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Macular Diseases: From Diagnosis to Treatment

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Ophthalmology".

Deadline for manuscript submissions: 22 July 2026 | Viewed by 1404

Special Issue Editor


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Guest Editor
1. Department of Ophthalmology, University Hospital Southampton NHS Foundation Trust, Southampton SO16 6YD, UK
2. Faculty of Medicine, University of Southampton, Southampton SO17 1BJ, UK
Interests: age-related macular degeneration; retinal vascular diseases; intraocular tumours; retinal imaging
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Special Issue Information

Dear Colleagues,

Macular diseases, including age-related macular degeneration (AMD), diabetic macular edema (DME), and macular holes, are major contributors to central vision loss, especially in older adults. Diagnosis relies on advanced imaging techniques such as optical coherence tomography (OCT) and fluorescein angiography, which allow clinicians to detect retinal abnormalities with high precision. Although early detection methods have improved, many patients are still diagnosed in the later stages of disease progression. Current treatment strategies—primarily anti-VEGF injections, corticosteroids, and laser therapy—can slow vision loss but often require frequent administration and show variable effectiveness. Surgical options exist for specific cases like macular holes, but overall treatment outcomes remain suboptimal. Ongoing research is exploring innovative approaches such as gene therapy, stem cell treatments, and long-acting drug delivery systems to address these limitations. Key challenges in the field include a deeper understanding of disease mechanisms, identifying predictive biomarkers, and enhancing access to timely diagnosis and care. Addressing these issues is crucial for improving patient outcomes and reducing the global impact of macular diseases.

Dr. Gabriella De Salvo
Guest Editor

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Keywords

  • age-related macular degeneration
  • retinal vascular diseases
  • intraocular tumours
  • retinal imaging
  • OCT
  • OCT angiography
  • anti-VEGF
  • laser therapy
  • macular surgery
  • gene therapy
  • biomarkers
  • progression
  • intravitreal injections
  • biomarkers

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Published Papers (2 papers)

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Research

12 pages, 1932 KB  
Article
Diagnostic Agreement Between a General-Purpose AI Model and Retinal Specialists in Color Fundus Photography—A Pilot Study
by Sara Vaz-Pereira, Laura Vilaverde, André Ferreira and Bernardete Pessoa
J. Clin. Med. 2026, 15(9), 3430; https://doi.org/10.3390/jcm15093430 - 30 Apr 2026
Viewed by 239
Abstract
Background: Artificial intelligence (AI) has shown strong performance in disease-specific retinal screening tasks; however, its reliability in heterogeneous clinical diagnostic settings remains unclear. This study compared a general-purpose multimodal AI model with experienced retinal specialists in the interpretation of color fundus photographs (CFPs). [...] Read more.
Background: Artificial intelligence (AI) has shown strong performance in disease-specific retinal screening tasks; however, its reliability in heterogeneous clinical diagnostic settings remains unclear. This study compared a general-purpose multimodal AI model with experienced retinal specialists in the interpretation of color fundus photographs (CFPs). Methods: In this pilot retrospective cross-sectional study, 66 CFPs were independently evaluated by a masked retinal specialist and an AI model (Google Gemini 2.5 Flash). Diagnoses were compared with those of the unblinded treating specialist. The comparison was inherently asymmetric, as the reference specialist had access to full clinical information, whereas the masked evaluators performed image-only assessment. Agreement was assessed using weighted percent agreement and Gwet’s AC2 with quadratic weights. Results: Substantial agreement was observed between the two human specialists (AC2 = 0.67). In contrast, agreement between the AI model and the reference specialist was low (AC2 = −0.58). Direct comparison between the masked specialist and the AI also showed limited reliability (AC2 = −0.38). Conclusions: In this pilot study, the evaluated AI model demonstrated limited agreement relative to a context-informed specialist reference. These findings support cautious interpretation of consumer-facing multimodal AI in open-ended retinal image assessment and warrant validation in larger, multicenter studies. Full article
(This article belongs to the Special Issue Macular Diseases: From Diagnosis to Treatment)
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12 pages, 10042 KB  
Article
Optical Coherence Tomography Angiography Features and Flow-Based Classification of Retinal Artery Macroaneurysms
by Mohamed Oshallah, Anastasios E. Sepetis, Antonio Valastro, Eslam Ahmed, Sara Vaz-Pereira, Luca Ventre and Gabriella De Salvo
J. Clin. Med. 2025, 14(24), 8686; https://doi.org/10.3390/jcm14248686 - 8 Dec 2025
Viewed by 708
Abstract
Objectives: We propose a flow-signal-based classification of retinal artery macroaneurysms (RAMs) using Optical Coherence Tomography Angiography (OCTA) and compare the findings with fundus fluorescein angiography (FFA). Methods: A retrospective review of 49 RAM cases observed over 6 years (October 2017–March 2023) at a [...] Read more.
Objectives: We propose a flow-signal-based classification of retinal artery macroaneurysms (RAMs) using Optical Coherence Tomography Angiography (OCTA) and compare the findings with fundus fluorescein angiography (FFA). Methods: A retrospective review of 49 RAM cases observed over 6 years (October 2017–March 2023) at a medical retina clinic at the University Hospital Southampton, UK. Electronic clinical records, FFA, and OCTA images (en face and B-scan) were reviewed to identify pathology and assess RAM flow profiles. Results: In total, 30 eyes from 30 patients were included. The mean age of the patients was 76 years (range 49–91), with 17 females and 13 males. All eyes underwent OCTA, enabling classification of RAMs into three flow signal types: high (9 eyes), low (10 eyes), and absent (9 eyes), while 2 eyes had haemorrhage-related artefacts. A subgroup of 13 eyes also underwent FFA, allowing direct comparison, which showed flow profiles similar to those of OCTA: high (4 eyes), low (6 eyes), and absent (2 eyes), with 1 ungradable case due to subretinal haemorrhage masking. A discrepancy in flow was observed in one case where FFA indicated flow, but OCTA did not. Despite this, FFA and OCTA generally agreed on the flow levels, with a Spearman correlation of r = 0.79 (p = 0.004). Conclusions: OCTA flow profiles were directly comparable to FFA. OCTA effectively identified different levels of blood flow signal behaviour in RAMs. The proposed flow-based RAM classification may aid in prognosis, treatment indications, follow-up, and safe repeat imaging in clinical practice without systemic risk to the patient. Full article
(This article belongs to the Special Issue Macular Diseases: From Diagnosis to Treatment)
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