Evidence map›Paper›PMID 34518364›Full record

ArticleeNeuro

MEYE: Web App for Translational and Real-Time Pupillometry.

Raffaele Mazziotti, Fabio Carrara, Aurelia Viglione, Leonardo Lupori, Luca Lo Verde, Alessandro Benedetto, Giulia Ricci, Giulia Sagona, Giuseppe Amato, Tommaso Pizzorusso

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in eNeuro. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
1.8field-weighted citation impact, top 16% of its field
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

13 citing papers in PubMed, 25 citations in OpenAlex.

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  12. Pilot Study of Smartphone Infrared Pupillography and Pupillometry.Clinical ophthalmology (Auckland, N.Z.) · 2022
    Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors at 5 institutions in 1 country.

Raffaele MazziottiDepartment of Neuroscience, Psychology, Drug Research and Child Health (NEUROFARBA), University of Florence, 50135 Florence, Italy raffaele.mazziotti@in.cnr.it.ORCID 0000-0001-5344-5079
Fabio CarraraIstituto di Scienza e Tecnologia dell'Informazione (ISTI), 1-56124 Pisa, Italy.
Aurelia ViglioneInstitute of Neuroscience, National Research Council, 1-56124 Pisa, Italy.
Leonardo LuporiInstitute of Neuroscience, National Research Council, 1-56124 Pisa, Italy.
Luca Lo VerdeInstitute of Neuroscience, National Research Council, 1-56124 Pisa, Italy.
Alessandro BenedettoDepartment of Translational Research on New Technologies in Medicine and Surgery, University of Pisa, 56127 Pisa, Italy.
Giulia RicciDepartment of Neuroscience, Psychology, Drug Research and Child Health (NEUROFARBA), University of Florence, 50135 Florence, Italy.
Giulia SagonaInstitute of Neuroscience, National Research Council, 1-56124 Pisa, Italy.
Giuseppe AmatoIstituto di Scienza e Tecnologia dell'Informazione (ISTI), 1-56124 Pisa, Italy.
Tommaso PizzorussoDepartment of Neuroscience, Psychology, Drug Research and Child Health (NEUROFARBA), University of Florence, 50135 Florence, Italy.
Neuroscience Institute · ITScuola Normale Superiore · ITIstituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" · ITUniversity of Florence · ITUniversity of Pisa · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pupil dynamics alterations have been found in patients affected by a variety of neuropsychiatric conditions, including autism. Studies in mouse models have used pupillometry for phenotypic assessment and as a proxy for arousal. Both in mice and humans, pupillometry is noninvasive and allows for longitudinal experiments supporting temporal specificity; however, its measure requires dedicated setups. Here, we introduce a convolutional neural network that performs online pupillometry in both mice and humans in a web app format. This solution dramatically simplifies the usage of the tool for the nonspecialist and nontechnical operators. Because a modern web browser is the only software requirement, this choice is of great interest given its easy deployment and setup time reduction. The tested model performances indicate that the tool is sensitive enough to detect both locomotor-induced and stimulus-evoked pupillary changes, and its output is comparable to state-of-the-art commercial devices.

Indexed as

Mobile ApplicationsAnimalsArousalHumansMicePupilarousalneural networkoddballpupillometryvirtual realityweb app

Identifiers

PMID34518364
PMCPMC8489024
OpenAlexW3199162188

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.