โก Quick Summary
The study introduces KLiP, a neural network-based tool designed for the early identification of listening difficulties in children aged 3-6 years. With a remarkable 90% sensitivity and 97% specificity, KLiP shows promise as a first-line surveillance tool in pediatric hearing healthcare.
๐ Key Details
- ๐ Dataset: 341 children (202 with typical hearing, 139 with hearing loss)
- ๐งฉ Features used: Eight behavioral items and six risk factors
- โ๏ธ Technology: Neural network model
- ๐ Performance: 90% sensitivity, 97% specificity
- ๐จโ๐ฉโ๐งโ๐ฆ Validation: 71 parent-child pairs in real-world settings
๐ Key Takeaways
- ๐ KLiP is a checklist aimed at identifying listening difficulties in preschool children.
- ๐ก The tool was developed through a multi-phase process involving expert evaluation and inter-rater reliability assessment.
- ๐ High KLiP scores (โฅ0.5) indicate a need for further audiological attention.
- ๐ Low scores (<0.5) suggest typical hearing abilities without developmental concerns.
- ๐ KLiP’s implementation as an online platform enhances awareness and provides educational resources for parents and teachers.
- ๐ The study emphasizes the importance of early identification of listening difficulties in children.
- ๐งโโ๏ธ The tool supports pediatric hearing healthcare frameworks by promoting timely interventions.
๐ Background
Listening difficulties in early childhood can significantly impact a child’s development and learning. Early identification is crucial for effective intervention. Traditional methods of assessing listening abilities can be subjective and inconsistent, highlighting the need for a more reliable and systematic approach. The development of KLiP aims to address this gap by utilizing advanced technology to enhance early detection.
๐๏ธ Study
The study was conducted through a comprehensive multi-phase process, beginning with a systematic review to create preliminary items for the KLiP checklist. Following expert evaluations for content validity, the researchers assessed inter-rater reliability between parents and teachers. The neural network model was then developed using data from 341 children, with subsequent real-world validation testing involving 71 parent-child pairs.
๐ Results
The KLiP checklist successfully identified eight behavioral items and six risk factors associated with listening difficulties. The neural network model demonstrated impressive performance, achieving 90% sensitivity and 97% specificity in distinguishing between children with and without hearing loss. Preliminary testing indicated that high KLiP scores effectively identified children needing further audiological evaluation.
๐ Impact and Implications
The introduction of KLiP as a surveillance tool has the potential to transform how listening difficulties are identified in preschool children. By providing a structured and reliable method for early detection, KLiP can facilitate timely interventions, ultimately improving outcomes for children with hearing challenges. The online platform aspect further enhances its accessibility, promoting awareness among parents and educators.
๐ฎ Conclusion
KLiP represents a significant advancement in the early identification of listening difficulties in young children. With its high sensitivity and specificity, this neural network-based tool could become a vital component of pediatric hearing healthcare. Continued research and implementation of KLiP may lead to improved awareness and support for children facing listening challenges, paving the way for better developmental outcomes.
๐ฌ Your comments
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KLiP: A neural network-based surveillance tool for early identification of listening difficulties in children aged 3-6ย years.
Abstract
PURPOSE: To develop and validate a neural network-based Kid’s Listening Performance Checklist (KLiP) for early identification of listening difficulties in children aged 3-6 years, and to evaluate its effectiveness as a surveillance tool in real-world settings.
METHODS: Development followed a multi-phase process: (1) systematic resource review to create preliminary items, (2) expert evaluation for content validity (7 experts), (3) assessment of inter-rater reliability between parents and teachers (182 parent-teacher pairs), (4) discriminant validity testing, (5) neural network model development trained on data from 341 children (202 with typical hearing, 139 with hearing loss), and (6) real-world validation testing with 71 parent-child pairs who completed both KLiP and hearing screening.
RESULTS: The KLiP checklist comprises eight discriminating behavioral items and six risk factors. The neural network model achieved 90% sensitivity and 97% specificity in distinguishing between children with and without hearing loss in the development dataset. In preliminary real-world testing, high KLiP scores (โฅ0.5) appeared to identify children requiring further audiological attention, while low scores (<0.5) strongly indicated typical hearing abilities in children without developmental concerns.
CONCLUSIONS: This preliminary study suggests that KLiP may demonstrate potential as a first-line surveillance tool for identifying listening difficulties in preschool children. Beyond surveillance, its implementation as an online platform with automated risk assessment and educational resources promotes awareness of listening difficulties among parents and teachers, supporting early identification within the pediatric hearing healthcare framework.
Author: [‘Hung YC’, ‘Chang YP’, ‘Chan YC’, ‘Lo M’, ‘Chen PC’, ‘Chang ST’, ‘Liao Y’, ‘Hong HM’]
Journal: Int J Pediatr Otorhinolaryngol
Citation: Hung YC, et al. KLiP: A neural network-based surveillance tool for early identification of listening difficulties in children aged 3-6ย years. KLiP: A neural network-based surveillance tool for early identification of listening difficulties in children aged 3-6ย years. 2026; 201:112700. doi: 10.1016/j.ijporl.2025.112700