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人工智能在眼底病中的应用

Application of artificial intelligence in ocular fundus diseases

来源期刊： 眼科学报 | 2022年3月 第37卷 第3期： 200-207 发布时间：2021–07–18 收稿时间：2022/11/28 13:47:52 阅读量：3808
作者：
林喆,
李龙辉,
赵兰琴,
邓文锐,
崔婷欣,
林浩添,
关键词：
人工智能深度学习糖尿病性视网膜病变青光眼可解释性
artificial intelligence deep learning diabetic retinopathy glaucoma interpretability
DOI：
10.3978/j.issn.1000-4432.2022.03.04
人工智能是对人类智能的模拟和拓展。基于深度学习的人工智能可以很好地利用图像的内在特征，如轮廓、框架等，来分析图像。研究人员通常利用图像来诊断眼底病，因此将人工智能应用于眼底检查是有意义的。在眼科领域，人工智能通过分析光学相干断层扫描图像、眼底照片和超宽视野图像，已经在检测多种眼底疾病上取得了类似医生的性能。它也已经被广泛应用于疾病进展预测。然而，人工智能在眼科的应用也存在一些潜在的挑战，黑盒问题是其中之一。研究人员致力于开发更多的可解释的深度学习系统，并确认其临床可行性。人工智能在最流行的眼底病中的最新应用、可能遇到的挑战以及未来的道路将一一阐述。
Artificial intelligence (AI) is about simulating and expanding human intelligence. AI based on deep learning (DL) can analyze images well by using their inherent features, such as outlines, frames and so on. As researchers generally diagnoses ocular fundus diseases by images, it makes sense to apply AI to fundus examination. In ophthalmology, AI has achieved doctor-like performance in detecting multiple ocular fundus diseases through optical coherence tomography (OCT) images, fundus photographs, and ultra-wide-field (UWF) images. It has also been widely used in disease progression prediction. Nonetheless, there are also some potential challenges with AI application in ophthalmology, one of which is the black-box problem. Researchers are devoted to developing more interpretable deep learning systems (DLS) and confirming their clinical feasibility. This review describes a summary of the state-of-the-art AI application in the most popular ocular fundus diseases, potential challenges and the path forward.
    Ocular fundus disease is complex and challenging. As the population aged, it has posed a major threat to people’s health. In China, the most common fundus diseases include diabetic retinopathy (DR), retinal vein occlusion (RVO), glaucoma and so on. Among all kinds of technology, medical image is the most important one for clinical diagnosis and prediction. Ophthalmologists tend to use fundus images, optical coherence tomography (OCT) and so on. At present, the diagnosis based on medical images is quite subjective. For the same image, different ophthalmologists may get different results. In addition, manual analysis will require a lot of time.
    Deep learning (DL) is an important part of artificial intelligence (AI). It can learn experience (characteristics of fundus diseases) from data (medical images) and analyze new data. Therefore, AI technology can provide objective results. It can also save time.
In recent years, with the rapid development of AI technology, the automatic fundus diseases diagnosis has attracted worldwide attention. This review is about recent achievements in this area and problems we are meeting with.

1 AI, DL, and convolutional neural networks (CNN)

    AI was first proposed by Mccarthy’s team[1] in the 1950s. It is a branch of computer science and mainly develops intelligent systems which simulate human thought. Machine learning (ML) is an important part of AI[2]. It is a group of efficient algorithms figuring out laws from data. Traditional ML includes support vector machine (SVM), random forest, and other algorithms. DL, including CNN and artificial neural networks, is a sub-field of ML[3]. DL is far more excellent than classic ML in analyzing images. This is because DL is good at processing large amounts of data, so that the algorithm can better fit the original data. Exceptionally, DL can retain the original features of the data, that is, some components in the image. These components are layered on top of each other and eventually become an image. This is similar to how the human brain processes images. Thanks to the development of computer performance, DL has achieved great progress in image analysis[4], language[5], and other aspects. In the medical field, especially in the ocular fundus disease diagnosis, DL can diagnose a variety of diseases, and the accuracy is close to or even higher than that of experienced doctors[6-8].

