Dataset Information

A Deep Learning Algorithm to Quantify Neuroretinal Rim Loss From Optic Disc Photographs.

ABSTRACT: PURPOSE:To train a deep learning (DL) algorithm that quantifies glaucomatous neuroretinal damage on fundus photographs using the minimum rim width relative to Bruch membrane opening (BMO-MRW) from spectral-domain optical coherence tomography (SDOCT). DESIGN:Cross-sectional study. METHODS:A total of 9282 pairs of optic disc photographs and SDOCT optic nerve head scans from 927 eyes of 490 subjects were randomly divided into the validation plus training (80%) and test sets (20%). A DL convolutional neural network was trained to predict the SDOCT BMO-MRW global and sector values when evaluating optic disc photographs. The predictions of the DL network were compared to the actual SDOCT measurements. The area under the receiver operating curve (AUC) was used to evaluate the ability of the network to discriminate glaucomatous visual field loss from normal eyes. RESULTS:The DL predictions of global BMO-MRW from all optic disc photographs in the test set (mean ± standard deviation [SD]: 228.8 ± 63.1 ?m) were highly correlated with the observed values from SDOCT (mean ± SD: 226.0 ± 73.8 ?m) (Pearson's r = 0.88; R2 = 77%; P < .001), with mean absolute error of the predictions of 27.8 ?m. The AUCs for discriminating glaucomatous from healthy eyes with the DL predictions and actual SDOCT global BMO-MRW measurements were 0.945 (95% confidence interval [CI]: 0.874-0.980) and 0.933 (95% CI: 0.856-0.975), respectively (P = .587). CONCLUSIONS:A DL network can be trained to quantify the amount of neuroretinal damage on optic disc photographs using SDOCT BMO-MRW as a reference. This algorithm showed high accuracy for glaucoma detection, and may potentially eliminate the need for human gradings of disc photographs.

SUBMITTER: Thompson AC

PROVIDER: S-EPMC6884088 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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A Deep Learning Algorithm to Quantify Neuroretinal Rim Loss From Optic Disc Photographs.

Thompson Atalie C AC Jammal Alessandro A AA Medeiros Felipe A FA

American journal of ophthalmology 20190126

<h4>Purpose</h4>To train a deep learning (DL) algorithm that quantifies glaucomatous neuroretinal damage on fundus photographs using the minimum rim width relative to Bruch membrane opening (BMO-MRW) from spectral-domain optical coherence tomography (SDOCT).<h4>Design</h4>Cross-sectional study.<h4>Methods</h4>A total of 9282 pairs of optic disc photographs and SDOCT optic nerve head scans from 927 eyes of 490 subjects were randomly divided into the validation plus training (80%) and test sets (2 ...[more]

PMID: 30689990

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Project description:Spectral-domain optical coherence tomography (SDOCT) is a noncontact and noninvasive imaging technology offering three-dimensional (3-D), objective, and quantitative assessment of optic nerve head (ONH) in human eyes in vivo. The image quality of SDOCT scans is crucial for an accurate and reliable interpretation of ONH structure and for further detection of diseases. Traditionally, signal strength (SS) is used as an index to include or exclude SDOCT scans for further analysis. However, it is insufficient to assess other image quality issues such as off-centration, out of registration, missing data, motion artifacts, mirror artifacts, or blurriness, which require specialized knowledge in SDOCT for such assessment. We proposed a deep learning system (DLS) as an automated tool for filtering out ungradable SDOCT volumes. In total, 5599 SDOCT ONH volumes were collected for training (80%) and primary validation (20%). Other 711 and 298 volumes from two independent datasets, respectively, were used for external validation. An SDOCT volume was labeled as ungradable when SS was <5 or when any artifacts influenced the measurement circle or >25% of the peripheral area. Artifacts included (1) off-centration, (2) out of registration, (3) missing signal, (4) motion artifacts, (5) mirror artifacts, and (6) blurriness. An SDOCT volume was labeled as gradable when SS was ?5 , and there was an absence of any artifacts or artifacts only influenced <25% peripheral area but not the retinal nerve fiber layer calculation circle. We developed and validated a 3-D DLS based on squeeze-and-excitation ResNeXt blocks and experimented with different training strategies. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were calculated to evaluate the performance. Heatmaps were generated by gradient-weighted class activation map. Our findings show that the presented DLS achieved a good performance in both primary and external validations, which could potentially increase the efficiency and accuracy of SDOCT volumetric scans quality control by filtering out ungradable ones automatically.

Project description:ImportanceA deep learning system (DLS) that could automatically detect glaucomatous optic neuropathy (GON) with high sensitivity and specificity could expedite screening for GON.ObjectiveTo establish a DLS for detection of GON using retinal fundus images and glaucoma diagnosis with convoluted neural networks (GD-CNN) that has the ability to be generalized across populations.Design, setting, and participantsIn this cross-sectional study, a DLS for the classification of GON was developed for automated classification of GON using retinal fundus images obtained from the Chinese Glaucoma Study Alliance, the Handan Eye Study, and online databases. The researchers selected 241 032 images were selected as the training data set. The images were entered into the databases on June 9, 2009, obtained on July 11, 2018, and analyses were performed on December 15, 2018. The generalization of the DLS was tested in several validation data sets, which allowed assessment of the DLS in a clinical setting without exclusions, testing against variable image quality based on fundus photographs obtained from websites, evaluation in a population-based study that reflects a natural distribution of patients with glaucoma within the cohort and an additive data set that has a diverse ethnic distribution. An online learning system was established to transfer the trained and validated DLS to generalize the results with fundus images from new sources. To better understand the DLS decision-making process, a prediction visualization test was performed that identified regions of the fundus images utilized by the DLS for diagnosis.ExposuresUse of a deep learning system.Main outcomes and measuresArea under the receiver operating characteristics curve (AUC), sensitivity and specificity for DLS with reference to professional graders.ResultsFrom a total of 274 413 fundus images initially obtained from CGSA, 269 601 images passed initial image quality review and were graded for GON. A total of 241 032 images (definite GON 29 865 [12.4%], probable GON 11 046 [4.6%], unlikely GON 200 121 [83%]) from 68 013 patients were selected using random sampling to train the GD-CNN model. Validation and evaluation of the GD-CNN model was assessed using the remaining 28 569 images from CGSA. The AUC of the GD-CNN model in primary local validation data sets was 0.996 (95% CI, 0.995-0.998), with sensitivity of 96.2% and specificity of 97.7%. The most common reason for both false-negative and false-positive grading by GD-CNN (51 of 119 [46.3%] and 191 of 588 [32.3%]) and manual grading (50 of 113 [44.2%] and 183 of 538 [34.0%]) was pathologic or high myopia.Conclusions and relevanceApplication of GD-CNN to fundus images from different settings and varying image quality demonstrated a high sensitivity, specificity, and generalizability for detecting GON. These findings suggest that automated DLS could enhance current screening programs in a cost-effective and time-efficient manner.

Dataset Information

A Deep Learning Algorithm to Quantify Neuroretinal Rim Loss From Optic Disc Photographs.

Publications

A Deep Learning Algorithm to Quantify Neuroretinal Rim Loss From Optic Disc Photographs.

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