ArticleTranslational vision science & technology2022
Application of Artificial Intelligence and Deep Learning for Choroid Segmentation in Myopia.
Article in Translational vision science & technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- Artificial intelligence in pathologic myopia: a review of clinical research studies.Frontiers in medicine · 2025Pooled it
- Three-Dimensional Choroidal Vessels Assessment in Fellow Eyes of Patients With Central Serous Chorioretinopathy.Translational vision science & technology · 2025Article
- An Intelligent Grading Model for Myopic Maculopathy Based on Long-Tailed Learning.Translational vision science & technology · 2025Article
- Assessment of choroidal vessels in healthy eyes using 3-dimensional vascular maps and a semi-automated deep learning approach.Scientific reports · 2025Article
- Uncovering predictors of myopia in youth: a secondary data analysis using a machine learning approach.Frontiers in medicine · 2025Article
- Segmentation of choroidal area in optical coherence tomography images using a transfer learning-based conventional neural network: a focus on diabetic retinopathy and a literature review.BMC medical imaging · 2024Review
- Choroidalyzer: An Open-Source, End-to-End Pipeline for Choroidal Analysis in Optical Coherence Tomography.Investigative ophthalmology & visual science · 2024Article
- Peripheral Choroidal Response to Localized Defocus Blur: Influence of Native Peripheral Aberrations.Investigative ophthalmology & visual science · 2024Article
- SMLS-YOLO: an extremely lightweight pathological myopia instance segmentation method.Frontiers in neuroscience · 2024Article
- Multimodal imaging of optic nerve head abnormalities in high myopia.Frontiers in neurology · 2024Review
- Automatic fovea detection and choroid segmentation for choroidal thickness assessment in optical coherence tomography.International journal of ophthalmology · 2024Article
- Evaluation of an Automated Choroid Segmentation Algorithm in a Longitudinal Kidney Donor and Recipient Cohort.Translational vision science & technology · 2023Article
- An Open-Source Deep Learning Algorithm for Efficient and Fully Automatic Analysis of the Choroid in Optical Coherence Tomography.Translational vision science & technology · 2023Article
- IMI 2023 Digest.Investigative ophthalmology & visual science · 2023Article
- A Deep Learning-Based Fully Automated Program for Choroidal Structure Analysis Within the Region of Interest in Myopic Children.Translational vision science & technology · 2023Article
- Artificial intelligence technology for myopia challenges: A review.Frontiers in cell and developmental biology · 2023Review
- Novel Uses and Challenges of Artificial Intelligence in Diagnosing and Managing Eyes with High Myopia and Pathologic Myopia.Diagnostics (Basel, Switzerland) · 2022Review
- Ocular and Hemodynamic Factors Contributing to the Central Visual Function in Glaucoma Patients With Myopia.Investigative ophthalmology & visual science · 2022Observational
- Choroidal layer segmentation in OCT images by a boundary enhancement network.Frontiers in cell and developmental biology · 2022Article
- Role of artificial intelligence in optical coherence tomography in myopia and pathological myopia.Taiwan journal of ophthalmologyReview
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo investigate the correlation between choroidal thickness and myopia progression using a deep learning method.
methodsTwo data sets, data set A and data set B, comprising of 123 optical coherence tomography (OCT) volumes, were collected to establish the model and verify its clinical utility. The proposed mask region-based convolutional neural network (R-CNN) model, trained with the pretrained weights from the Common Objects in Context database as well as the manually labeled OCT images from data set A, was used to automatically segment the choroid. To verify its clinical utility, the mask R-CNN model was tested with data set B, and the choroidal thickness estimated by the model was also used to explore its relationship with myopia.
resultsCompared with the result of manual segmentation in data set B, the error of the automatic choroidal inner and outer boundary segmentation was 6.72 ± 2.12 and 13.75 ± 7.57 µm, respectively. The mean dice coefficient between the region segmented by automatic and manual methods was 93.87% ± 2.89%. The mean difference in choroidal thickness over the Early Treatment Diabetic Retinopathy Study zone between the two methods was 10.52 µm. Additionally, the choroidal thickness estimated using the proposed model was thinner in high-myopic eyes, and axial length was the most significant predictor.
conclusionsThe mask R-CNN model has excellent performance in choroidal segmentation and quantification. In addition, the choroid of high myopia is significantly thinner than that of nonhigh myopia. TRANSLATIONAL RELEVANCE: This work lays the foundations for mask R-CNN models that could aid in the evaluation of more intricate changes occurring in chorioretinal diseases.
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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.