Deep learning-based classification of retinal vascular diseases using ultra-widefield colour fundus photographs

Abitbol, Elie; Mière, Alexandra; Excoffier, Jean-Baptiste; Mehanna, Carl-Joe; Amoroso, Francesca; Kerr, Samuel; Ortala, Matthieu

doi:10.1136/bmjophth-2021-000924

Cited by 22 publications

(19 citation statements)

References 32 publications

(68 reference statements)

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“…We are aware that the performance gains of the NUN compared to other CNN models are statistically significant (p < 0.05), but still relatively small. As demonstrated by other studies, [24][25][26] the CNN-based models were able to reasonably detect and classify the presence of RVO on CFPs, making it challenging to improve this classification performance. However, NUN can provide a better visualization of the network's decision process based on our visual inspections, as well as provide useful analysis of the class distributions leveraging graph matrices.…”

Section: Branch Vein Occlusionmentioning

confidence: 93%

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Assessing retinal vein occlusion based on color fundus photographs using neural understanding network (NUN)

et al. 2022

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Objective To develop and validate a novel deep learning architecture to classify retinal vein occlusion (RVO) on color fundus photographs (CFPs) and reveal the image features contributing to the classification. Methods The neural understanding network (NUN) is formed by two components: (1) convolutional neural network (CNN)‐based feature extraction and (2) graph neural networks (GNN)‐based feature understanding. The CNN‐based image features were transformed into a graph representation to encode and visualize long‐range feature interactions to identify the image regions that significantly contributed to the classification decision. A total of 7062 CFPs were classified into three categories: (1) no vein occlusion (“normal”), (2) central RVO, and (3) branch RVO. The area under the receiver operative characteristic (ROC) curve (AUC) was used as the metric to assess the performance of the trained classification models. Results The AUC, accuracy, sensitivity, and specificity for NUN to classify CFPs as normal, central occlusion, or branch occlusion were 0.975 (± 0.003), 0.911 (± 0.007), 0.983 (± 0.010), and 0.803 (± 0.005), respectively, which outperformed available classical CNN models. Conclusion The NUN architecture can provide a better classification performance and a straightforward visualization of the results compared to CNNs.

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Section: Branch Vein Occlusionmentioning

confidence: 93%

“…Despite the success of CNNs in image classification, only a limited number of studies were performed to analyze RVO on fundus images. Abitbol et al 24 . used CNN algorithms to classify retinal vascular diseases including RVO; however, their data set was limited to 224 color fundus photographs (CFPs) with 55 “normal” images.…”

Section: Introductionmentioning

confidence: 99%

Assessing retinal vein occlusion based on color fundus photographs using neural understanding network (NUN)

et al. 2022

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“…After testing, the AUC of this model for BRVO was 0.959 and that of CRVO was 0.988. Abitbol et al (2022) collected 224 ultra-widefield color fundus images and constructed an AI model based on the DenseNet121 network to assist diagnose three types of retinal vascular diseases such as retinal vein occlusion. Finally, the accuracy of the model in the diagnosis of RVO was 0.884, and the AUC was 0.912.…”

Section: Application Of Artificial Intelligence In Retinal Vascular D...mentioning

confidence: 99%

Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images

Liu

et al. 2023

Front. Cell Dev. Biol.

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As the only blood vessels that can directly be seen in the whole body, pathological changes in retinal vessels are related to the metabolic state of the whole body and many systems, which seriously affect the vision and quality of life of patients. Timely diagnosis and treatment are key to improving vision prognosis. In recent years, with the rapid development of artificial intelligence, the application of artificial intelligence in ophthalmology has become increasingly extensive and in-depth, especially in the field of retinal vascular diseases. Research study results based on artificial intelligence and fundus images are remarkable and provides a great possibility for early diagnosis and treatment. This paper reviews the recent research progress on artificial intelligence in retinal vascular diseases (including diabetic retinopathy, hypertensive retinopathy, retinal vein occlusion, retinopathy of prematurity, and age-related macular degeneration). The limitations and challenges of the research process are also discussed.

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“…Existing DL approaches are based on adaptations of supervised DL techniques [8], i.e., techniques capable of "automatically learning" features by analyzing large training sets of segmented images [20] not offered by databases. Through deep learning classifiers, multiple retinal vascular diseases may be distinguished with an accuracy above 80% and may be a valuable instrument in areas with a shortage of ophthalmic care [21].…”

Section: Automated Classification Of Glaucomamentioning

confidence: 99%

Retinal Glaucoma Public Datasets: What Do We Have and What Is Missing?

Camara

Rezende

Pires

et al. 2022

JCM

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Public databases for glaucoma studies contain color images of the retina, emphasizing the optic papilla. These databases are intended for research and standardized automated methodologies such as those using deep learning techniques. These techniques are used to solve complex problems in medical imaging, particularly in the automated screening of glaucomatous disease. The development of deep learning techniques has demonstrated potential for implementing protocols for large-scale glaucoma screening in the population, eliminating possible diagnostic doubts among specialists, and benefiting early treatment to delay the onset of blindness. However, the images are obtained by different cameras, in distinct locations, and from various population groups and are centered on multiple parts of the retina. We can also cite the small number of data, the lack of segmentation of the optic papillae, and the excavation. This work is intended to offer contributions to the structure and presentation of public databases used in the automated screening of glaucomatous papillae, adding relevant information from a medical point of view. The gold standard public databases present images with segmentations of the disc and cupping made by experts and division between training and test groups, serving as a reference for use in deep learning architectures. However, the data offered are not interchangeable. The quality and presentation of images are heterogeneous. Moreover, the databases use different criteria for binary classification with and without glaucoma, do not offer simultaneous pictures of the two eyes, and do not contain elements for early diagnosis.

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Deep learning-based classification of retinal vascular diseases using ultra-widefield colour fundus photographs

Cited by 22 publications

References 32 publications

Assessing retinal vein occlusion based on color fundus photographs using neural understanding network (NUN)

Assessing retinal vein occlusion based on color fundus photographs using neural understanding network (NUN)

Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images

Retinal Glaucoma Public Datasets: What Do We Have and What Is Missing?

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