Automated segmentation of the ciliary muscle in OCT images using fully convolutional networks.

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

5 citing papers in PubMed, 10 citations in OpenAlex.

1. Quantification of the Anterior-Centripetal Movement of the Ciliary Muscle During Accommodation Using Dynamic OCT Imaging.Translational vision science & technology · 2025
   Article
2. The effect of aging on the ciliary muscle and its potential relationship with presbyopia: a literature review.PeerJ · 2024
   Review
3. CMS-NET: deep learning algorithm to segment and quantify the ciliary muscle in swept-source optical coherence tomography images.Therapeutic advances in chronic disease · 2023
   Article
4. Performance of the Deep Neural NetworkDiagnostics (Basel, Switzerland) · 2022
   Article
5. Wavelet attention network for the segmentation of layer structures on OCT images.Biomedical optics express · 2022
   Article

Corrections and comments

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

Authors and funding

5 authors at 2 institutions in 2 countries.

Funding

No grant is acknowledged in the PubMed record.

Abstract