Evidence map›Paper›PMID 40563916›Full record

ArticleBiology2025

A Deep Learning Approach to Measure Visual Function in Zebrafish.

Manjiri Patil, Annabel Birchall, Hammad Syed, Vanessa Rodwell, Ha-Jun Yoon, William H J Norton, Mervyn G Thomas

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
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

7 authors.

Manjiri PatilThe University of Leicester Ulverscroft Eye Unit, School of Psychology and Vision Sciences, University of Leicester, Robert Kilpatrick Clinical Sciences Building, P.O. Box 65, Leicester LE2 7LX, UK.
Annabel BirchallThe University of Leicester Ulverscroft Eye Unit, School of Psychology and Vision Sciences, University of Leicester, Robert Kilpatrick Clinical Sciences Building, P.O. Box 65, Leicester LE2 7LX, UK.
Hammad SyedThe University of Leicester Ulverscroft Eye Unit, School of Psychology and Vision Sciences, University of Leicester, Robert Kilpatrick Clinical Sciences Building, P.O. Box 65, Leicester LE2 7LX, UK.ORCID 0009-0004-3812-5922
Vanessa RodwellThe University of Leicester Ulverscroft Eye Unit, School of Psychology and Vision Sciences, University of Leicester, Robert Kilpatrick Clinical Sciences Building, P.O. Box 65, Leicester LE2 7LX, UK.
Ha-Jun YoonThe University of Leicester Ulverscroft Eye Unit, School of Psychology and Vision Sciences, University of Leicester, Robert Kilpatrick Clinical Sciences Building, P.O. Box 65, Leicester LE2 7LX, UK.
William H J NortonNeurogenetics Group, Department of Genetics, Genomics and Cancer Sciences, University of Leicester, Robert Kilpatrick Clinical Sciences Building, P.O. Box 65, Leicester LE2 7LX, UK.
Mervyn G ThomasThe University of Leicester Ulverscroft Eye Unit, School of Psychology and Vision Sciences, University of Leicester, Robert Kilpatrick Clinical Sciences Building, P.O. Box 65, Leicester LE2 7LX, UK.

Funding

Wellcome TrustWellcome Trust, Academy of Medical Sciences, Fight for Sight, Medical Research Council, BBSRC-MIBTP RM38J0003M20, SGL021\1066, SGANYN2210, MR/W007002/1
6 · The paper itself

Abstract

Visual behaviour in zebrafish, often measured by the optokinetic reflex (OKR), serves as a valuable model for studying aspects of human neurological and ocular diseases and for conducting therapeutic or toxicology assays. Traditional methods for OKR analysis often rely on binarization techniques (threshold-based conversion of images to black and white) or costly software, which limits their utility in low-contrast settings or hypopigmented disease models. Here, we present a novel deep learning pipeline for OKR analysis, using ResNet-50 within the DeepLabCut framework in a Python Version 3.10 environment. Our approach employs object tracking to enable robust eye movement quantification, regardless of variations in contrast or pigmentation. OKR responses were elicited in both wild-type and

Indexed as

deep learningoptokinetic responsevisual behaviourvisual developmentzebrafish

Identifiers

PMID40563916
PMCPMC12189860

What OpenQuestion holds

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LicenceCC BY
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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.