Dataset Information

Characterizing right-angled vessel in macular telangiectasia type 2 with structural optical coherence tomography.

ABSTRACT: We investigated the structural findings on spectral-domain optical coherence tomography (SD-OCT) related to the presence of right-angled vessels (RAV) in patients with macular telangiectasia (MacTel) type 2 with severity 3 in Korea. A retrospective multicenter cross-sectional study was conducted in six tertiary hospitals in Korea; the study included 116 MacTel type 2 eyes with severity 3. The SD-OCT findings were compared between eyes with RAV on fundus photography or fluorescein angiography and those without RAV. Logistic regression was performed to determine factors associated with the presence of RAV. Fifty eyes presented with RAV and 61 eyes without RAV. More eyes presented with only inner retinal (IR) cavities on SD-OCT among eyes without RAV than among those with RAV (P < 0.001). However, eyes with RAV presented with IR disorganization, outer retinal (OR) cavity, and ellipsoid zone (EZ) disruption more frequently than eyes without RAV did (all P < 0.001). These SD-OCT findings were significantly associated with the presence of RAV. The presence of RAV was closely related to IR disorganization, OR cavities, and EZ disruption on SD-OCT. These findings suggest an advanced phase of MacTel type 2.

SUBMITTER: Chung YR

PROVIDER: S-EPMC8387352 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Characterizing right-angled vessel in macular telangiectasia type 2 with structural optical coherence tomography.

Chung Yoo-Ri YR Kim Young Ho YH Oh Jaeryung J Kim Seong-Woo SW Lee Christopher Seungkyu CS Yun Cheolmin C Lee Boram B Ahn So Min SM Choi Eun Young EY Jang Sungmin S Lee Kihwang K

Scientific reports 20210825 1

We investigated the structural findings on spectral-domain optical coherence tomography (SD-OCT) related to the presence of right-angled vessels (RAV) in patients with macular telangiectasia (MacTel) type 2 with severity 3 in Korea. A retrospective multicenter cross-sectional study was conducted in six tertiary hospitals in Korea; the study included 116 MacTel type 2 eyes with severity 3. The SD-OCT findings were compared between eyes with RAV on fundus photography or fluorescein angiography and ...[more]

PMID: 34433894

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Project description:ImportanceDeep learning image analysis often depends on large, labeled datasets, which are difficult to obtain for rare diseases.ObjectiveTo develop a self-supervised approach for automated classification of macular telangiectasia type 2 (MacTel) on optical coherence tomography (OCT) with limited labeled data.Design, setting, and participantsThis was a retrospective comparative study. OCT images from May 2014 to May 2019 were collected by the Lowy Medical Research Institute, La Jolla, California, and the University of Washington, Seattle, from January 2016 to October 2022. Clinical diagnoses of patients with and without MacTel were confirmed by retina specialists. Data were analyzed from January to September 2023.ExposuresTwo convolutional neural networks were pretrained using the Bootstrap Your Own Latent algorithm on unlabeled training data and fine-tuned with labeled training data to predict MacTel (self-supervised method). ResNet18 and ResNet50 models were also trained using all labeled data (supervised method).Main outcomes and measuresThe ground truth yes vs no MacTel diagnosis is determined by retinal specialists based on spectral-domain OCT. The models' predictions were compared against human graders using accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under precision recall curve (AUPRC), and area under the receiver operating characteristic curve (AUROC). Uniform manifold approximation and projection was performed for dimension reduction and GradCAM visualizations for supervised and self-supervised methods.ResultsA total of 2636 OCT scans from 780 patients with MacTel and 131 patients without MacTel were included from the MacTel Project (mean [SD] age, 60.8 [11.7] years; 63.8% female), and another 2564 from 1769 patients without MacTel from the University of Washington (mean [SD] age, 61.2 [18.1] years; 53.4% female). The self-supervised approach fine-tuned on 100% of the labeled training data with ResNet50 as the feature extractor performed the best, achieving an AUPRC of 0.971 (95% CI, 0.969-0.972), an AUROC of 0.970 (95% CI, 0.970-0.973), accuracy of 0.898%, sensitivity of 0.898, specificity of 0.949, PPV of 0.935, and NPV of 0.919. With only 419 OCT volumes (185 MacTel patients in 10% of labeled training dataset), the ResNet18 self-supervised model achieved comparable performance, with an AUPRC of 0.958 (95% CI, 0.957-0.960), an AUROC of 0.966 (95% CI, 0.964-0.967), and accuracy, sensitivity, specificity, PPV, and NPV of 90.2%, 0.884, 0.916, 0.896, and 0.906, respectively. The self-supervised models showed better agreement with the more experienced human expert graders.Conclusions and relevanceThe findings suggest that self-supervised learning may improve the accuracy of automated MacTel vs non-MacTel binary classification on OCT with limited labeled training data, and these approaches may be applicable to other rare diseases, although further research is warranted.

