Artificial Intelligence for Screening of Multiple Retinal and Optic Nerve Diseases

Dong, Li; He, Wanji; Zhang, Ruiheng; Ge, Zongyuan; Wang, Ya Xing; Zhou, Jinqiong; Xu, Jie; Shao, Lei; Wang, Qian; Yan, Yanni; Xie, Yao; Fang, Li-Jian; Wang, Haiwei; Wang, Yenan; Zhu, Xiaobo; Wang, Jin‐Yuan; Zhang, Chuan; Wang, Heng; Wang, Yining; Chen, Rongtian; Wan, Qianqian; Yang, Jingyan; Zhou, Wen-Da; Li, Heyan; Yao, Xuan; Yang, Zhiwen; Xiong, Jianhao; Wang, Xin; Huang, Yelin; Chen, Yuzhong; Wang, Zhaohui; Rong, Ce; Gao, Jianxiong; Zhang, Huiliang; Wu, Shouling; Jonas, Jost B.; Wei, Wen Bin

doi:10.1001/jamanetworkopen.2022.9960

Cited by 52 publications

(37 citation statements)

References 38 publications

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“…However, although many studies on the application of AI to the diagnosis of ocular surface diseases have exhibited satisfactory results, they still have numerous limitations and challenges. 1) Datasets suffer from image quality problems ( Ghosh et al, 2022 ; Dong et al, 2022 ). Some of the images in the training, verification, and test sets used in some AI studies suffered from quality problems, such as unclear or incomplete images, which significantly impacted the research results.…”

Section: Limitations and Challengesmentioning

confidence: 99%

Advances in artificial intelligence applications for ocular surface diseases diagnosis

Hong

et al. 2022

Front. Cell Dev. Biol.

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In recent years, with the rapid development of computer technology, continual optimization of various learning algorithms and architectures, and establishment of numerous large databases, artificial intelligence (AI) has been unprecedentedly developed and applied in the field of ophthalmology. In the past, ophthalmological AI research mainly focused on posterior segment diseases, such as diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, retinal vein occlusion, and glaucoma optic neuropathy. Meanwhile, an increasing number of studies have employed AI to diagnose ocular surface diseases. In this review, we summarize the research progress of AI in the diagnosis of several ocular surface diseases, namely keratitis, keratoconus, dry eye, and pterygium. We discuss the limitations and challenges of AI in the diagnosis of ocular surface diseases, as well as prospects for the future.

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Section: Limitations and Challengesmentioning

confidence: 99%

Advances in artificial intelligence applications for ocular surface diseases diagnosis

Hong

et al. 2022

Front. Cell Dev. Biol.

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“…The figure shows the true label and the predicted mislabel recognition models for individual common fundus diseases [22,23]. In recent years, several teams have demonstrated good performance with multiclassification task models derived from CFP [4][5][6]24]. Real-world applications of these AI platforms with broader clinical applicability will greatly enhance the early screening and diagnosis of fundus diseases.…”

Section: Human Doctors Versus Deep-learning Modelsmentioning

confidence: 99%

“…In recent years, deep learning has been gradually applied to screen, diagnose, classify, and guide the treatment of retinal diseases, which can greatly reduce the human and material resources required for conventional screening modalities [2,3]. Artificial intelligence (AI) models for multidisease classification based on conventional retinal fundus images have gradually matured [4][5][6]. However, color fundus photography (CFP) has a small imaging range and a time-consuming pupil dilation preparation.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for the Detection of Multiple Fundus Diseases Using Ultra-widefield Images

Sun

Wang

Xu³

et al. 2022

Ophthalmol Ther

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Introduction:To design and evaluate a deep learning model based on ultra-widefield images (UWFIs) that can detect several common fundus diseases. Methods: Based on 4574 UWFIs, a deep learning model was trained and validated that can identify normal fundus and eight common fundus diseases, namely referable diabetic retinopathy, retinal vein occlusion, pathologic myopia, retinal detachment, retinitis pigmentosa, age-related macular degeneration, vitreous opacity, and optic neuropathy. The model was tested on three test sets with data volumes of 465, 979, and 525. The performance of the three deep learning networks, EfficientNet-B7, Den-seNet, and ResNet-101, was evaluated on the internal test set. Additionally, we compared the performance of the deep learning model with that of doctors in a tertiary referral hospital. Results: Compared to the other two deep learning models, EfficientNet-B7 achieved the best performance. The area under the receiver operating characteristic curves of the Effi-cientNet-B7 model on the internal test set, external test set A and external test set B were 0.9708 (0.8772, 0.9849) to 1.0000 (1.0000, 1.0000), 0.9683 (0.8829, 0.9770) to 1.0000 (0.9975, 1.0000), and 0.8919 (0.7150, 0.9055) to 0.9977 (0.9165, 1.0000), respectively. On a data set of 100 images, the total accuracy of the deep learning model was 93.00%, the average Gongpeng Sun and Xiaoling Wang contributed equally.

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“…Alterations in key retinal features have already been associated with numerous prevalent disease processes [ 27 ]. Retinal microvascular changes have been linked to coronary heart disease, hypertension, kidney disease, and stroke [ 28 , 29 , 30 , 31 , 32 , 33 , 34 ]. In addition, as the retina itself is an extension of the central nervous system, retinal nerve fiber layer thickness and retinal vessel morphology changes have been found to be predictive of dementia and neurodegenerative illnesses like Parkinson’s and Alzheimer’s disease [ 35 , 36 , 37 , 38 , 39 , 40 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting Systemic Health Features from Retinal Fundus Images Using Transfer-Learning-Based Artificial Intelligence Models

et al. 2022

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While color fundus photos are used in routine clinical practice to diagnose ophthalmic conditions, evidence suggests that ocular imaging contains valuable information regarding the systemic health features of patients. These features can be identified through computer vision techniques including deep learning (DL) artificial intelligence (AI) models. We aim to construct a DL model that can predict systemic features from fundus images and to determine the optimal method of model construction for this task. Data were collected from a cohort of patients undergoing diabetic retinopathy screening between March 2020 and March 2021. Two models were created for each of 12 systemic health features based on the DenseNet201 architecture: one utilizing transfer learning with images from ImageNet and another from 35,126 fundus images. Here, 1277 fundus images were used to train the AI models. Area under the receiver operating characteristics curve (AUROC) scores were used to compare the model performance. Models utilizing the ImageNet transfer learning data were superior to those using retinal images for transfer learning (mean AUROC 0.78 vs. 0.65, p-value < 0.001). Models using ImageNet pretraining were able to predict systemic features including ethnicity (AUROC 0.93), age > 70 (AUROC 0.90), gender (AUROC 0.85), ACE inhibitor (AUROC 0.82), and ARB medication use (AUROC 0.78). We conclude that fundus images contain valuable information about the systemic characteristics of a patient. To optimize DL model performance, we recommend that even domain specific models consider using transfer learning from more generalized image sets to improve accuracy.

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Artificial Intelligence for Screening of Multiple Retinal and Optic Nerve Diseases

Abstract: This diagnostic study develops and prospectively validates a deep learning algorithm that uses ocular fundus images to recognize numerous retinal diseases in a clinical setting at 65 screening centers in 19 Chinese provinces.

Cited by 52 publications

References 38 publications

Advances in artificial intelligence applications for ocular surface diseases diagnosis

Advances in artificial intelligence applications for ocular surface diseases diagnosis

Deep Learning for the Detection of Multiple Fundus Diseases Using Ultra-widefield Images

Predicting Systemic Health Features from Retinal Fundus Images Using Transfer-Learning-Based Artificial Intelligence Models

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