ArticleScientific reports2022
Deep learning-based 3D OCT imaging for detection of lamina cribrosa defects in eyes with high myopia.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.
- Artificial intelligence in pathologic myopia: a review of clinical research studies.Frontiers in medicine · 2025Pooled it
- Automated Optic Disc Tilt Classification in Fundus Photographs Using Segmentation and the Elliptical Ratio: External Clinical Validation Study.JMIR formative research · 2026Article
- Optic Nerve Atrophy Conditions Associated With 3D Unsegmented Optical Coherence Tomography Volumes Using Deep Learning.JAMA ophthalmology · 2025Article
- Multimodal imaging of optic nerve head abnormalities in high myopia.Frontiers in neurology · 2024Review
- Temporal Protrusion of the Optic Disc Implicated in Central Visual Field Abnormalities in High Myopia: Two Case Reports.Case reports in ophthalmologyArticle
- Shaping the future of myopia with artificial intelligence: Mapping trends and promising directions.Advances in ophthalmology practice and researchArticle
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The lamina cribrosa (LC) is a collagenous tissue located in the optic nerve head, and its dissection is observed in eyes with pathologic myopia as a LC defect (LCD). The diagnosis of LCD has been difficult because the LC is located deep beneath the retinal nerve fibers. The purpose of this study was to determine the prevalence and three-dimensional shape of LCDs in highly myopic eyes. Swept-source OCT scan images of a 3 × 3-mm cube centered on the optic disc were obtained from 119 eyes of 62 highly myopic patients. Each LC was manually labelled in cross-sectional OCT images along the axial, coronal, and sagittal planes. A deep convolutional neural network (DCNN) was trained with the manually labeled images, and the trained DCNN was applied to the detection of the LC in every image in each plane. Three-dimensional images of the LC were generated from the labeled image of each eye. The results showed that LCDs were detected in 12 of the 42 (29%) eyes in which an LC was visible. The LCDs ran vertically at the temporal edge of the optic disc. In conclusion, 3D OCT imaging with the application of DCNN is helpful in diagnosing LCDs.
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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.