2 AI in fundus diseases

2.1 DR

    DR is a leading cause of vision impairment and blindness. By 2040, 600 million people worldwide will have diabetes, with a third having DR[9]. The fundus photography is generally recognized as a conventional method of diagnosing DR. As DL performs well in detecting features in fundus photographs, it can be applied to detecting DR. While the DR screening progress meets with difficulties, such as high cost, not enough qualified doctors, DL has totally changed the performance of detecting DR.
    In 2016, Gulshan et al.[8] showed that DL gave an excellent performance in detecting referable DR. The system achieved 90.3% and 87.0% sensitivity, with 98.1% and 98.5% specificity, when detecting two validation sets of referable DR. It rivaled results of a panel of 7 US board-certified ophthalmologists. Later in 2017, Ting et al.[10] showed that deep learning system (DLS) also performed well when detecting DR through retinal images from multiethnic populations. Its sensitivity was 90.5% and specificity was 91.6%.
    Recently, researchers are devoted to verifying the application to more advanced technology. Oh et al.[11] showed that the DLS based on ultra-wide-field (UWF) outperformed that based on the optic disc and macula-centered image in a statistical sense. Hacisoftaoglu et al.[12] applied ResNet50 model to images taken on smartphones to detect DR. The model achieved 98.6% accuracy, with 98.2% sensitivity and 99.1% specificity.
    Besides, DLS has achieved remarkable progress in clinical application. In 2018, IDx-DR[13] was the first DLS to be approved for DR detection by Food and Drug Administration. It performed well in the clinical trial, with 87.2% sensitivity and 90.7% specificity. It can be used in primary care and alleviate the demand for human physicians. EyeArt[14] is a DR detection system based on cloud technology. It achieved 91.7% sensitivity and 91.5% specificity in a retrospective study with 78,685 patients involved. With its potential benefits of efficiency and reproducibility, EyeArt would be useful in reducing the burden from the increased people with diabetes.

2.2 RVO

    RVO is one of the major global blinding diseases[15]. After DR, it is the second most common clinical retinal vascular disease[16]. In 2018, Nagasato et al.[17] used DLS to detect central retinal vein occlusion (CRVO) in UWF images. The DL technology is superior in sensitivity, specificity and area under curve (AUC), better than SVM. This experiment showed the possibility of automatic CRVO detection with DL. In 2019, Nagasato et al.[18] confirmed that DLS has similar advantages in detecting branch retinal vein occlusion (BRVO) through UWF images. At the same time, Nagasato et al.[19] used DLS to detect the non-perfusion area through optical coherence tomography angiography (OCTA) images. The results showed that DLS is superior to SVM and human RVO experts. Yeung et al.[20] graded the OCTA images of BRVO patients as mild, moderate and severe macular ischemia. DLS detected these images, generated parameters and was compared with OCTA machine. In the images of severe macular ischemia, DLS results of denoised images are more accurate than of original images. The research showed that it is conducive to denoise OCTA images for automatic macular ischemia grading in BRVO eyes.

2.3 The fundus lesion of glaucoma

    Glaucoma is a progressive optic neuropathy that can cause retinal nerve fiber layer (RNFL) defect and glaucomatous optic neuropathy (GON)[21]. For people aged 40 to 80, the global prevalence rate of glaucoma is 3.4%. Moreover, it is estimated that by 2040, there will be approximately 112 million affected people worldwide[9].
    While glaucoma damage is irreversible, early treatment can usually prevent or slow functional impairment progression. Therefore, researches on detecting potential glaucoma and glaucoma progression have flourished recently. Lee et al.[22] developed a DLS to predict glaucoma development through fundus images. The system detected RNFL thickness and predicted the change of it. The study showed that the rate of change of RNFL thickness predicted the conversion to glaucoma. Christopher et al.[23] used DL models to predict the progression of glaucoma visual field damage through spectral domain OCT (SD OCT). The study showed that the model based on RNFL en face images performed better than that based on RNFL thickness maps. Garcia et al.[6] used Kalman filtering to predict intraocular pressure (IOP) through previous IOP results. The study showed that predicted IOP accorded well with observed IOP.
    Besides, some important progress has been made on glaucoma detection. Medeiros et al.[24] developed a machine-to-machine DLS without human labeling. The system used a classifier trained by RNFL thickness data which is extracted from SD OCT. In this way, it can avoid mistakes that human markers usually make. For example, it can distinguish eyes with large physiologic cups from those with actual glaucomatous damage. The study has opened new possibilities of surpassing human markers’ limitations. Thompson et al.[25] developed a DLS without conventional segmentation of RNFL. It performed better than the conventional method in detecting glaucoma. This may be a new approach to improving the accuracy and sensitivity in glaucoma detection. Li et al.[26] developed a DLS for GON detection through color fundus photographs. The DLS achieved an AUC of 0.983 to 0.999. It is comparable to that of an experienced ophthalmologist.

