Editorial: Advanced ophthalmic imaging in glaucoma and other optic neuropathies
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».