scholarly journals Deep learning based models to study the effect of glaucoma genes on angle dysgenesis in-vivo

2021 ◽  
Author(s):  
Viney Gupta ◽  
Shweta Birla ◽  
Toshit Varshney ◽  
Bindu I Somarajan ◽  
Shikha Gupta ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Trabecular Meshwork ◽  
External Validation ◽  
Anterior Segment ◽  
Gene Mutations ◽  
Validation Dataset ◽  
Healthy Controls ◽  
Optical Coherence ◽  
Learning Models

Abstract Objective: To predict the presence of Angle Dysgenesis on Anterior Segment Optical Coherence Tomography (ADoA) using deep learning and to correlate ADoA with mutations in known glaucoma genes. Design: A cross-sectional observational study. Participants: Eight hundred, high definition anterior segment optical coherence tomography (ASOCT) B-scans were included, out of which 340 images (One scan per eye) were used to build the machine learning (ML) model and the rest were used for validation of ADoA. Out of 340 images, 170 scans included PCG (n=27), JOAG (n=86) and POAG (n=57) eyes and the rest were controls. The genetic validation dataset consisted of another 393 images of patients with known mutations compared with 320 images of healthy controls Methods: ADoA was defined as the absence of Schlemm's canal(SC), the presence of extensive hyper-reflectivity over the region of trabecular meshwork or a hyper-reflective membrane (HM) over the region of the trabecular meshwork. Deep learning was used to classify a given ASOCT image as either having angle dysgenesis or not. ADoA was then specifically looked for, on ASOCT images of patients with mutations in the known genes for glaucoma (MYOC, CYP1B1, FOXC1 and LTBP2). Main Outcome measures: Using Deep learning to identify ADoA in patients with known gene mutations. Results: Our three optimized deep learning models showed an accuracy > 95%, specificity >97% and sensitivity >96% in detecting angle dysgenesis on ASOCT in the internal test dataset. The area under receiver operating characteristic (AUROC) curve, based on the external validation cohort were 0.91 (95% CI, 0.88 to 0.95), 0.80 (95% CI, 0.75 to 0.86) and 0.86 (95% CI, 0.80 to 0.91) for the three models. Amongst the patients with known gene mutations, ADoA was observed among all the patients with MYOC mutations, as it was also observed among those with CYP1B1, FOXC1 and with LTBP2 mutations compared to only 5% of those healthy controls (with no glaucoma mutations). Conclusions: Three deep learning models were developed for a consensus-based outcome to objectively identify ADoA among glaucoma patients. All patients with MYOC mutations had ADoA as predicted by the models.

scholarly journals Development and Validation of a Deep Learning System for Diagnosing Glaucoma Using Optical Coherence Tomography

2020 ◽  
Vol 9 (7) ◽  
pp. 2167
Author(s):  
Ko Eun Kim ◽  
Joon Mo Kim ◽  
Ji Eun Song ◽  
Changwon Kee ◽  
Jong Chul Han ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
External Validation ◽  
Learning System ◽  
Validation Dataset ◽  
Inner Plexiform Layer ◽  
Optical Coherence ◽  
Diagnostic Ability ◽  
Rnfl Thickness ◽  
Validation Set

This study aimed to develop and validate a deep learning system for diagnosing glaucoma using optical coherence tomography (OCT). A training set of 1822 eyes (332 control, 1490 glaucoma) with 7288 OCT images, an internal validation set of 425 eyes (104 control, 321 glaucoma) with 1700 images, and an external validation set of 355 eyes (108 control, 247 glaucoma) with 1420 images were included. Deviation and thickness maps of retinal nerve fiber layer (RNFL) and ganglion cell–inner plexiform layer (GCIPL) analyses were used to develop the deep learning system for glaucoma diagnosis based on the visual geometry group deep convolutional neural network (VGG-19) model. The diagnostic abilities of deep learning models using different OCT maps were evaluated, and the best model was compared with the diagnostic results produced by two glaucoma specialists. The glaucoma-diagnostic ability was highest when the deep learning system used the RNFL thickness map alone (area under the receiver operating characteristic curve (AUROC) 0.987), followed by the RNFL deviation map (AUROC 0.974), the GCIPL thickness map (AUROC 0.966), and the GCIPL deviation map (AUROC 0.903). Among combination sets, use of the RNFL and GCIPL deviation map showed the highest diagnostic ability, showing similar results when tested via an external validation dataset. The inclusion of the axial length did not significantly affect the diagnostic performance of the deep learning system. The location of glaucomatous damage showed generally high level of agreement between the heatmap and the diagnosis of glaucoma specialists, with 90.0% agreement when using the RNFL thickness map and 88.0% when using the GCIPL thickness map. In conclusion, our deep learning system showed high glaucoma-diagnostic abilities using OCT thickness and deviation maps. It also showed detection patterns similar to those of glaucoma specialists, showing promising results for future clinical application as an interpretable computer-aided diagnosis.

