Evidence map›Paper›PMID 42105087›Full record

ArticleGraefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie2026

A deep learning model for automated identification of age-related macular degeneration atrophy.

Oren Avram, Yahel Shwartz, Adi Green, Rivka Bloom, Giulia Corradetti, Anthony Wu, Zeyuan Johnson Chen, Tal Eshkoly Lior, Berkin Durmus, Akos Rudas and 13 more

Abstract read
PubMed Publisher
In one paragraph

Article in Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

23 authors.

Oren Avram *Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA. orenavram@gmail.com.
Yahel Shwartz *Department of Ophthalmology, Hadassah Medical Organization and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.
Adi GreenDepartment of Ophthalmology, Hadassah Medical Organization and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.
Rivka BloomDepartment of Ophthalmology, Hadassah Medical Organization and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.
Giulia CorradettiDoheny Eye Institute, Pasadena, CA, USA.
Anthony WuDepartment of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Zeyuan Johnson ChenDepartment of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Tal Eshkoly LiorStein Eye Institute, University of California Los Angeles, Los Angeles, CA, USA.
Berkin DurmusDepartment of Computer Science, University of California Los Angeles, Los Angeles, CA, USA.
Akos RudasDepartment of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Ravi PalDepartment of Anesthesiology and Perioperative Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Nadav RakoczDepartment of Computer Science, University of California Los Angeles, Los Angeles, CA, USA.
Ceren SoyluDoheny Eye Institute, Pasadena, CA, USA.
Mai AlhelalyDoheny Eye Institute, Pasadena, CA, USA.
Giacomo BosciaDoheny Eye Institute, Pasadena, CA, USA.
Charles C WykoffRetina Consultants of Texas, Retina Consultants of America, Houston, TX, USA.
Maxime CannessonDepartment of Anesthesiology and Perioperative Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Srinivas R SaddaDoheny Eye Institute, Pasadena, CA, USA.
Jaime LevyDepartment of Ophthalmology, Hadassah Medical Organization and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.
Eran HalperinDepartment of Computer Science, New York University, New York, NY, USA.
Jeffrey N ChiangDepartment of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Itay ChowersDepartment of Ophthalmology, Hadassah Medical Organization and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.
Liran TiosanoDepartment of Ophthalmology, Hadassah Medical Organization and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel. tiosano@hadassah.org.il.

Funding

Personalized Risk Prediction for Prevention and Early Detection of Postoperative Failure to RescueR01EB035028 · NIBIB · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Maxime Cannesson · 2023 to 2026
$2.3M
NIBIB NIH HHS R01 EB035028
6 · The paper itself

Abstract

Age-related macular degeneration (AMD), a leading cause of visual impairment and blindness among the elderly, is projected to affect 288 million individuals globally by 2040. Advanced AMD, including complete retinal pigment epithelium and outer retinal atrophy (cRORA), pose significant challenges for diagnosis and monitoring due to the labor-intensive, costly, and variable nature of manual annotation of volumetric optical coherence tomography (OCT) scans. Automating cRORA diagnosis offers the potential to improve annotation consistency and reduce clinical burden, which could facilitate, for example, the evaluation of recently FDA-approved treatments that delay disease progression. In this study, we compiled two large independent cohorts totaling nearly 5,000 3D OCT scans, labeled them for cRORA presence, and developed a deep learning model for cRORA automated detection. The model achieved state-of-the-art performance, with a ROC AUC of 0.97 on internal validation, and demonstrated robust translatability (zero-shot learning) with a ROC AUC of 0.88 on external evaluation. Notably, it exhibited high accuracy for both non-neovascular (non-nv) and neovascular (nv) AMD subgroups (ROC AUC 0.98 and 0.93, respectively), including complex cases with exudation. This model and dataset combination could facilitate clinical research and trial analyses by providing scalable, standardized assessments across non-nv and nv AMD patient subgroups.

Indexed as

3D medical imagingAge-related macular degeneration (AMD)CRORADeep learningOptical coherence tomography (OCT)Retinal atrophy

Identifiers

PMID42105087

What OpenQuestion holds

Textmetadata
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.