Evidence map›Paper›PMID 39847417›Full record

ArticleJMIR research protocols2025

Sign Language Recognition System for Deaf Patients: Protocol for a Systematic Review.

Milena Soriano Marcolino, Lucca Fagundes Ramos Oliveira, Lucas Rocha Valle, Luiza Marinho Motta Santa Rosa, Zilma Silveira Nogueira Reis, Thiago Barbabela de Castro Soares, Elidéa Lúcia Almeida Bernardino, Raniere Alislan Almeida Cordeiro, Raquel Oliveira Prates, Mario Fernando Montenegro Campos

Abstract read
In one paragraph

Article in JMIR research protocols, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. 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

10 authors.

Milena Soriano MarcolinoUniversidade Federal de Minas Gerais, Medical School, Belo Horizonte, Brazil.ORCID 0000-0003-4278-3771
Lucca Fagundes Ramos OliveiraMedical School, Universidade Federal de Ouro Preto, Ouro Preto, Brazil.ORCID 0000-0002-2248-1218
Lucas Rocha ValleUniversidade Federal de Minas Gerais, Medical School, Belo Horizonte, Brazil.ORCID 0000-0002-0676-8657
Luiza Marinho Motta Santa RosaFaculdade Ciências Médicas de Minas Gerais, Belo Horizonte, Brazil.ORCID 0000-0002-4741-4871
Zilma Silveira Nogueira ReisUniversidade Federal de Minas Gerais, Medical School, Belo Horizonte, Brazil.ORCID 0000-0001-6374-9295
Thiago Barbabela de Castro SoaresTelehealth Center, University Hospital of Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.ORCID 0000-0002-6972-5507
Elidéa Lúcia Almeida BernardinoFaculty of Arts and Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.ORCID 0000-0002-3718-9631
Raniere Alislan Almeida CordeiroFaculty of Arts and Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.ORCID 0009-0006-5588-6023
Raquel Oliveira PratesDepartment of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.ORCID 0000-0002-7128-4974
Mario Fernando Montenegro CamposDepartment of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.ORCID 0000-0002-8336-9190

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIndividuals with hearing impairments may face hindrances in health care assistance, which may significantly impact the prognosis and the incidence of complications and iatrogenic events. Therefore, the development of automatic communication systems to assist the interaction between this population and health care workers is paramount.

objectiveThis study aims to systematically review the evidence on communication systems using human-computer interaction techniques developed for deaf people who communicate through sign language that are already in use or proposed for use in health care contexts and have been tested with human users or videos of human users.

methodsA systematic review will be performed based on a literature search in MEDLINE, Web of Science, ACM, and IEEE Xplore as well as top-tiered conferences in the area to identify relevant studies. The inclusion criteria are the description of the development of a sign language recognition system in a health care context and the testing with human users. Independent investigators (LFRO, LRV, and LMMSR) will screen eligible studies, and disagreements will be solved by a senior researcher (MSM). The included papers will undergo full-text screening. A PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) flow diagram will be presented to visually summarize the screening process, ensuring clarity and transparency in presenting the results. Additionally, a comprehensive chart table will be constructed to consolidate essential data related to the key variables extracted from the studies. These results will be meticulously analyzed and presented descriptively, offering insightful interpretations of the information encapsulated within the table.

resultsA preliminary search was performed in April 2024. Researchers concluded the study selection by July 2024. Data extraction, synthesis, report, and recommendations are expected to be finished by February 2025.

conclusionsThis systematic review will identify human-machine systems that enable communication in health services involving deaf patients, presenting the framework that includes usability and application in human contexts. We will present a comprehensive panel of findings, highlighting systems used to tackle communication barriers and offer a narrative comparison of current implementation practices. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/55427.

Indexed as

DeafnessPersons with Hearing DisabilitiesSign LanguageHumansSystematic Reviews as Topicartificial intelligencebiomedical technologycommunication aids for disabledcommunication barrierscomputer neural networkscomputer visiondeaf peoplegestureshearing losssign language

Identifiers

PMID39847417
PMCPMC11803331

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.