Development and validation of a computer-aided diagnostic tool to screen for age-related macular degeneration by optical coherence tomography

Serrano‐Aguilar, Pedro; Abreu, Rodrigo B.; Antón-Canalís, Luis; Guerra-Artal, Cayetano; Ramallo-Fariña, Yolanda; Gómez-Ulla, F; Nadal, Jennifer

doi:10.1136/bjophthalmol-2011-300660

Cited by 8 publications

(8 citation statements)

References 22 publications

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“…Typically, we have two main approaches for automated AMD diagnosis based on SD-OCT. One of the approaches involves the following steps to be effectively addressed: (a) obtaining relevant biomedical characteristics for the differentiation between healthy and compromised retinas; (b) use of classifiers to accurately determine the presence or absence of the disease; and (c) validation of the method for generating reliable results from a properly classified image database [ 4 , 5 , 7 – 13 ]. This approach has the advantage of constructing robust classifiers, based on fixed mathematical models that can characterize well a specific pathology.…”

Section: Introductionmentioning

confidence: 99%

Semivariogram and Semimadogram functions as descriptors for AMD diagnosis on SD-OCT topographic maps using Support Vector Machine

et al. 2018

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BackgroundAge-related macular degeneration (AMD) is a degenerative ocular disease that develops by the formation of drusen in the macula region leading to blindness. This condition can be detected automatically by automated image processing techniques applied in spectral domain optical coherence tomography (SD-OCT) volumes. The most common approach is the individualized analysis of each slice (B-Scan) of the SD-OCT volumes. However, it ends up losing the correlation between pixels of neighboring slices. The retina representation by topographic maps reveals the similarity of these structures with geographic relief maps, which can be represented by geostatistical descriptors. In this paper, we present a methodology based on geostatistical functions for the automatic diagnosis of AMD in SD-OCT.MethodsThe proposed methodology is based on the construction of a topographic map of the macular region. Over the topographic map, we compute geostatistical features using semivariogram and semimadogram functions as texture descriptors. The extracted descriptors are then used as input for a Support Vector Machine classifier.ResultsFor training of the classifier and tests, a database composed of 384 OCT exams (269 volumes of eyes exhibiting AMD and 115 control volumes) with layers segmented and validated by specialists were used. The best classification model, validated with cross-validation k-fold, achieved an accuracy of 95.2% and an AUROC of 0.989.ConclusionThe presented methodology exclusively uses geostatistical descriptors for the diagnosis of AMD in SD-OCT images of the macular region. The results are promising and the methodology is competitive considering previous results published in literature.

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Section: Introductionmentioning

confidence: 99%

Semivariogram and Semimadogram functions as descriptors for AMD diagnosis on SD-OCT topographic maps using Support Vector Machine

et al. 2018

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“…In the current study, we wanted to compare the accuracy of different CNN designs, trained on the same dataset, to [30][31][32][33][34][35][36]. Furthermore, the majority of these studies have been based on single transverse OCT image (cross section) taken through the fovea [23,[37][38][39][40]. Although such an approach, training a CNN on a single OCT image, can achieve impressive results, this approach is flawed as a single transverse image will only sample a very limited part of the macula and relies on the pathology being present within the scan analysed.…”

Section: Discussionmentioning

confidence: 99%

Multimodal Retinal Image Analysis via Deep Learning for the Diagnosis of Intermediate Dry Age-Related Macular Degeneration: A Feasibility Study

Vaghefi

Hill

Kersten

et al. 2020

Journal of Ophthalmology

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Background and Objective. To determine if using a multi-input deep learning approach in the image analysis of optical coherence tomography (OCT), OCT angiography (OCT-A), and colour fundus photographs increases the accuracy of a CNN to diagnose intermediate dry age-related macular degeneration (AMD). Patients and Methods. Seventy-five participants were recruited and divided into three cohorts: young healthy (YH), old healthy (OH), and patients with intermediate dry AMD. Colour fundus photography, OCT, and OCT-A scans were performed. The convolutional neural network (CNN) was trained on multiple image modalities at the same time. Results. The CNN trained using OCT alone showed a diagnostic accuracy of 94%, whilst the OCT-A trained CNN resulted in an accuracy of 91%. When multiple modalities were combined, the CNN accuracy increased to 96% in the AMD cohort. Conclusions. Here we demonstrate that superior diagnostic accuracy can be achieved when deep learning is combined with multimodal image analysis.

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“…Classification is also available for other AMD stages: CAD tools are capable of distinguishing between healthy and early AMD 33 and healthy and exudative AMD (Schlegl T, et al IOVS 2015;56: ARVO E-abstract 5920). 64,67 A classifier able to differentiate early from advanced AMD is the next step. There is already one algorithm, which partially fulfills this and can discriminate between DME and early AMD.…”

Section: Discussionmentioning

confidence: 99%

Algorithms for the Automated Analysis of Age-Related Macular Degeneration Biomarkers on Optical Coherence Tomography: A Systematic Review

Wintergerst

Schultz

Birtel

et al. 2017

Trans. Vis. Sci. Tech.

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PurposeTo assess the quality of optical coherence tomography (OCT) grading algorithms for retinal biomarkers of age-related macular degeneration (AMD).MethodsFollowing a systematic review of the literature data on detection and quantification of AMD retinal biomarkers by available algorithms were extracted and descriptively synthesized. Algorithm quality was assessed using a modified version of the Quality Assessment of Diagnostic Accuracy Studies 2 checklist with a focus on accuracy against established reference standards and risk of bias.ResultsThirty five studies reporting computer-aided diagnosis (CAD) tools for qualitative analysis or algorithms for quantitative analysis were identified. Compared with manual assessment in reference standards correlation coefficients ranged from 0.54 to 0.97 for drusen, 0.80 to 0.98 for geographic atrophy (GA), and 0.30 to 0.98 for intra- or subretinal fluid and pigment epithelial detachment (PED) detection by automated algorithms. CAD tools achieved area under the curve (AUC) values of 0.94 to 0.99, sensitivity of 0.90 to 1.00, and specificity of 0.89 to 0.92.ConclusionsAutomated analysis of AMD biomarkers on OCT is promising. However, most of the algorithm validation was performed in preselected patients, exhibiting the targeted biomarker only. In addition, type and quality of reported algorithm validation varied substantially.Translational RelevanceThe development of algorithms for combined, simultaneous analysis of multiple AMD biomarkers including AMD staging and the agreement on standardized validation procedures would be of considerable translational value for the clinician and the clinical researcher.

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Development and validation of a computer-aided diagnostic tool to screen for age-related macular degeneration by optical coherence tomography

Cited by 8 publications

References 22 publications

Semivariogram and Semimadogram functions as descriptors for AMD diagnosis on SD-OCT topographic maps using Support Vector Machine

Semivariogram and Semimadogram functions as descriptors for AMD diagnosis on SD-OCT topographic maps using Support Vector Machine

Multimodal Retinal Image Analysis via Deep Learning for the Diagnosis of Intermediate Dry Age-Related Macular Degeneration: A Feasibility Study

Algorithms for the Automated Analysis of Age-Related Macular Degeneration Biomarkers on Optical Coherence Tomography: A Systematic Review

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