2.4 Age-related macular degeneration (AMD)

    In developed countries, many patients are blind because of AMD[27-28]. It is the major course of vision impairment in American whites aged over 50[29]. The incidence rate of AMD in China will be increasing because of the aging population[30]. Scientists estimated that there would be 288 million AMD patients by 2040[31].
    In 2018, Rohm et al.[32] showed that ML performed well in predicting AMD progression. The system predicted subjects’ visual acuity (VA) by 41 medical records and OCT features. The prediction results are close to the actual ones.
    There is an urgent need for efficient methods of AMD prediction. AI has made excellent progress in this area. Banerjee et al.[33] proposed a model to predict the risk of exudation in non-exudative AMD eyes. The system performed well in forecasting in the short term (within 3 months) and long term (within 21 months). While the AUC of 21 months is significantly lower than 3 months, its high performance within 3 months showed the possibility of impacting clinical follow-up. Yim et al.[7] used a DLS to predict the AMD progression of patients’ one eye, the other eye of whom has been diagnosed as AMD. Its accuracy is better than 5 of the 6 ophthalmologists. Waldstein et al.[34] used a DLS to predict early-stage and middle-stage AMD progression to late stage. The system made a prediction by detecting drusen and hyper-reflective foci (HRF) from OCT images. Lee et al.[35] developed a DLS to predict visual prognosis. The system made a prediction by analyzing features in OCT images, such as intraretinal fluid, subretinal fluid, and pigment epithelial detachment. Liu et al.[36] developed a DLS to predict responses to treatment. The system made a prediction by detecting pretherapeutic OCT images. Researchers compared the prediction with the observed post-therapeutic OCT image and found a strong correlation between them.
     Besides, AI has performed well in AMD diagnosis. Liefers et al.[37] developed a DLS to detect AMD and evaluated its performance. They trained the system for the segmentation of 13 features associated with atrophic AMD. The system obtained a higher score than experienced human graders on 11 features out of 13.

2.5 Retinopathy of prematurity (ROP)

    ROP is a leading cause of childhood blindness worldwide[38]. ROP screening can identify early signs of severe ROP, and with timely treatment it can prevent most cases of ROP blindness[39]. Therefore, researchers have been paying attention to the prediction of ROP. Taylor et al.[40] developed a DLS to predict ROP progression. The system proposed the ROP vascular severity score by detecting clinical examination images. And it made a prediction based on these scores. The prediction is associated with clinical progression. Huang et al.[41] used a DLS to predict visual prognosis after treatment. The system predicted the VA, best corrected VA (BCVA) and spherical equivalent (SE) of ROP patients. It performed better on predicting SE than on predicting VA and BCVA. Wang et al.[42] developed 4 classifiers, including image quality, any stage of ROP, intraocular hemorrhage, and preplus/plus diseases. The platform integrated the results of 4 classifiers and generated the diagnosis results. Its performance is comparable to human ROP experts.

2.6 Central serous chorioretinopathy (CSC)

    CSC is one of the most common ocular fundus diseases, affecting middle-aged men mostly[43]. Zhen et al.[44] used Inception-V3 to assess CSC depicted on color fundus photographs. The research showed that the performance of DLS is comparable to 2 ophthalmologists. Yoon et al.[45] developed a new DL model to diagnose CSC in SD-OCT images, and distinguish chronic from acute CSC. The research showed that the new model is superior to VGG-16 and Resnet-50 in both diagnosis and classification. And it is comparable to human experts.

2.7 Retinal detachment (RD)

    According to the cause, RD can be divided into rhegmatogenous retinal detachment (RRD) and non-rhegmatogenous. RRD can lead to blindness without surgical treatment[46]. Li et al.[47] developed a DLS that can detect RD based on UWF images. Its performance is comparable to human experts. It can also guide patients to adopt an appropriate position before surgery, which can reduce RD progression. Xing et al.[48] developed a weakly supervised two-stage learning architecture to detect and segment RD. They designed a Located-CNN to detect lesion regions in SD-OCT. The results showed that this method is superior to some models trained with stronger supervision. It is a promising method as it reduces the amount of labelling.

2.8 Retinitis pigmentosa (RP)

    RP is the most common blinding inherited fundus disease[49]. Masumoto et al.[50] used CNN to detect RP through UWF pseudocolor imaging and UWF autofluorescence of retinitis pigmentosa. The research showed that DLS performed well in these two kinds of UWF images. Arsalan et al.[51] developed RPS-Net, an automatic RP segmentation network based on DL. This method provided fine segmentation and accurately detected RP even in the case of degraded images. The research showed that RPS-Net is superior to the state-of-the-art methods in segmentation.

2.9 Other optical fundus diseases

    Diabetic macular edema (DME), optical neuritis, lattice degeneration, and retinal breaks are common. Wu et al.[52] developed a DLS to detect DME based on OCT images. They train the DLS to detect 3 OCT patterns of DME, including diffused retinal thickening, cystoid macular edema, and serous retinal detachment. The system showed high accuracy, sensitivity, specificity, and AUC in all 3 patterns. It also performed well in external validation. This research emphasized the potential of DL in detecting DME. Abri Aghdam et al.[53] used learning decision tree to assess the probability of conversion to multiple sclerosis (MS) in patients with optic neuritis. The DLS also evaluated the factor most related to the conversion. Results showed that the overall conversion rate to MS was 42.2% and the presence of white matter plaque was the most important factor. Li et al.[54] developed a DLS to identify lattice degeneration and retinal breaks based on UWF images. They used 4 different DL algorithms (VGG16, InceptionV3, InceptionResNetV2, and ResNet50) with 3 preprocessing techniques (original, augmented, and histogram-equalized images) for the training. The research confirmed that InceptionResNetV2 was the best algorithm and the original image augmentation was the best preprocessing technique.

