ArticleTranslational vision science & technology2020
Application of a Deep Machine Learning Model for Automatic Measurement of EZ Width in SD-OCT Images of RP.
Article in Translational vision science & technology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 18 citations in OpenAlex.
- Deep Learning-Based Automated Segmentation and Quantification of the Ellipsoid Zone and the RPE-Bruch's Membrane Complex in Healthy Subjects and in Geographic Atrophy.Diagnostics (Basel, Switzerland) · 2026Article
- The Role of Artificial Intelligence in Imaging-Based Diagnosis of Retinal Dystrophy and Evaluation of Gene Therapy Efficacy.Journal of personalized medicine · 2025Review
- Quantification of Optical Coherence Tomography Features in >3500 Patients with Inherited Retinal Disease Reveals Novel Genotype-Phenotype Associations.medRxiv : the preprint server for health sciences · 2025Article
- Update on Clinical Trial Endpoints in Gene Therapy Trials for Inherited Retinal Diseases.Journal of clinical medicine · 2024Review
- Deep Learning-Assisted Measurements of Photoreceptor Ellipsoid Zone Area and Outer Segment Volume as Biomarkers for Retinitis Pigmentosa.Bioengineering (Basel, Switzerland) · 2023Article
- Deep Learning-Facilitated Study of the Rate of Change in Photoreceptor Outer Segment Metrics in RPGR-Related X-Linked Retinitis Pigmentosa.Investigative ophthalmology & visual science · 2023Article
- Prediction of visual impairment in retinitis pigmentosa using deep learning and multimodal fundus images.The British journal of ophthalmology · 2023Article
- Performance of Deep Learning Models in Automatic Measurement of Ellipsoid Zone Area on Baseline Optical Coherence Tomography (OCT) Images From the Rate of Progression of USH2A-Related Retinal Degeneration (RUSH2A) Study.Frontiers in medicine · 2022Article
- Deep Learning-Based Automatic Detection of Ellipsoid Zone Loss in Spectral-Domain OCT for Hydroxychloroquine Retinal Toxicity Screening.Ophthalmology science · 2021Article
- A Hybrid Model Composed of Two Convolutional Neural Networks (CNNs) for Automatic Retinal Layer Segmentation of OCT Images in Retinitis Pigmentosa (RP).Translational vision science & technology · 2021Article
- Challenges Associated With Ellipsoid Zone Intensity Measurements Using Optical Coherence Tomography.Translational vision science & technology · 2021Article
- Corneal pachymetry by AS-OCT after Descemet's membrane endothelial keratoplasty.Scientific reports · 2021Article
- Feasibility study to improve deep learning in OCT diagnosis of rare retinal diseases with few-shot classification.Medical & biological engineering & computing · 2021Article
- Retinal Boundary Segmentation in Stargardt Disease Optical Coherence Tomography Images Using Automated Deep Learning.Translational vision science & technology · 2020Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Purpose: We applied a deep convolutional neural network model for automatic identification of ellipsoid zone (EZ) in spectral domain optical coherence tomography B-scans of retinitis pigmentosa (RP). Methods: Midline B-scans having visible EZ from 220 patients with RP and 20 normal subjects were manually segmented for inner limiting membrane, inner nuclear layer, EZ, retinal pigment epithelium, and Bruch's membrane. A total of 2.87 million labeled image patches (33 × 33 pixels) extracted from 480 B-scans were used for training a convolutional neural network model implemented in MATLAB. B-scans from a separate group of 80 patients with RP were used for testing the model. A local connected area searching algorithm was developed to process the model output for reconstructing layer boundaries. Correlation and Bland-Altman analyses were conducted to compare EZ width measured by the model to those by manual segmentation. Results: The accuracy of the trained model to identify inner limiting membrane, inner nuclear layer, EZ, retinal pigment epithelium, and Bruch's membrane patches in the test dataset was 98%, 89%, 91%, 94%, and 96%, respectively. The EZ width measured by the model was highly correlated with that by two graders (r = 0.97; Conclusions: The results demonstrated the capability of a deep machine learning-based method for automatic identification of EZ in RP, suggesting that the method can be used to quantify structural deficits in RP for detecting disease progression and for evaluating treatment effect. Translational Relevance: A deep machine learning model has the potential to replace humans for grading spectral domain optical coherence tomography images in RP.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.