scholarly journals Foveal avascular zone segmentation in optical coherence tomography angiography images using a deep learning approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Reza Mirshahi ◽  
Pasha Anvari ◽  
Hamid Riazi-Esfahani ◽  
Mahsa Sardarinia ◽  
Masood Naseripour ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Healthy Subjects ◽  
Optical Coherence Tomography Angiography ◽  
Validation Dataset ◽  
Diabetic Patients ◽  
Dice Similarity Coefficient ◽  
Optical Coherence ◽  
Avascular Zone

AbstractThe purpose of this study was to introduce a new deep learning (DL) model for segmentation of the fovea avascular zone (FAZ) in en face optical coherence tomography angiography (OCTA) and compare the results with those of the device’s built-in software and manual measurements in healthy subjects and diabetic patients. In this retrospective study, FAZ borders were delineated in the inner retinal slab of 3 × 3 enface OCTA images of 131 eyes of 88 diabetic patients and 32 eyes of 18 healthy subjects. To train a deep convolutional neural network (CNN) model, 126 enface OCTA images (104 eyes with diabetic retinopathy and 22 normal eyes) were used as training/validation dataset. Then, the accuracy of the model was evaluated using a dataset consisting of OCTA images of 10 normal eyes and 27 eyes with diabetic retinopathy. The CNN model was based on Detectron2, an open-source modular object detection library. In addition, automated FAZ measurements were conducted using the device’s built-in commercial software, and manual FAZ delineation was performed using ImageJ software. Bland–Altman analysis was used to show 95% limit of agreement (95% LoA) between different methods. The mean dice similarity coefficient of the DL model was 0.94 ± 0.04 in the testing dataset. There was excellent agreement between automated, DL model and manual measurements of FAZ in healthy subjects (95% LoA of − 0.005 to 0.026 mm2 between automated and manual measurement and 0.000 to 0.009 mm2 between DL and manual FAZ area). In diabetic eyes, the agreement between DL and manual measurements was excellent (95% LoA of − 0.063 to 0.095), however, there was a poor agreement between the automated and manual method (95% LoA of − 0.186 to 0.331). The presence of diabetic macular edema and intraretinal cysts at the fovea were associated with erroneous FAZ measurements by the device’s built-in software. In conclusion, the DL model showed an excellent accuracy in detection of FAZ border in enfaces OCTA images of both diabetic patients and healthy subjects. The DL and manual measurements outperformed the automated measurements of the built-in software.

scholarly journals Anterior Segment Optical Coherence Tomography Signs of Local Dilatation Effect of a Micro-Stent on Schlemm’s Canal

2018 ◽  
Vol 10 (2) ◽  
pp. 184-187
Author(s):  
Kevin Gillmann ◽  
Giorgio Enrico Bravetti ◽  
Kaweh Mansouri ◽  
André Mermoud
Keyword(s):  
Optical Coherence Tomography ◽  
Trabecular Meshwork ◽  
Anterior Segment ◽  
Optical Coherence ◽  
Schlemm’S Canal ◽  
New Device ◽  
Schlemm's Canal ◽  
Canal Diameter ◽  
Trabecular Bypass

Introduction: The iStent inject® (Glaukos Corporation, CA, USA) is a relatively new device designed to be implanted ab-interno through the trabecular meshwork. This is, to the best of our knowledge, the first in-vivo description of a trabecular bypass device visualised with anterior segment optical coherence tomography (AS-OCT), and report of its structural effect on Schlemm’s canal. Case Report: A 74 year-old female patient suffering from long-standing primary open-angle glaucoma and nuclear sclerosis underwent cataract surgery combined with the implantation of two iStent injects®. Surgery was uncomplicated and achieved intraocular pressure (-1 mmHg) and medication (-2 molecules) reduction at 6 months. Under AS-OCT (Spectralis OCT, Heidelberg Engineering AG, Germany) the stent appears as a 300 μm long hyper reflective hollow device within the trabecular meshwork. Approximately a third of it protruded into the anterior chamber. Profound OCT signal loss was notable within the shadow of the device. A second AS-OCT section 500 μm beside the microstent shows a markedly dilated Schlemm’s canal, with a major diameter of 390 μm. Discussions: This report confirms that AS-OCT is a suitable technique to assess microstent positioning, and provides a first report on the in-vivo appearance of a functioning stent. It also indicates that iStent injects® could have a tangible effect on adjacent portions of Schlemm’s canal with, in this case, a 220% increase in canal diameter compared to the observed average (122 μm). This suggests the IOP-lowering effect of trabecular bypass devices could rely on a  dual mechanism involving Schlemm’s canal dilatation.

scholarly journals Deep Learning for Anterior Segment Optical Coherence Tomography to Predict the Presence of Plateau Iris

2021 ◽  
Vol 10 (1) ◽  
pp. 7
Author(s):  
Boonsong Wanichwecharungruang ◽  
Natsuda Kaothanthong ◽  
Warisara Pattanapongpaiboon ◽  
Pantid Chantangphol ◽  
Kasem Seresirikachorn ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Anterior Segment ◽  
Optical Coherence

scholarly journals Keratoconus detection using deep learning of colour-coded maps with anterior segment optical coherence tomography: a diagnostic accuracy study

