Development of a deep-learning system for detection of lattice degeneration, retinal breaks, and retinal detachment in tessellated eyes using ultra-wide-field fundus images: a pilot study

Zhang, Chenxi; He, Feng; Li, Bing; He, Xixi; Li, Xirong; Wu, Yu; Chen, Youxin

doi:10.1007/s00417-021-05105-3

Cited by 19 publications

(20 citation statements)

References 18 publications

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“…A previous study proposed a model (identifying notable peripheral retinal lesions) with an AUROC of 0.999, 8 and another study proposed a model detecting retinal breaks with AUROC values of 0.843 to 0.953, depending on the preprocessing technique implemented in tessellated eyes. 10 In per-object detection analysis, while the recall score was 0.9189, the precision score was 0.6800, which was relatively low. Among the 200 detected bounding boxes with the confidence level above the threshold, 136 were true positive and 64 were false positive.…”

Section: Discussionmentioning

confidence: 97%

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DETECTION AND LOCALIZATION OF RETINAL BREAKS IN ULTRAWIDEFIELD FUNDUS PHOTOGRAPHY USING a YOLO v3 ARCHITECTURE-BASED DEEP LEARNING MODEL

Lee

et al. 2022

Retina

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Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

DETECTION AND LOCALIZATION OF RETINAL BREAKS IN ULTRAWIDEFIELD FUNDUS PHOTOGRAPHY USING a YOLO v3 ARCHITECTURE-BASED DEEP LEARNING MODEL

Lee

et al. 2022

Retina

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“…This ASModel_UWF might have promising prospects, which could be used to screen anemia in a large population or in rural areas. Given our previously accomplished function of deep-learning-based retinal break and retinal detachment recognition through UWF images ( Zhang et al, 2021 ) and the renal function prediction by fundus images ( Zhao et al, 2021 ), we might achieve the goal of screening and evaluating cardiovascular disease, renal function, nervous system diseases, and fundus diseases merely by a single shot of UWF images.…”

Section: Discussionmentioning

confidence: 99%

“…Ultra-wide-field (UWF) scanning laser ophthalmoscopy enables capturing the ocular fundus up to 200° in a single exposure ( Yoo et al, 2020 ), offering us an opportunity to solve the aforementioned problems. Previous studies have successfully developed deep-learning-based techniques to identify the lattice degeneration, retinal detachment, or other notable peripheral retinal lesions based on UWF fundus images ( Ohsugi et al, 2017 ; Li et al, 2020 ; Zhang et al, 2021 ). While still, no study focuses on deep-learning-based anemia prediction by UWF fundus images.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Based Hemoglobin Concentration Prediction and Anemia Screening Using Ultra-Wide Field Fundus Images

Zhao

Meng

Su³

et al. 2022

Front. Cell Dev. Biol.

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Background: Anemia is the most common hematological disorder. The purpose of this study was to establish and validate a deep-learning model to predict Hgb concentrations and screen anemia using ultra-wide-field (UWF) fundus images.Methods: The study was conducted at Peking Union Medical College Hospital. Optos color images taken between January 2017 and June 2021 were screened for building the dataset. ASModel_UWF using UWF images was developed. Mean absolute error (MAE) and area under the receiver operating characteristics curve (AUC) were used to evaluate its performance. Saliency maps were generated to make the visual explanation of the model.Results: ASModel_UWF acquired the MAE of the prediction task of 0.83 g/dl (95%CI: 0.81–0.85 g/dl) and the AUC of the screening task of 0.93 (95%CI: 0.92–0.95). Compared with other screening approaches, it achieved the best performance of AUC and sensitivity when the test dataset size was larger than 1000. The model tended to focus on the area around the optic disc, retinal vessels, and some regions located at the peripheral area of the retina, which were undetected by non-UWF imaging.Conclusion: The deep-learning model ASModel_UWF could both predict Hgb concentration and screen anemia in a non-invasive and accurate way with high efficiency.

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“…Five studies have been published on retinal detachment detection from UWFIs. [33,[45][46][47][48] The rst published, from Ohsugi et al, used UWFIs to detect rhegmatogenous retinal detachment (RRD). [45] The study used a CNN with 3 convolutional layers, of which each were followed by activation function (ReLU) layers and nished with two fully connected layers.…”

Section: Retinal Detachmentmentioning

confidence: 99%

Deep learning for ultra-widefield imaging: a scoping review

Bhambra

Antaki

Malt

et al. 2022

Graefes Arch Clin Exp Ophthalmol

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Purpose: This article is a scoping review of published and peer-reviewed articles using deep-learning (DL) applied to ultra-wide eld (UWF) imaging. This study provides an overview of the published uses of DL and UWF imaging for the detection of ophthalmic and systemic diseases, generative image synthesis, quality assessment of images, and segmentation and localization of ophthalmic image features. Methods: A literature search was performed up to August 31st, 20201 using PubMed, Embase, Cochrane Library, and Google Scholar. The inclusion criteria were as follows: (1) Deep Learning, (2) Ultra-Wide eld Imaging. The exclusion criteria were as follows: (1) articles published in any language other than English, (2) articles not peer-reviewed (usually preprints) (3) no full-text availability (4) articles using machine learning algorithms other than deep learning. No study design was excluded from consideration.Results: A total of 36 studies were included. 23 studies discussed ophthalmic disease detection and classi cation, 5 discussed segmentation and localization of UWF images, 3 discussed generative image synthesis, 3 discussed ophthalmic image quality assessment, and 2 discussed detecting systemic diseases via UWFI. Conclusion:The application of DL to UWFI has demonstrated signi cant effectiveness in the diagnosis and detection of ophthalmic diseases including diabetic retinopathy, retinal detachment, and glaucoma. DL has been used with UWFI to also diagnose systemic diseases like Alzheimer's, and also applied in the generation of synthetic ophthalmic images. This scoping review highlights and discusses the current uses of DL with UWFI, and the future of DL applications in this eld.

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Development of a deep-learning system for detection of lattice degeneration, retinal breaks, and retinal detachment in tessellated eyes using ultra-wide-field fundus images: a pilot study

Cited by 19 publications

References 18 publications

DETECTION AND LOCALIZATION OF RETINAL BREAKS IN ULTRAWIDEFIELD FUNDUS PHOTOGRAPHY USING a YOLO v3 ARCHITECTURE-BASED DEEP LEARNING MODEL

DETECTION AND LOCALIZATION OF RETINAL BREAKS IN ULTRAWIDEFIELD FUNDUS PHOTOGRAPHY USING a YOLO v3 ARCHITECTURE-BASED DEEP LEARNING MODEL

Deep-Learning-Based Hemoglobin Concentration Prediction and Anemia Screening Using Ultra-Wide Field Fundus Images

Deep learning for ultra-widefield imaging: a scoping review

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