Prediction of Epiretinal Membrane from Retinal Fundus Images Using Deep Learning

Casado-García, Ángela; García-Domínguez, Manuel; Heras, Jónathan; Inés, Adrián; Royo, Didac; Zapata, Miguel Ángel

doi:10.1007/978-3-030-85713-4_1

Cited by 7 publications

(6 citation statements)

References 18 publications

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“…Our proposed model showed high accuracy in this multi-class classification scenario. In this respect, our study makes progress in follow-up to the existing work on ERM detection from retinal images, which so far considered binary scenarios, i.e., deciding either the absence or presence of ERM [11, 12, 13, 14, 15]. In fact, our proposed DNN seems to grade the extend of the disease automatically.…”

Section: Discussionmentioning

confidence: 87%

“…Here, also early stages of the disease and changes of the ERM over time are visible [7]. Owing to recent advances in deep learning and the large-scale analysis of medical images via deep neural networks (DNNs) [8, 9, 10], fundus and OCT imaging modalities have been investigated for automated ERM detection from retinal images [11, 12, 13, 14, 15]. Nevertheless, these studies were limited to relatively small datasets or patients with ERM constituted only a small fraction of larger datasets [12, 14, 15, 13, 11].…”

Section: Introductionmentioning

confidence: 99%

“…Owing to recent advances in deep learning and the large-scale analysis of medical images via deep neural networks (DNNs) [8, 9, 10], fundus and OCT imaging modalities have been investigated for automated ERM detection from retinal images [11, 12, 13, 14, 15]. Nevertheless, these studies were limited to relatively small datasets or patients with ERM constituted only a small fraction of larger datasets [12, 14, 15, 13, 11]. In addition, these studies primarily used data from patients with advanced stages of the disease, which are comparably easy to classify.…”

Section: Introductionmentioning

confidence: 99%

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Interpretable Detection of Epiretinal Membrane from Optical Coherence Tomography with Deep Neural Networks

Ayhan

Neubauer

Uzel

et al. 2022

Preprint

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Purpose: To automatically detect epiretinal membranes (ERM) in various OCT scans of the central and paracentral macula region and classify them by size using deep neural networks (DNNs). Methods: 11,061 OCT-images of 624 volume OCT scans (624 eyes of 461 patients) were included and graded according to the presence of an ERM and its size (small 100-1000μm, large >1000μm). The data set was divided into training, validation and test sets (comprising of 75%, 10%, 15% of the data, respectively). An ensemble of DNNs was trained and saliency maps were generated using Guided Backprob. OCT-scans were also transformed into a one-dimensional-value using t-SNE analysis. Results: The DNNs' receiver-operating-characteristics on the test set showed a high performance for no ERM, small ERM and large ERM cases (AUC: 0.99, 0.92, 0.99, respectively; 3-way accuracy: 89% ), with small ERMs being the most difficult ones to detect. t-SNE analysis sorted cases by size and, in particular, revealed increased classification uncertainty at the transitions between groups. Saliency maps reliably highlighted ERM, regardless of the presence of other OCT features (i.e. retinal thickening, intraretinal pseudo- cysts, epiretinal proliferation) and entities such as ERM-retinoschisis, macular pseudohole and lamellar macular hole. Conclusion: DNNs can reliably detect and grade ERMs according to their size not only in the fovea but also in the paracentral region. This is also achieved in cases of hard-to-detect, small ERMs. In addition, the generated saliency maps can be used effectively to highlight small ERMs that might otherwise be missed. The proposed model could be used for screening programs or decision support systems in the future.

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Section: Discussionmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Interpretable Detection of Epiretinal Membrane from Optical Coherence Tomography with Deep Neural Networks

Ayhan

Neubauer

Uzel

et al. 2022

Preprint

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“…Several studies previously described the application of deep learning methods for the diagnosis of epiretinal membranes starting from OCT or fundus images. 27–30…”

Section: Discussionmentioning

confidence: 99%

“…Several studies previously described the application of deep learning methods for the diagnosis of epiretinal membranes starting from OCT or fundus images. [27][28][29][30] Our study proposes a new automated method for prediction of functional outcome 1 year after ERM surgery based on OCT B-scan images. Interestingly, the performances of both the image processing and the machine-based method proposed by our study remained particularly high, despite the inclusion of OCT images from different devices.…”

Section: Discussionmentioning

confidence: 99%

New Artificial Intelligence Analysis for Prediction of Long-Term Visual Improvement After Epiretinal Membrane Surgery

Crincoli

Savastano

et al. 2023

Retina

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Purpose: To predict improvement of best-corrected visual acuity (BCVA) 1 year after pars plana vitrectomy for epiretinal membrane (ERM) using artificial intelligence methods on optical coherence tomography B-scan images.Methods: Four hundred and eleven (411) patients with Stage II ERM were divided in a group improvement (IM) ($15 ETDRS letters of VA recovery) and a group no improvement (N-IM) (,15 letters) according to 1-year VA improvement after 25-G pars plana vitrectomy with internal limiting membrane peeling. Primary outcome was the creation of a deep learning classifier (DLC) based on optical coherence tomography B-scan images for prediction. Secondary outcome was assessment of the influence of various clinical and imaging predictors on BCVA improvement. Inception-ResNet-V2 was trained using standard augmentation techniques. Testing was performed on an external data set. For secondary outcome, B-scan acquisitions were analyzed by graders both before and after fibrillary change processing enhancement.Results: The overall performance of the DLC showed a sensitivity of 87.3% and a specificity of 86.2%. Regression analysis showed a difference in preoperative images prevalence of ectopic inner foveal layer, foveal detachment, ellipsoid zone interruption, cotton wool sign, unprocessed fibrillary changes (odds ratio = 2.75 [confidence interval: 2.49-2.96]), and processed fibrillary changes (odds ratio = 5.42 [confidence interval: 4.81-6.08]), whereas preoperative BCVA and central macular thickness did not differ between groups. Conclusion:The DLC showed high performances in predicting 1-year visual outcome in ERM surgery patients. Fibrillary changes should also be considered as relevant predictors.RETINA 43:173-181, 2023E piretinal membranes (ERMs) are avascular fibro-

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Semi-supervised Learning for Myopic Maculopathy Analysis

Heras

2024

Lecture Notes in Computer Science

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Prediction of Epiretinal Membrane from Retinal Fundus Images Using Deep Learning

Cited by 7 publications

References 18 publications

Interpretable Detection of Epiretinal Membrane from Optical Coherence Tomography with Deep Neural Networks

Interpretable Detection of Epiretinal Membrane from Optical Coherence Tomography with Deep Neural Networks

New Artificial Intelligence Analysis for Prediction of Long-Term Visual Improvement After Epiretinal Membrane Surgery

Semi-supervised Learning for Myopic Maculopathy Analysis

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