BMJ Open ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. e031313 ◽  
Author(s):  
Kazutaka Kamiya ◽  
Yuji Ayatsuka ◽  
Yudai Kato ◽  
Fusako Fujimura ◽  
Masahide Takahashi ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Diagnostic Accuracy ◽  
Anterior Segment ◽  
Optical Coherence ◽  
Diagnostic Accuracy Study ◽  
Clinical Trial Registration ◽  
Swept Source ◽  
Refractive Power ◽  
Accuracy Study

ObjectiveTo evaluate the diagnostic accuracy of keratoconus using deep learning of the colour-coded maps measured with the swept-source anterior segment optical coherence tomography (AS-OCT).DesignA diagnostic accuracy study.SettingA single-centre study.ParticipantsA total of 304 keratoconic eyes (grade 1 (108 eyes), 2 (75 eyes), 3 (42 eyes) and 4 (79 eyes)) according to the Amsler-Krumeich classification, and 239 age-matched healthy eyes.Main outcome measuresThe diagnostic accuracy of keratoconus using deep learning of six colour-coded maps (anterior elevation, anterior curvature, posterior elevation, posterior curvature, total refractive power and pachymetry map).ResultsDeep learning of the arithmetical mean output data of these six maps showed an accuracy of 0.991 in discriminating between normal and keratoconic eyes. For single map analysis, posterior elevation map (0.993) showed the highest accuracy, followed by posterior curvature map (0.991), anterior elevation map (0.983), corneal pachymetry map (0.982), total refractive power map (0.978) and anterior curvature map (0.976), in discriminating between normal and keratoconic eyes. This deep learning also showed an accuracy of 0.874 in classifying the stage of the disease. Posterior curvature map (0.869) showed the highest accuracy, followed by corneal pachymetry map (0.845), anterior curvature map (0.836), total refractive power map (0.836), posterior elevation map (0.829) and anterior elevation map (0.820), in classifying the stage.ConclusionsDeep learning using the colour-coded maps obtained by the AS-OCT effectively discriminates keratoconus from normal corneas, and furthermore classifies the grade of the disease. It is suggested that this will become an aid for improving the diagnostic accuracy of keratoconus in daily practice.Clinical trial registration number000034587.

scholarly journals A Deep Learning System for Automated Angle-Closure Detection in Anterior Segment Optical Coherence Tomography Images

2019 ◽  
Vol 203 ◽  
pp. 37-45 ◽  
Author(s):  
Huazhu Fu ◽  
Mani Baskaran ◽  
Yanwu Xu ◽  
Stephen Lin ◽  
Damon Wing Kee Wong ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Anterior Segment ◽  
Learning System ◽  
Angle Closure ◽  
Optical Coherence

scholarly journals Detection of features associated with neovascular age-related macular degeneration in ethnically distinct data sets by an optical coherence tomography: trained deep learning algorithm

2020 ◽  
pp. bjophthalmol-2020-316984
Author(s):  
Tyler Hyungtaek Rim ◽  
Aaron Y Lee ◽  
Daniel S Ting ◽  
Kelvin Teo ◽  
Bjorn Kaijun Betzler ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Macular Degeneration ◽  
External Validation ◽  
Age Related Macular Degeneration ◽  
Data Sets ◽  
Optical Coherence ◽  
Data Set ◽  
Saliency Maps ◽  
Age Related

BackgroundThe ability of deep learning (DL) algorithms to identify eyes with neovascular age-related macular degeneration (nAMD) from optical coherence tomography (OCT) scans has been previously established. We herewith evaluate the ability of a DL model, showing excellent performance on a Korean data set, to generalse onto an American data set despite ethnic differences. In addition, expert graders were surveyed to verify if the DL model was appropriately identifying lesions indicative of nAMD on the OCT scans.MethodsModel development data set—12 247 OCT scans from South Korea; external validation data set—91 509 OCT scans from Washington, USA. In both data sets, normal eyes or eyes with nAMD were included. After internal testing, the algorithm was sent to the University of Washington, USA, for external validation. Area under the receiver operating characteristic curve (AUC) and precision–recall curve (AUPRC) were calculated. For model explanation, saliency maps were generated using Guided GradCAM.ResultsOn external validation, AUC and AUPRC remained high at 0.952 (95% CI 0.942 to 0.962) and 0.891 (95% CI 0.875 to 0.908) at the individual level. Saliency maps showed that in normal OCT scans, the fovea was the main area of interest; in nAMD OCT scans, the appropriate pathological features were areas of model interest. Survey of 10 retina specialists confirmed this.ConclusionOur DL algorithm exhibited high performance for nAMD identification in a Korean population, and generalised well to an ethnically distinct, American population. The model correctly focused on the differences within the macular area to extract features associated with nAMD.

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