3 Potential challenges

3.1 The Black-box problem

    As AI is widely used in the medical field, the AI black-box has become a critical problem which researchers have to solve. Since AI’s neural network algorithm is too complicated for humans, it is difficult for human end-users to know whether the conclusion is reliable. As humans require more accurate algorithms, the system will be more complicated and more difficult to be interpreted. However, when DL is used in medical diagnosis, it is bound to use detection methods which is understandable for humans, in order to judge the reliability.
    In recent years, researchers are developing methods to interpret DL algorithms to solve this problem. The most common and simplest method is to perform an occlusion test used for AMD detection on OCT[55]. Recently, Schmidt-Erfurth et al.[56] have used statistical indicators to compare the interpretability of different algorithms used to diagnose glaucoma. These studies will significantly promote clinical understanding.

3.2 Poor adaptability to different equipment

    When we developed a new DLS, the image data for training and detecting often come from the same equipment. When the AI model is applied to images obtained by equipment different from its training data source, its accuracy will often decrease. This problem is obvious in OCT images.
    Different countries, regions and medical institutions have different inspection equipment. And the quality of the pictures needed for training will be unstable, which will ultimately affect the accuracy of AI model.

3.3 Unsophisticated databases

    Several databases cannot reflect the real world. The data currently used for AI research are mostly public databases or databases designed for other researches. Most of the data only include the target disease, but many people actually have multiple eye diseases. What’s more, the data often include only one single race, which means it cannot reflect the obvious ethnic differences among people. It is extremely urgent for us to develop more sophisticated databases.

3.4 Different diagnostic criteria

    In clinical practice, ophthalmologists make a diagnosis based on multiple factors. But in researches, diagnosis from DLS usually bases on one certain factor. This makes a huge difference. Taking glaucoma as an example, ophthalmologists need to consider complicated examination results, such as IOP, OCT images, visual field and so on. While the DLS only consider one of these factors. The ability of AI to make a diagnosis based on multiple factors is unknown.

4 Conclusions

    DL is the most advanced AI ML technology. It has shown clinically acceptable performance in many ocular fundus diseases, such as DR, glaucoma, and AMD. AI has helped humans to predict ocular fundus diseases and reduce their prevalence. The progress on AI also shows the possibility of decreasing the incidence rate in low-income and middle-income areas. Especially for glaucoma, a fast and irreversible eye disease, it will vastly change patients’ fate. In order to improve the DL system’s clinical acceptance, it is essential to use existing and future methods to unlock the black-box nature of DL. Despite the challenges, DL may lead the advance in medicine and ophthalmology for decades.

