Suppr超能文献

基于智能手机的听力筛查测试的准确性:系统评价。

Accuracy of smartphone-based hearing screening tests: a systematic review.

机构信息

Universidade Federal do Rio Grande do Norte - UFRN, Natal (RN), Brasil.

Faculdade de Odontologia de Bauru, Universidade de São Paulo - FOB/USP, Bauru (SP), Brasil.

出版信息

Codas. 2022 Feb 23;34(3):e20200380. doi: 10.1590/2317-1782/20212020380. eCollection 2022.

Abstract

PURPOSE

To verify the accuracy of smartphone apps to identify hearing loss.

RESEARCH STRATEGIES

A systematic review followed the PRISMA-DATA checklist. The search strategies were applied across four databases (Lilacs, PubMed, Scopus and Web of Science) and grey literature (Google Scholar, OpenGrey, and ProQuest Dissertations and Thesis).

SELECTION CRITERIA

The acronym PIRD was used in review. This included populations of any gender and all age groups. The Index test is the smartphone-based hearing screening test; the Reference test is the pure-tone audiometry, which is considered the gold reference for hearing diagnostics; the diagnosis was performed via validity data (sensitivity and specificity) to identify hearing loss and diagnostic studies.

DATA ANALYSIS

Two reviewers selected the studies in a two-step process. The risk of bias was assessed according to the criteria of the QUADAS-2.

RESULTS

Of 1395 articles, 104 articles were eligible for full-text reading and 17 were included. Only four met all criteria for methodological quality. All of the included studies were published in English between 2015 and 2020. The applications Digits-in noise Test (5 articles), uHear (4 articles), HearScreen (2 articles), hearTest (2 articles) and Hearing Test (2 articles) were the most studied. All this application showed sensitivity and specificity values between 75 and 100%. The other applications were EarScale, uHearing Test, Free field hearing (FFH) and Free Hearing Test.

CONCLUSION

uHear, Digit-in-Noise Test, HearTest and HearScreen have shown significant values of sensitivity and specificity and can be considered as the most accurate methods for screening of hearing impairment.

摘要

目的

验证智能手机应用程序识别听力损失的准确性。

研究策略

系统评价遵循 PRISMA-DATA 清单。搜索策略应用于四个数据库(Lilacs、PubMed、Scopus 和 Web of Science)和灰色文献(Google Scholar、OpenGrey 和 ProQuest Dissertations and Thesis)。

选择标准

综述中使用了 acronym PIRD。这包括任何性别和所有年龄组的人群。索引测试是基于智能手机的听力筛查测试;参考测试是纯音测听,被认为是听力诊断的黄金参考;诊断是通过有效性数据(敏感性和特异性)来识别听力损失和诊断研究进行的。

数据分析

两名审查员分两步选择研究。根据 QUADAS-2 的标准评估偏倚风险。

结果

在 1395 篇文章中,有 104 篇文章有资格进行全文阅读,有 17 篇文章被纳入。只有 4 篇完全符合方法学质量标准。所有纳入的研究均发表于 2015 年至 2020 年期间的英文期刊。应用最广泛的是 Digits-in noise Test(5 篇文章)、uHear(4 篇文章)、HearScreen(2 篇文章)、hearTest(2 篇文章)和 Hearing Test(2 篇文章)。所有这些应用程序的敏感性和特异性值在 75%到 100%之间。其他应用程序包括 EarScale、uHearing Test、Free field hearing (FFH) 和 Free Hearing Test。

结论

uHear、Digit-in-Noise Test、HearTest 和 HearScreen 显示出了显著的敏感性和特异性值,可被视为筛查听力障碍最准确的方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee11/9769434/19cb00a20d16/codas-34-3-e20200380-g01.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验