Re: Lee et al: Deep Learning Is Effective for Classifying Normal versus Age-Related Macular Degeneration OCT Images (Ophthalmol Retina. 2017;1:322-327).

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

4 citing papers in PubMed.

1. Beyond convolutions and supervised learning with transformers and representation learning for retinal image analysis.Progress in retinal and eye research · 2026
   Review
2. Artificial Intelligence and Advanced Technology in Glaucoma: A Review.Journal of personalized medicine · 2024
   Review
3. CNV-Net: Segmentation, Classification and Activity Score Measurement of Choroidal Neovascularization (CNV) Using Optical Coherence Tomography Angiography (OCTA).Diagnostics (Basel, Switzerland) · 2023
   Article
4. Artificial intelligence can assist with diagnosing retinal vein occlusion.International journal of ophthalmology · 2021
   Article

Corrections and comments

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  Deep learning is effective for the classification of OCT images of normal versus Age-related Macular Degeneration.

Authors and funding

1 author.

Funding

No grant is acknowledged in the PubMed record.

Abstract

PubMed holds no abstract for this paper.