screening of Cognitive Impairment Using Multimodal Fundus Images[J].OPHTHALMOLOGY RETINA.2024,8(7):666-677.doi:10.1016/j.oret.2024.01.019.
APA：
Shi Xu Han,Ju Lie,Dong Li,Zhang Rui Heng,Shao Lei...&Wei Wen Bin.(2024).Deep Learning Models for the Screening of Cognitive Impairment Using Multimodal Fundus Images.OPHTHALMOLOGY RETINA,8,(7)
MLA：
Shi Xu Han,et al."Deep Learning Models for the Screening of Cognitive Impairment Using Multimodal Fundus Images".OPHTHALMOLOGY RETINA 8..7(2024):666-677

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