ArticleAnnals of translational medicine2022
Choroid automatic segmentation and thickness quantification on swept-source optical coherence tomography images of highly myopic patients.
Article in Annals of translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Artificial intelligence in pathologic myopia: a review of clinical research studies.Frontiers in medicine · 2025Pooled it
- 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
- A fully automatic MRI-guided decision support system for lumbar disc herniation using machine learning.JOR spine · 2024Article
- Automatic fovea detection and choroid segmentation for choroidal thickness assessment in optical coherence tomography.International journal of ophthalmology · 2024Article
- Deep learning in optical coherence tomography: Where are the gaps?Clinical & experimental ophthalmology · 2023Review
- A Deep Learning-Based Fully Automated Program for Choroidal Structure Analysis Within the Region of Interest in Myopic Children.Translational vision science & technology · 2023Article
- Deep-learning algorithms for choroidal thickness measurements in high myopia.Annals of translational medicine · 2022Article
- Does everyone understand the terminology 'borrowed' from computer sciences creeping into medical sciences?Annals of translational medicine · 2022Article
- Applications of Artificial Intelligence in Choroid Visualization for Myopia: A Comprehensive Scoping Review.Middle East African journal of ophthalmologyArticle
- Role of artificial intelligence in optical coherence tomography in myopia and pathological myopia.Taiwan journal of ophthalmologyReview
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Authors and funding
10 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Thinning of the choroid has been linked with various ocular diseases, including high myopia (HM), which can lead to visual impairment. Although various artificial intelligence (AI) algorithms have been developed to quantify choroidal thickness (ChT), few patients with HM were included in their development. The choroid in patients with HM tends to be thinner than that of normal patients, making it harder to segment. Therefore, in this study, we aimed to develop and implement a novel deep learning algorithm based on a group-wise context selection network (GCS-Net) to automatically segment the choroid and quantify its thickness on swept-source optical coherence tomography (SS-OCT) images of HM patients. Methods: A total of 720 SS-OCT images were obtained from 40 HM eyes and 20 non-HM eyes and were used to develop a GCS-Net to segment the choroid. The intersection-over-union (IoU), Dice similarity coefficient (DSC), sensitivity, and specificity were used to assess the performance in relation to manually segmented ground truth. The independent test dataset included 3,192 images from 266 HM eyes. The ChT in the test dataset was measured manually and automatically at 9 different regions within the choroid. The average difference in the ChT between the 2 methods was calculated. The intraclass correlation coefficient (ICC) was calculated to evaluate the agreement between the 2 measurements. Results: Our method reached an IoU, DSC, sensitivity, and specificity of 87.89%, 93.40%, 92.42%, and 99.82% in HM, respectively. The average difference in the ChT between the 2 measurements was 5.54±4.57 µm. The ICC was above 0.90 (P<0.001) for all regions of the choroid, indicating a very high level of agreement. Conclusions: The GCS-Net proposed in our study provides a reliable and fast tool to quantify ChT in HM patients and could potentially be used as a tool for monitoring ChT in ocular diseases related to the choroid.
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