Project description:ImportanceAs currently used, microperimetry is a burdensome clinical testing modality for testing retinal sensitivity requiring long testing times and trained technicians.ObjectiveTo create a deep-learning network that could directly estimate function from structure de novo to provide an en face high-resolution map of estimated retinal sensitivity.Design, setting, and participantsA cross-sectional imaging study using data collected between January 1, 2016, and November 30, 2017, from the Natural History Observation and Registry of macular telangiectasia type 2 (MacTel) evaluated 38 participants with confirmed MacTel from 2 centers.Main outcomes and measuresMean absolute error of estimated compared with observed retinal sensitivity. Observed retinal sensitivity was obtained with fundus-controlled perimetry (microperimetry). Estimates of retinal sensitivity were made with deep-learning models that learned on superpositions of high-resolution optical coherence tomography (OCT) scans and microperimetry results. Those predictions were used to create high-density en face sensitivity maps of the macula. Training, validation, and test sets were segregated at the patient level.ResultsA total of 2499 microperimetry sensitivities were mapped onto 1708 OCT B-scans from 63 eyes of 38 patients (mean [SD] age, 74.3 [9.7] years; 15 men [39.5%]). The numbers of examples for our algorithm were 67 899 (103 053 after data augmentation) for training, 1695 for validation, and 1212 for testing. Mean absolute error results were 4.51 dB (95% CI, 4.36-4.65 dB) when using linear regression and 3.66 dB (95% CI, 3.53-3.78 dB) when using the LeNet model. Using a 49.9 million-variable deep-learning model, a mean absolute error of 3.36 dB (95% CI, 3.25-3.48 dB) of retinal sensitivity for validation and test was achieved. Correlation showed a high degree of agreement (Pearson correlation r = 0.78). By paired Wilcoxon rank sum test, our model significantly outperformed these 2 baseline models (P < .001).Conclusions and relevanceHigh-resolution en face maps of estimated retinal sensitivities were created in eyes with MacTel. The maps were of unequalled resolution compared with microperimetry and were able to correctly delineate functionally healthy and impaired retina. This model may be useful to monitor structural and functional disease progression and has potential as an objective surrogate outcome measure in investigational trials.

