Evidence map›Paper›PMID 40059223›Full record

ReviewGraefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie2025

Imaging biomarkers and artificial intelligence for diagnosis, prediction, and therapy of macular fibrosis in age-related macular degeneration: Narrative review and future directions.

Rishikesh Gandhewar, Thales Guimaraes, Sagnik Sen, Nikolas Pontikos, Ismail Moghul, Theodoros Empeslidis, Michel Michaelides, Konstantinos Balaskas

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Rishikesh GandhewarFaculty of Medicine, Imperial College London, London, England.
Thales GuimaraesInstitute of Ophthalmology, University College London, London, England.
Sagnik SenInstitute of Ophthalmology, University College London, London, England.
Nikolas PontikosInstitute of Ophthalmology, University College London, London, England.
Ismail MoghulInstitute of Ophthalmology, University College London, London, England.
Theodoros EmpeslidisStoneygate Eye Hospital, Leicester, England.
Michel MichaelidesInstitute of Ophthalmology, University College London, London, England.
Konstantinos BalaskasInstitute of Ophthalmology, University College London, London, England. Konstantinos.Balaskas@gmail.com.ORCID http://orcid.org/0000-0002-7690-6277

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Macular fibrosis is an end-stage complication of neovascular Age-related Macular Degeneration (nAMD) with a complex and multifactorial pathophysiology that can lead to significant visual impairment. Despite the success of anti-vascular endothelium growth factors (anti-VEGF) over the last decade that revolutionised the management and visual prognosis of nAMD, macular fibrosis develops in a significant proportion of patients and, along with macular atrophy (MA), is a main driver of long-term vision deterioration. There remains an unmet need to better understand macular fibrosis and develop anti-fibrotic therapies. The use of imaging biomarkers in combination with novel Artificial Intelligence (AI) algorithms holds significant potential for improving the accuracy of diagnosis, disease monitoring, and therapeutic discovery for macular fibrosis. In this review, we aim to provide a comprehensive overview of the current state of knowledge regarding the various imaging modalities and biomarkers for macular fibrosis alongside outlining potential avenues for AI applications. We discuss manifestations of macular fibrosis and its precursors with diagnostic and prognostic significance on various imaging modalities, including Optical Coherence Tomography (OCT), Colour Fundus Photography (CFP), Fluorescein Angiography (FA), OCT-Angiography (OCTA) and collate data from prospective and retrospective research on known biomarkers. The predominant role of OCT for biomarker identification is highlighted. The review coincides with a resurgence of intense research interest in academia and industry for therapeutic discovery and clinical testing of anti-fibrotic molecules.

Indexed as

Artificial IntelligenceBiomarkersFluorescein AngiographyMacula LuteaTomography, Optical CoherenceWet Macular DegenerationFibrosisHumansPrognosisBiomarkersAge-related Macular DegenerationAnti-Vascular Endothelium Growth FactorsArtificial IntelligenceBiomarkersColour Fundus PhotographyFluorescein AngiographyMacular FibrosisOptical Coherence Tomography

Identifiers

PMID40059223
PMCPMC12373684

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.