Evidence map›Paper›PMID 40325123›Full record

ArticleScientific reports2025

Behavior recognition technology based on deep learning used in pediatric behavioral audiometry.

Wen Xie, Chunhua Li, Haisen Peng, Yuehui Liu, Zhilin Zhang, Xiaogang Cheng, Jiali Liu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Wen XieDepartment of Otolaryngology, Head and Neck Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China.
Chunhua LiDepartment of Otolaryngology, Head and Neck Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China.
Haisen PengDepartment of Otolaryngology, Head and Neck Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China.
Yuehui LiuDepartment of Otolaryngology, Head and Neck Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China.
Zhilin ZhangDepartment of Otolaryngology, Head and Neck Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China.
Xiaogang ChengCollege of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China. chengxg@njupt.edu.cn.
Jiali LiuDepartment of Otolaryngology, Head and Neck Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China. 475302391@qq.com.

Funding

The National Natural Science Foundation of China 62371253The Science and Technology Department of Jiangxi Province 20202BBGL73017
6 · The paper itself

Abstract

This study aims to explore the feasibility and accuracy of deep learning-based pediatric behavioral audiometry. The research provides a dedicated pediatric posture detection dataset, which contains a large number of video clips from children's behavioral hearing tests, encompassing various typical hearing test actions. A detection platform based on this dataset is also constructed, named intelligent diagnostic model of pediatric hearing based on optimized transformer (DoT); further, an estimation model of patient skeletal keypoints based on optimized transformer (POTR) was proposed to estimate human skeleton points. Based on this, the DoT approach was handled to perform posture recognition on videos of children undergoing behavioral hearing tests, thus enabling an automated hearing testing process. Through this platform, children's movements can be monitored and analyzed in real-time, allowing for the assessment of their hearing levels. Moreover, the study establishes decision rules based on specific actions, combining professional knowledge and experience in audiology to evaluate children's hearing levels based on their movement status. Firstly, we gathered image and video data related to posture in the process of conditioned play audiometry to test the hearing of 120 children aged 2.5 to 6 years old. Next, we built and optimized a deep learning model suitable for pediatric posture recognition. Finally, in the deployment and application phase, we deployed the trained pediatric posture recognition model into real-world application environments. We found that for children aged 2.5 - 4 years, the sensitivity of artificial behavior audiometry (0.900) was not as high as that of AI behavior audiometry (0.929), but the specificity of artificial behavior audiometry (0.824) and Area Under Curve (AUC) (0.901) was higher than that of AI behavior audiometry. For children aged 4-6 years, the sensitivity (0.943), specificity (0.947), and AUC (0.924) of artificial behavioral audiometry were higher than those of AI behavioral audiometry. The application of these rules facilitates objective assessment and diagnosis of children's hearing, providing essential foundations for early screening and treatment of children with hearing disorders.Trial Registration: Chinese Clinical Trial Registry: Registration number ChiCTR2100050416.

Indexed as

AudiometryDeep LearningChildChild, PreschoolFemaleHearingHearing TestsHumansMalePostureVideo RecordingAIImage processingPediatric behavioral audiometryPediatric hearing lossSkeleton keypointsTransformer

Identifiers

PMID40325123
PMCPMC12053576

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.