Footnote

    Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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1、Mccarthy J, Minsky ML, Shannon CE. A proposal for the Dartmouth summer research project on artificial intelligence - August 31, 1955[J]. Ai Magazine, 2006, 27(4): 12-4.Mccarthy J, Minsky ML, Shannon CE. A proposal for the Dartmouth summer research project on artificial intelligence - August 31, 1955[J]. Ai Magazine, 2006, 27(4): 12-4.
2、Lip GY, Nieuwlaat R, Pisters R, et al. Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach: the euro heart survey on atrial fibrillation[J]. Chest, 2010, 137(2): 263-272.Lip GY, Nieuwlaat R, Pisters R, et al. Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach: the euro heart survey on atrial fibrillation[J]. Chest, 2010, 137(2): 263-272.
3、LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.
4、Olga R, Jia D, Hao S, et al. ImageNet large scale visual recognition challenge[J]. International Journal of Computer Vision, 2015.Olga R, Jia D, Hao S, et al. ImageNet large scale visual recognition challenge[J]. International Journal of Computer Vision, 2015.
5、Hirschberg J, Manning CD. Advances in natural language processing[J]. Science, 2015, 349(6245): 261-266.Hirschberg J, Manning CD. Advances in natural language processing[J]. Science, 2015, 349(6245): 261-266.
6、Garcia GP, Lavieri MS, Andrews C, et al. Accuracy of Kalman filtering in forecasting visual field and intraocular pressure trajectory in patients with ocular hypertension[J]. JAMA Ophthalmol, 2019, 137(12): 1416-1423.Garcia GP, Lavieri MS, Andrews C, et al. Accuracy of Kalman filtering in forecasting visual field and intraocular pressure trajectory in patients with ocular hypertension[J]. JAMA Ophthalmol, 2019, 137(12): 1416-1423.
7、Yim J, Chopra R, Spitz T, et al. Predicting conversion to wet age-related macular degeneration using deep learning[J]. Nat Med, 2020, 26(6): 892-899.Yim J, Chopra R, Spitz T, et al. Predicting conversion to wet age-related macular degeneration using deep learning[J]. Nat Med, 2020, 26(6): 892-899.
8、Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs[J]. JAMA, 2016, 316(22): 2402-2410.Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs[J]. JAMA, 2016, 316(22): 2402-2410.
9、Tham YC, Li X, Wong TY, et al. Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis[J]. Ophthalmology, 2014, 121(11): 2081-2090.Tham YC, Li X, Wong TY, et al. Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis[J]. Ophthalmology, 2014, 121(11): 2081-2090.
10、Ting DSW, Cheung CY, Lim G, et al. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes[J]. JAMA, 2017, 318(22): 2211-2223.Ting DSW, Cheung CY, Lim G, et al. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes[J]. JAMA, 2017, 318(22): 2211-2223.
11、Oh K, Kang HM, Leem D, et al. Early detection of diabetic retinopathy based on deep learning and ultra-wide-field fundus images[J]. Sci Rep, 2021, 11(1): 1897.Oh K, Kang HM, Leem D, et al. Early detection of diabetic retinopathy based on deep learning and ultra-wide-field fundus images[J]. Sci Rep, 2021, 11(1): 1897.
12、Hacisoftaoglu RE, Karakaya M, Sallam AB. Deep learning frameworks for diabetic retinopathy detection with smartphone-based retinal imaging systems[J]. Pattern Recognit Lett, 2020, 135: 409-417.Hacisoftaoglu RE, Karakaya M, Sallam AB. Deep learning frameworks for diabetic retinopathy detection with smartphone-based retinal imaging systems[J]. Pattern Recognit Lett, 2020, 135: 409-417.
13、van der Heijden AA, Abramoff MD, Verbraak F, et al. Validation of automated screening for referable diabetic retinopathy with the IDx-DR device in the Hoorn Diabetes Care System[J]. Acta Ophthalmol, 2018, 96(1): 63-68.van der Heijden AA, Abramoff MD, Verbraak F, et al. Validation of automated screening for referable diabetic retinopathy with the IDx-DR device in the Hoorn Diabetes Care System[J]. Acta Ophthalmol, 2018, 96(1): 63-68.
14、Rajalakshmi R, Subashini R, Anjana RM, et al. Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence[J]. Eye (Lond), 2018, 32(6): 1138-1144.Rajalakshmi R, Subashini R, Anjana RM, et al. Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence[J]. Eye (Lond), 2018, 32(6): 1138-1144.
15、Wong TY, Scott IU. Clinical practice. Retinal-vein occlusion[J]. N Engl J Med, 2010, 363(22): 2135-2144.Wong TY, Scott IU. Clinical practice. Retinal-vein occlusion[J]. N Engl J Med, 2010, 363(22): 2135-2144.
16、Buehl W, Sacu S, Schmidt-Erfurth U. Retinal vein occlusions[J]. Dev Ophthalmol, 2010, 46: 54-72.Buehl W, Sacu S, Schmidt-Erfurth U. Retinal vein occlusions[J]. Dev Ophthalmol, 2010, 46: 54-72.
17、Nagasato D, Tabuchi H, Ohsugi H, et al. Deep neural network-based method for detecting central retinal vein occlusion using ultrawide-field fundus ophthalmoscopy[J]. J Ophthalmol, 2018, 2018: 1875431.Nagasato D, Tabuchi H, Ohsugi H, et al. Deep neural network-based method for detecting central retinal vein occlusion using ultrawide-field fundus ophthalmoscopy[J]. J Ophthalmol, 2018, 2018: 1875431.