Project description:Importance:Whether optical coherence tomography angiography (OCT-A) outperforms OCT to detect glaucoma remains inconclusive. Objective:To compare (1) the diagnostic performance for detection of glaucoma and (2) the structure-function association between inner macular vessel density and inner macular thickness. Design, Setting, and Participants:This cross-sectional study included 115 patients with glaucoma and 35 healthy individuals for measurements of retinal thickness and retinal vessel density, segmented between the anterior boundary of internal limiting membrane and the posterior boundary of the inner plexiform layer, over the 3 × 3-mm2 macula using swept-source OCT. All participants were Chinese. Visual sensitivity corresponding to the 3 × 3-mm2 macular region was expressed in unlogged 1/lambert for investigation of the structure-function associations. Diagnostic performance was evaluated with area under the receiver operating characteristic curves (AUCs). The study was conducted between January 12, 2016, and December 12, 2016. Main Outcomes and Measures:Area under the receiver operating characteristic curve and R2 analysis. Results:Of the 115 patients with glaucoma, 42 (36.5%) were women (mean [SD] age, 53.5 [13.4] years); of the 35 individuals with healthy eyes, 25 (71.4%) were women (age, 60.6 [5.9] years). Inner macular vessel density and thickness were 4.3% (95% CI, 2.4%-6.1%) and 21.1 μm (95% CI, 17.4-24.9 μm) smaller, respectively, in eyes with glaucoma compared with healthy eyes. The AUC of mean inner macular thickness for glaucoma detection was greater than that of mean inner macular vessel density (difference, 0.17; 95% CI, 0.01-0.31; P = .03). At 90% specificity, the sensitivity of mean inner macular thicknesses for detection of glaucoma was greater than that of mean inner macular vessel densities (difference, 29.2%; 95% CI, 11.5%-64.6%; P = .02). The strength of the structure-function association was stronger for mean inner macular thickness than mean inner macular vessel density in the linear (difference in R2 = 0.38; 95% CI, 0.22-0.54; P < .001) and nonlinear (difference in R2 = 0.36; 95% CI, 0.21-0.51; P < .001) regression models. Conclusions and Relevance:In this study, OCT measurement of inner macular thickness shows a higher diagnostic performance to detect glaucoma and a stronger structure-function association than the currently used OCT-A measurement of inner macular vessel density. These findings may suggest that OCT-A of the macula has a limited role in the diagnostic evaluation of glaucoma.

Project description:PurposeThe purpose of this study was to investigate the diurnal variation in peripapillary and macular vessel density (VD) measurements using optical coherence tomographic angiography (OCT-A) and its correlation to intraocular pressure (IOP) changes in glaucoma patients.MethodsProspective, observational cross-sectional study including 37 patients (74 eyes; age, 63.8±12.9 y) with open-angle glaucoma. OCT-A imaging and IOP measurements were performed at 08:00, 11:00, 14:00, and 16:00 timepoints on a single day. At each timepoint, 2 scan protocols were used to generate 3-dimensional en face OCT angiograms: 4.5×4.5-mm scan centered on the optic nerve head and 6×6-mm scan centered on the fovea. For each scan mode, the "radial peripapillary capillary" segment, composed of the vasculature of the retinal nerve fiber layer and ganglion cell layer, was calculated. Two trained readers reviewed OCT-A image quality. Only scans with signal strength intensity (SSI) higher than 46 and without image artifacts interfering with measurements were included. Variation in VD measurements assessed using analysis of variance (ANOVA) and the association between VD and IOP changes assessed using linear mixed modeling methods.ResultsThe optic nerve head and peripapillary VD measurements at 14:00 and 16:00 timepoints were greater than the measurements at 08:00 and 11:00 timepoints. The 14:00 and 16:00 VD measurements were statistically significantly greater (P<0.05) than the 08:00 measurements for the whole en face (50.1% and 50.1% vs. 49.4%), inside disc (50.6% and 50.5% vs. 49.6%), and average peripapillary (58.2% and 58.5% vs. 57.5%) VDs. The macular VD measurements at the 14:00 timepoint were greater than the measurements at 08:00 and 11:00 timepoints. Changes in VD were significantly associated with changes in SSI but not IOP.ConclusionsDiurnal changes in OCT-A-measured VD in glaucoma patients were small and clinically insignificant. These changes were not associated with IOP changes.

Dataset Information

Characterizing right-angled vessel in macular telangiectasia type 2 with structural optical coherence tomography.

Publications

Characterizing right-angled vessel in macular telangiectasia type 2 with structural optical coherence tomography.

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