Editorial: Advanced ophthalmic imaging in glaucoma and other optic neuropathies
Bibliographic record
Abstract
Shiga Y et al. emphasize the utility of OCT and OCTA in the posterior segment of the eye for diagnosing glaucoma and detecting its progression. OCT is essential for diagnosing and monitoring glaucoma, enabling non-invasive and objective assessment of structural changes from the anterior to the posterior segment. More recently, OCTA has emerged as a technique for non-invasive assessment of vascular perfusion, including patency of the retinal capillary plexuses and microvasculature of the optic nerve head. This systematic review provides an update on OCT and OCTA as essential tools in glaucoma care. In addition, this review focuses on the importance of detecting macular damage in glaucoma, as well as the latest findings from optic nerve head imaging and future directions of the OCT and OCTA applications.Jiang Z et al. developed improved data acquisition and analysis methods for MRI of layerspecific retinal and choroidal blood flow. They applied this approach to detect reduced ocular blood flow in a well-established mouse model of glaucoma in both eyes. MRI-based imaging has emerged as a promising technique for studying ocular blood flow, addressing the depth resolution limitations of conventional vascular imaging methods. MRI facilitates a more comprehensive analysis of neurovascular dynamics in glaucoma by enabling precise quantification of retinal and choroidal blood flow. This study introduces an MRI-based simultaneous acquisition of bilateral data by dual eye coil setup, along with a novel retinal blood flow analysis approach. These advancements may provide deeper insight into ocular blood flow changes in glaucoma in vivo.Glaucoma, optic neuritis, and non-arteritic anterior ischemic optic neuropathy produce distinct retinal ganglion cell (RGC) damage patterns. Wang JK et al. used a booster Variational Autoencoder (bVAE), a deep learning model, to capture spatial variations in RGC loss in glaucoma and other optic neuropathies. The authors confirmed that the developed bVAE model could track spatial patterns of RGC thinning over time and classify underlying causes based on generated latent space montage maps, which visualize different degrees and spatial patterns of RGC axon bundle damage. The proposed approach using the bVAE model effectively captures key spatial features in the optic neuropathies and facilitates visualization and quantification of RGC thinning patterns, potentially aiding in predicting disease progression.In summary, the articles collected in this research topic cover 1) technological innovations such as OCT-based imaging techniques in assessing the anterior and posterior structures of the eye, 2) advances in techniques to visualize and quantify ocular blood flow in glaucomatous eyes, and 3) AI algorithms applications to extract disease-specific features. This research topic offers a broader perspective on imaging technologies and analytical approaches that contribute to the early detection and monitoring of glaucoma, essential for disease management. Future research should prioritize the integration of multiple imaging modalities to provide a more comprehensive view of glaucoma pathophysiology. Establishing standardized imaging biomarkers and increasing accessibility to advanced imaging tools will further enhance glaucoma management, ultimately leading to better patient outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.022 | 0.016 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".