18、Nagasato D, Tabuchi H, Ohsugi H, et al. Deep-learning classifier with ultrawide-field fundus ophthalmoscopy for detecting branch retinal vein occlusion[J]. Int J Ophthalmol, 2019, 12(1): 94-99.Nagasato D, Tabuchi H, Ohsugi H, et al. Deep-learning classifier with ultrawide-field fundus ophthalmoscopy for detecting branch retinal vein occlusion[J]. Int J Ophthalmol, 2019, 12(1): 94-99.
19、Nagasato D, Tabuchi H, Masumoto H, et al. Automated detection of a nonperfusion area caused by retinal vein occlusion in optical coherence tomography angiography images using deep learning[J]. PLoS One, 2019, 14(11): e0223965.Nagasato D, Tabuchi H, Masumoto H, et al. Automated detection of a nonperfusion area caused by retinal vein occlusion in optical coherence tomography angiography images using deep learning[J]. PLoS One, 2019, 14(11): e0223965.
20、Yeung L, Lee YC, Lin YT, et al. Macular ischemia quantification using deep-learning denoised optical coherence tomography angiography in branch retinal vein occlusion[J]. Transl Vis Sci Technol, 2021, 10(7): 23.Yeung L, Lee YC, Lin YT, et al. Macular ischemia quantification using deep-learning denoised optical coherence tomography angiography in branch retinal vein occlusion[J]. Transl Vis Sci Technol, 2021, 10(7): 23.
21、Osborne NN, Wood JP, Chidlow G, et al. Ganglion cell death in glaucoma: what do we really know?[J]. Br J Ophthalmol, 1999, 83(8): 980-986.Osborne NN, Wood JP, Chidlow G, et al. Ganglion cell death in glaucoma: what do we really know?[J]. Br J Ophthalmol, 1999, 83(8): 980-986.
22、Lee T, Jammal AA, Mariottoni EB, et al. Predicting glaucoma development with longitudinal deep learning predictions from fundus photographs[J]. Am J Ophthalmol, 2021, 225: 86-94.Lee T, Jammal AA, Mariottoni EB, et al. Predicting glaucoma development with longitudinal deep learning predictions from fundus photographs[J]. Am J Ophthalmol, 2021, 225: 86-94.
23、Christopher M, Bowd C, Belghith A, et al. Deep learning approaches predict glaucomatous visual field damage from OCT optic nerve head en face images and retinal nerve fiber layer thickness maps[J]. Ophthalmology, 2020, 127(3): 346-356.Christopher M, Bowd C, Belghith A, et al. Deep learning approaches predict glaucomatous visual field damage from OCT optic nerve head en face images and retinal nerve fiber layer thickness maps[J]. Ophthalmology, 2020, 127(3): 346-356.
24、Medeiros FA, Jammal AA, Thompson AC. From machine to machine: an OCT-trained deep learning algorithm for objective quantification of glaucomatous damage in fundus photographs[J]. Ophthalmology, 2019, 126(4): 513-521.Medeiros FA, Jammal AA, Thompson AC. From machine to machine: an OCT-trained deep learning algorithm for objective quantification of glaucomatous damage in fundus photographs[J]. Ophthalmology, 2019, 126(4): 513-521.
25、Thompson AC, Jammal AA, Berchuck SI, et al. Assessment of a segmentation-free deep learning algorithm for diagnosing glaucoma from optical coherence tomography scans[J]. JAMA Ophthalmol, 2020, 138(4): 333-339.Thompson AC, Jammal AA, Berchuck SI, et al. Assessment of a segmentation-free deep learning algorithm for diagnosing glaucoma from optical coherence tomography scans[J]. JAMA Ophthalmol, 2020, 138(4): 333-339.
26、Li Z, He Y, Keel S, et al. Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs[J]. Ophthalmology, 2018, 125(8): 1199-1206.Li Z, He Y, Keel S, et al. Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs[J]. Ophthalmology, 2018, 125(8): 1199-1206.
27、Bressler NM, Bressler SB, Congdon NG, et al. Potential public health impact of Age-Related Eye Disease Study results: AREDS report no. 11[J]. Arch Ophthalmol, 2003, 121(11): 1621-1624.Bressler NM, Bressler SB, Congdon NG, et al. Potential public health impact of Age-Related Eye Disease Study results: AREDS report no. 11[J]. Arch Ophthalmol, 2003, 121(11): 1621-1624.
28、Smith W, Assink J, Klein R, et al. Risk factors for age-related macular degeneration: pooled findings from three continents[J]. Ophthalmology, 2001, 108(4): 697-704.Smith W, Assink J, Klein R, et al. Risk factors for age-related macular degeneration: pooled findings from three continents[J]. Ophthalmology, 2001, 108(4): 697-704.
29、Rudnicka AR, Kapetanakis VV, Jarrar Z, et al. Incidence of late-stage age-related macular degeneration in American Whites: systematic review and meta-analysis[J]. Am J Ophthalmol, 2015, 160(1): 85-93.e3.Rudnicka AR, Kapetanakis VV, Jarrar Z, et al. Incidence of late-stage age-related macular degeneration in American Whites: systematic review and meta-analysis[J]. Am J Ophthalmol, 2015, 160(1): 85-93.e3.
30、Song P, Du Y, Chan KY, et al. The national and subnational prevalence and burden of age-related macular degeneration in China[J]. J Glob Health, 2017, 7(2): 020703.Song P, Du Y, Chan KY, et al. The national and subnational prevalence and burden of age-related macular degeneration in China[J]. J Glob Health, 2017, 7(2): 020703.
