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Record W4411081279 · doi:10.1016/j.ajo.2025.05.051

Choriocapillaris in Age-Related Macular Degeneration: A Systematic Review of Optical Coherence Tomography Angiography–Based Assessments and Challenges in Standardization

2025· review· en· W4411081279 on OpenAlexaff
Alessandro Berni, Claudio Foti, Lorena Ulla, Chinmayi Vyas, Jay Chhablani, Varun Chaudhary, Yousif Subhi, Giovanni Gregori, Ruikang K. Wang, Philip J. Rosenfeld, SriniVas R. Sadda, Francesco Bandello, Michele Reibaldi, Enrico Borrelli

Bibliographic record

VenueAmerican Journal of Ophthalmology · 2025
Typereview
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsMcMaster University
FundersJanssen PharmaceuticalsAllerganSamsungGenentechAstellas PharmaHeidelberg EngineeringApellis PharmaceuticalsRegeneron PharmaceuticalsUniversity of MiamiAlnylam PharmaceuticalsBiogenBayer HealthCareAlexion PharmaceuticalsVertex PharmaceuticalsColgate-Palmolive CompanyPfizerCarl Zeiss Meditec AGAmgen
KeywordsStandardizationMedicineComputer scienceOperating system

Abstract

fetched live from OpenAlex

TOPIC: This systematic review evaluates the methodologies used for assessing the choriocapillaris (CC) in age-related macular degeneration (AMD) using optical coherence tomography angiography (OCTA). It focuses on identifying methodological heterogeneity in imaging and analysis protocols and its implications for clinical and research applications. CLINICAL RELEVANCE: AMD is a leading cause of vision loss, and assessing CC perfusion provides critical insights into its pathophysiology. OCTA has emerged as a noninvasive imaging technique offering high-resolution visualization of the CC. However, variability in methodologies has hindered the standardization of CC assessments. Establishing consistent practices is essential for improving clinical and research outcomes in AMD management. DESIGN: Systematic review. METHODS: Studies included in this review were selected on the basis of eligibility criteria defined by the Population, Intervention, Comparator, Outcome, Study design framework. Participants included patients with early, intermediate, and late AMD. Interventions involved the use of OCTA for CC assessment, with no restrictions on device type or scan parameters. Comprehensive searches of MEDLINE, Web of Science Core Collection, and the Cochrane Library were conducted up to November 30, 2024. Data extractions and narrative synthesis focused on device types, scan protocols, segmentation techniques, compensation strategies, and quantitative metrics. RESULTS: A total of 102 studies, encompassing 4047 patients with AMD and 4415 eyes, were analyzed. Fifty-two studies used spectral-domain OCTA, and 49 studies used swept-source OCTA, with significant heterogeneity in segmentation boundaries and slab thickness (4.4-30 µm). Only 32 studies used compensation strategies to address signal attenuation under drusen. Quantitative metrics varied widely, with CC flow deficit percentage being the most common. However, inconsistencies in thresholding methods and lack of standardized Phansalkar radius reporting limited comparability. The review highlights gaps in reporting segmentation boundaries and compensation techniques, which impact the ability to assess the reliability of findings. CONCLUSIONS: This review underscores substantial variability in methodologies for CC assessment in AMD, highlighting the urgent need for standardized imaging protocols and analytical approaches. Despite advances in OCTA technology, inconsistencies in segmentation, thresholding, and compensation strategies challenge data reliability and reproducibility. Future research should prioritize methodological standardization to enhance comparability and clinical applicability.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.036
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0080.009
Bibliometrics0.0070.006
Science and technology studies0.0000.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.043
GPT teacher head0.376
Teacher spread0.333 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations6
Published2025
Admission routes1
Has abstractno

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