Macular Telangiectasia Type 2

Chew, Emily Y.; Clemons, Traci E; Sallo, Ferenc B; Pauleikhoff, Daniel; Leung, Irene; Jaffe, Glenn J.; Heeren, Tjebo; Egan, Catherine; Issa, Peter Charbel; Balaskas, Konstantinos; Holz, Frank G.; Gaudric, Alain; Bird, Alan C.; Friedlander, Martin

doi:10.1016/j.xops.2022.100261

Cited by 10 publications

(2 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An EZ loss was observed in all cases at the time of SRNV onset. Before their progression to SRNVs, one or more ORNV tracts with ORHR with (four eyes) or without (four eyes) pigment (stages 4 and 5 of the recent MacTel classification 19 ) were observed (Figures 2 and 3). In these eight eyes, the mean VA decreased from 20/40 to 20/50.…”

Section: Resultsmentioning

confidence: 99%

From Outer Retinal Neovacularization to Exudative Subretinal Neovascularization in Macular Telangiectasia Type 2

Krivosic,

Zureik,

Mph

et al. 2024

Retina

Self Cite

View full text Add to dashboard Cite

Purpose: To describe the progression from outer retinal neovascularization (ORNV) to exudative subretinal new vessels (SRNV) in idiopathic macular telangiectasia type 2 (MacTel2) Patients and Methods: A total of 135 patients (270 eyes) imaged with optical coherence tomography angiography (OCTA) were included. Main Outcome Measures: Ellipsoid zone (EZ) loss, outer retinal hyperreflectivity (ORHR), ORNV, and SRNV. ORNV was defined as a flow signal passing through the outer plexiform layer, with or without vertical linear ORHR on the OCT B-scan. SRNV were defined as an abnormal capillary network with a peripheral anastomotic arcade seen on en face OCTA, and a convex hyperreflectivity at the retinal pigment epithelium. Results: SRNV were observed in 38/270 eyes (14%). SRNV were at a fibrotic stage in 24/38 eyes, at an exudative stage in 6/38 eyes, and a progression from ORNV to SRNV was documented in 8/38 eyes. All cases showed an EZ loss. In 7 eyes (2.5%), SRNV were also associated with subepithelial neovascularization. No retinochoroidal anastomosis was detected. The visual acuity dropped when SRNV were present. Conclusion: In this case series, SRNV were found in 14% of eyes. In all cases, they were associated with an EZ loss and with ORHR. A progression from ORNV to SRNV was observed.

show abstract

Section: Resultsmentioning

confidence: 99%

From Outer Retinal Neovacularization to Exudative Subretinal Neovascularization in Macular Telangiectasia Type 2

Krivosic,

Zureik,

Mph

et al. 2024

Retina

Self Cite

View full text Add to dashboard Cite

show abstract

“…In advanced stages, hyperplasia of the retinal pigment epithelium and subretinal neovascularization can result in vision impairment . Although the current standard for MacTel diagnosis relies on multimodal imaging (fundus photography, fluorescein angiography, fundus autofluorescence, and optical coherence tomography [OCT] imaging), a variety of findings are apparent on OCT imaging at all stages of disease …”

Section: Introductionmentioning

confidence: 99%

Self-Supervised Learning for Improved Optical Coherence Tomography Detection of Macular Telangiectasia Type 2

Gholami,

Scheppke,

Kshirsagar

et al. 2024

JAMA Ophthalmol

Self Cite

View full text Add to dashboard Cite

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.

show abstract