31、Wong WL, Su X, Li X, et al. Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis[J]. Lancet Glob Health, 2014, 2(2): e106-e116.Wong WL, Su X, Li X, et al. Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis[J]. Lancet Glob Health, 2014, 2(2): e106-e116.
32、Rohm M, Tresp V, Müller M, et al. Predicting visual acuity by using machine learning in patients treated for neovascular age-related macular degeneration[J]. Ophthalmology, 2018, 125(7): 1028-1036.Rohm M, Tresp V, Müller M, et al. Predicting visual acuity by using machine learning in patients treated for neovascular age-related macular degeneration[J]. Ophthalmology, 2018, 125(7): 1028-1036.
33、Banerjee I, de Sisternes L, Hallak JA, et al. Prediction of age-related macular degeneration disease using a sequential deep learning approach on longitudinal SD-OCT imaging biomarkers[J]. Sci Rep, 2020, 10(1): 15434.Banerjee I, de Sisternes L, Hallak JA, et al. Prediction of age-related macular degeneration disease using a sequential deep learning approach on longitudinal SD-OCT imaging biomarkers[J]. Sci Rep, 2020, 10(1): 15434.
34、Waldstein SM, Vogl WD, Bogunovic H, et al. Characterization of drusen and hyperreflective foci as biomarkers for disease progression in age-related macular degeneration using artificial intelligence in optical coherence tomography[J]. JAMA Ophthalmol, 2020, 138(7): 740-747.Waldstein SM, Vogl WD, Bogunovic H, et al. Characterization of drusen and hyperreflective foci as biomarkers for disease progression in age-related macular degeneration using artificial intelligence in optical coherence tomography[J]. JAMA Ophthalmol, 2020, 138(7): 740-747.
35、Lee H, Kang KE, Chung H, et al. Automated segmentation of lesions including subretinal hyperreflective material in neovascular age-related macular degeneration[J]. Am J Ophthalmol, 2018, 191: 64-75.Lee H, Kang KE, Chung H, et al. Automated segmentation of lesions including subretinal hyperreflective material in neovascular age-related macular degeneration[J]. Am J Ophthalmol, 2018, 191: 64-75.
36、Liu Y, Yang J, Zhou Y, et al. Prediction of OCT images of short-term response to anti-VEGF treatment for neovascular age-related macular degeneration using generative adversarial network[J]. Br J Ophthalmol, 2020, 104(12): 1735-1740.Liu Y, Yang J, Zhou Y, et al. Prediction of OCT images of short-term response to anti-VEGF treatment for neovascular age-related macular degeneration using generative adversarial network[J]. Br J Ophthalmol, 2020, 104(12): 1735-1740.
37、Liefers B, Taylor P, Alsaedi A, et al. Quantification of key retinal features in early and late age-related macular degeneration using deep learning[J]. Am J Ophthalmol, 2021, 226: 1-12.Liefers B, Taylor P, Alsaedi A, et al. Quantification of key retinal features in early and late age-related macular degeneration using deep learning[J]. Am J Ophthalmol, 2021, 226: 1-12.
38、Blencowe H, Moxon S, Gilbert C. Update on blindness due to retinopathy of prematurity globally and in India[J]. Indian Pediatr, 2016, 53 Suppl 2: S89-S92.Blencowe H, Moxon S, Gilbert C. Update on blindness due to retinopathy of prematurity globally and in India[J]. Indian Pediatr, 2016, 53 Suppl 2: S89-S92.
39、Robinson R, O'Keefe M. Cryotherapy for retinopathy of prematurity--a prospective study[J]. Br J Ophthalmol, 1992, 76(5): 289-291.Robinson R, O'Keefe M. Cryotherapy for retinopathy of prematurity--a prospective study[J]. Br J Ophthalmol, 1992, 76(5): 289-291.
40、Taylor S, Brown JM, Gupta K, et al. Monitoring disease progression with a quantitative severity scale for retinopathy of prematurity using deep learning[J/OL]. JAMA Ophthalmol, 2019, [Epub ahead of print].Taylor S, Brown JM, Gupta K, et al. Monitoring disease progression with a quantitative severity scale for retinopathy of prematurity using deep learning[J/OL]. JAMA Ophthalmol, 2019, [Epub ahead of print].
41、Huang CY, Kuo RJ, Li CH, et al. Prediction of visual outcomes by an artificial neural network following intravitreal injection and laser therapy for retinopathy of prematurity[J]. Br J Ophthalmol, 2020, 104(9): 1277-1282.Huang CY, Kuo RJ, Li CH, et al. Prediction of visual outcomes by an artificial neural network following intravitreal injection and laser therapy for retinopathy of prematurity[J]. Br J Ophthalmol, 2020, 104(9): 1277-1282.
42、Wang J, Ji J, Zhang M, et al. Automated explainable multidimensional deep learning platform of retinal images for retinopathy of prematurity screening[J]. JAMA Netw Open, 2021, 4(5): e218758.Wang J, Ji J, Zhang M, et al. Automated explainable multidimensional deep learning platform of retinal images for retinopathy of prematurity screening[J]. JAMA Netw Open, 2021, 4(5): e218758.
