Trial reportThe British journal of ophthalmology2024
Deep-learning automated quantification of longitudinal OCT scans demonstrates reduced RPE loss rate, preservation of intact macular area and predictive value of isolated photoreceptor degeneration in geographic atrophy patients receiving C3 inhibition treatment.
Trial report in The British journal of ophthalmology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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.
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.
Who cites it
12 citing papers in PubMed, 14 citations in OpenAlex.
- Dynamics of the EZ/RPE Loss Ratio on OCT Over Time During Geographic Atrophy Progression and Treatment With Pegcetacoplan.Investigative ophthalmology & visual science · 2025Trial
- Predictive Value of Perilesional Fundus Autofluorescence Patterns and OCT-Based Ellipsoid Zone/Retinal Pigment Epithelium Loss Ratios in Geographic Atrophy Growth Rates.Ophthalmology science · 2026Article
- From Subretinal to Intraretinal Fluid in Type 1 Macular Neovascularization: A Clinicopathologic Disease Transition Model.Investigative ophthalmology & visual science · 2026Article
- Deep Learning-Based Automated Segmentation and Quantification of the Ellipsoid Zone and the RPE-Bruch's Membrane Complex in Healthy Subjects and in Geographic Atrophy.Diagnostics (Basel, Switzerland) · 2026Article
- Phenotyping of Patients with Age-Related Macular Degeneration Using Artificial Intelligence-Driven Biomarker Patterns in OCT.Ophthalmology science · 2026Article
- Deep Learning-Based Choroidal Boundary Detection in Geographic Atrophy Using Spectral-Domain Optical Coherence Tomography.Diagnostics (Basel, Switzerland) · 2026Article
- Ophthalmic drug discovery and development using artificial intelligence and digital health technologies.NPJ digital medicine · 2025Review
- Consideration of patient phenotypes in geographic atrophy due to age-related macular degeneration.BMJ open ophthalmology · 2025Review
- Age-related macular degeneration: natural history revisited in geographic atrophy.Eye (London, England) · 2025Review
- Dense Convolutional Neural Network-Based Deep Learning Pipeline for Pre-Identification of Circular Leaf Spot Disease ofSensors (Basel, Switzerland) · 2024Article
- Bibliometric analysis of artificial intelligence and optical coherence tomography images: research hotspots and frontiers.International journal of ophthalmology · 2023Article
- Geographic atrophy: Understanding the relationship between structure and function.Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 2 institutions in 3 countries.
Funding
Abstract
objectiveTo evaluate the role of automated optical coherence tomography (OCT) segmentation, using a validated deep-learning model, for assessing the effect of C3 inhibition on the area of geographic atrophy (GA); the constituent features of GA on OCT (photoreceptor degeneration (PRD), retinal pigment epithelium (RPE) loss and hypertransmission); and the area of unaffected healthy macula.To identify OCT predictive biomarkers for GA growth.
methodsPost hoc analysis of the FILLY trial using a deep-learning model for spectral domain OCT (SD-OCT) autosegmentation. 246 patients were randomised 1:1:1 into pegcetacoplan monthly (PM), pegcetacoplan every other month (PEOM) and sham treatment (pooled) for 12 months of treatment and 6 months of therapy-free monitoring. Only participants with Heidelberg SD-OCT were included (n=197, single eye per participant).The primary efficacy endpoint was the square root transformed change in area of GA as complete RPE and outer retinal atrophy (cRORA) in each treatment arm at 12 months, with secondary endpoints including RPE loss, hypertransmission, PRD and intact macular area.
resultsEyes treated PM showed significantly slower mean change of cRORA progression at 12 and 18 months (0.151 and 0.277 mm, p=0.0039; 0.251 and 0.396 mm, p=0.039, respectively) and RPE loss (0.147 and 0.287 mm, p=0.0008; 0.242 and 0.410 mm, p=0.00809). PEOM showed significantly slower mean change of RPE loss compared with sham at 12 months (p=0.0313). Intact macular areas were preserved in PM compared with sham at 12 and 18 months (p=0.0095 and p=0.044). PRD in isolation and intact macula areas was predictive of reduced cRORA growth at 12 months (coefficient 0.0195, p=0.01 and 0.00752, p=0.02, respectively)
conclusionThe OCT evidence suggests that pegcetacoplan slows progression of cRORA overall and RPE loss specifically while protecting the remaining photoreceptors and slowing the progression of healthy retina to iRORA.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.