43、Sahoo NK, Singh SR, Rajendran A, et al. Masqueraders of central serous chorioretinopathy[J]. Surv Ophthalmol, 2019, 64(1): 30-44.Sahoo NK, Singh SR, Rajendran A, et al. Masqueraders of central serous chorioretinopathy[J]. Surv Ophthalmol, 2019, 64(1): 30-44.
44、Zhen Y, Chen H, Zhang X, et al. Assessment of central serous chorioretinopathy depicted on color fundus photographs using deep learning[J]. Retina, 2020, 40(8): 1558-1564.Zhen Y, Chen H, Zhang X, et al. Assessment of central serous chorioretinopathy depicted on color fundus photographs using deep learning[J]. Retina, 2020, 40(8): 1558-1564.
45、Yoon J, Han J, Park JI, et al. Optical coherence tomography-based deep-learning model for detecting central serous chorioretinopathy[J]. Sci Rep, 2020, 10(1): 18852.Yoon J, Han J, Park JI, et al. Optical coherence tomography-based deep-learning model for detecting central serous chorioretinopathy[J]. Sci Rep, 2020, 10(1): 18852.
46、Kunikata H, Abe T, Nakazawa T. Historical, current and future approaches to surgery for rhegmatogenous retinal detachment[J]. Tohoku J Exp Med, 2019, 248(3): 159-168.Kunikata H, Abe T, Nakazawa T. Historical, current and future approaches to surgery for rhegmatogenous retinal detachment[J]. Tohoku J Exp Med, 2019, 248(3): 159-168.
47、Li Z, Guo C, Nie D, et al. Deep learning for detecting retinal detachment and discerning macular status using ultra-widefield fundus images[J]. Commun Biol, 2020, 3(1): 15.Li Z, Guo C, Nie D, et al. Deep learning for detecting retinal detachment and discerning macular status using ultra-widefield fundus images[J]. Commun Biol, 2020, 3(1): 15.
48、Xing R, Niu S, Gao X, et al. Weakly supervised serous retinal detachment segmentation in SD-OCT images by two-stage learning[J]. Biomed Opt Express, 2021, 12(4): 2312-2327.Xing R, Niu S, Gao X, et al. Weakly supervised serous retinal detachment segmentation in SD-OCT images by two-stage learning[J]. Biomed Opt Express, 2021, 12(4): 2312-2327.
49、Ducloyer J B, Le Meur G, Cronin T, et al. Gene therapy for retinitis pigmentosa. [J]. Medecine Sciences, 2020, 36(6-7): 607-615.Ducloyer J B, Le Meur G, Cronin T, et al. Gene therapy for retinitis pigmentosa. [J]. Medecine Sciences, 2020, 36(6-7): 607-615.
50、Masumoto H, Tabuchi H, Nakakura S, et al. Accuracy of a deep convolutional neural network in detection of retinitis pigmentosa on ultrawide-field images[J]. PeerJ, 2019, 7: e6900.Masumoto H, Tabuchi H, Nakakura S, et al. Accuracy of a deep convolutional neural network in detection of retinitis pigmentosa on ultrawide-field images[J]. PeerJ, 2019, 7: e6900.
51、Arsalan M, Baek NR, Owais M, et al. Deep learning-based detection of pigment signs for analysis and diagnosis of retinitis pigmentosa[J]. Sensors (Basel), 2020, 20(12): 3454.Arsalan M, Baek NR, Owais M, et al. Deep learning-based detection of pigment signs for analysis and diagnosis of retinitis pigmentosa[J]. Sensors (Basel), 2020, 20(12): 3454.
52、Wu Q, Zhang B, Hu Y, et al. Detection of morphologic patterns of diabetic macular edema using a deep learning approach based on optical coherence tomography images[J]. Retina, 2021, 41(5): 1110-1117.Wu Q, Zhang B, Hu Y, et al. Detection of morphologic patterns of diabetic macular edema using a deep learning approach based on optical coherence tomography images[J]. Retina, 2021, 41(5): 1110-1117.
53、Abri Aghdam K, Aghajani A, Kanani F, et al. A novel decision tree approach to predict the probability of conversion to multiple sclerosis in Iranian patients with optic neuritis[J]. Mult Scler Relat Disord, 2021, 47: 102658.Abri Aghdam K, Aghajani A, Kanani F, et al. A novel decision tree approach to predict the probability of conversion to multiple sclerosis in Iranian patients with optic neuritis[J]. Mult Scler Relat Disord, 2021, 47: 102658.
54、Li Z, Guo C, Nie D, et al. A deep learning system for identifying lattice degeneration and retinal breaks using ultra-widefield fundus images[J]. Ann Transl Med, 2019, 7(22): 618.Li Z, Guo C, Nie D, et al. A deep learning system for identifying lattice degeneration and retinal breaks using ultra-widefield fundus images[J]. Ann Transl Med, 2019, 7(22): 618.
55、Kermany DS, Goldbaum M, Cai W, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning[J]. Cell, 2018, 172(5): 1122-1131.e9.Kermany DS, Goldbaum M, Cai W, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning[J]. Cell, 2018, 172(5): 1122-1131.e9.
56、Schmidt-Erfurth U, Sadeghipour A, Gerendas BS, et al. Artificial intelligence in retina[J]. Prog Retin Eye Res, 2018, 67: 1-29.Schmidt-Erfurth U, Sadeghipour A, Gerendas BS, et al. Artificial intelligence in retina[J]. Prog Retin Eye Res, 2018, 67: 1-29.
1、本科教学质量工程项目[教务(2021)93号]。This work was supported by the Undergraduate Teaching Quality Engineering Project, China [(2021) No. 93]. ()
2、本科教学质量工程项目 [ 教务 (2021)93 号 ]。This work was supported by the Undergraduate Teaching Quality Engineering Project, China [(2021) No. 93].()
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