Classification Techniques for Diagnosing Respiratory Sounds in Infants and Children

Gouda, Alaa; Shehaby, Saleh el; Diaa, Nancy; Abougabal, Mohamed S.

doi:10.1109/ccwc.2019.8666608

Cited by 6 publications

(4 citation statements)

References 18 publications

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“…Neural Networks and SVM were the most utilized AI models. Notably, Hariharan (2018) [10] and Gouda (2019) [11] achieved 100% accuracy for diagnosing asphyxia and wheezing, using Improved Binary Dragonfly Optimization and Artificial Neural Networks, respectively.…”

Section: Model Accuracymentioning

confidence: 99%

“…A table listing the country associated with each study and their reference number. Sharan 2021 [45] Austria [1] Pokorny 2022 [25] Brazil [1] Ribeiro 2020 [33] Canada [4] Khalilzad 2022 [65] Khalilzad 2022 [66] Salehian Matikolaie 2021 [68] Sharma 2020 [30] China [6] Chen 2023 [16] Wang 2019a [54] Wang 2019b [55] Wu 2019 [21] Zhang 2020 [29] Zhang 2022 [64] Croatia [1] Mazic 2015 [48] Czech Republic [1] Barua 2023 [12] Kotarba 2020 [59] Egypt [1] Badreldine 2018 [34] Gouda 2019 [11] France [2] Bokov 2015 [47] Deng 2017 [18] Hungary [1] Tulics 2018 [57] Table A2. Cont.…”

Section: Appendix Bmentioning

confidence: 99%

“…Amrulloh 2018 [40] Cho 2019 [17] Ji 2021 [35] MacFarlane 2022 [23] McGinnis 2019 [63] Nakai 2017 [24] Pokorny 2022 [25] Tartarisco 2021 [56] Tulics 2018 [57] Short-Time Fourier Transform (STFT) Gouda 2019 [11] Signal-to-Noise Ratio (SNR) Jayasree 2021 [9] Chi 2022 [26] Cho 2019 [17] McGinnis 2019 [63] Nafisah 2019 [69] Satar 2022 [38] Appendix F…”

Section: Appendix Ementioning

confidence: 99%

“…Aggarwal 2020 [13] Aggarwal 2018 [14] Aggarwal 2020 [15] Asgari 2021 [22] Badreldine 2018 [34] Barua 2023 [12] Bokov 2015 [47] Deng 2017 [18] Dubey 2018 [53] Gouda 2019 [11] Ji 2019 [36] Khalilzad 2022 [65] Khalilzad 2022 [66] Lee 2020 [19] Table A6. Cont.…”

Section: Appendix Ementioning

confidence: 99%

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Voice as a Biomarker of Pediatric Health: A Scoping Review

Rogers,

Hseu,

Kim

et al. 2024

Children

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The human voice has the potential to serve as a valuable biomarker for the early detection, diagnosis, and monitoring of pediatric conditions. This scoping review synthesizes the current knowledge on the application of artificial intelligence (AI) in analyzing pediatric voice as a biomarker for health. The included studies featured voice recordings from pediatric populations aged 0–17 years, utilized feature extraction methods, and analyzed pathological biomarkers using AI models. Data from 62 studies were extracted, encompassing study and participant characteristics, recording sources, feature extraction methods, and AI models. Data from 39 models across 35 studies were evaluated for accuracy, sensitivity, and specificity. The review showed a global representation of pediatric voice studies, with a focus on developmental, respiratory, speech, and language conditions. The most frequently studied conditions were autism spectrum disorder, intellectual disabilities, asphyxia, and asthma. Mel-Frequency Cepstral Coefficients were the most utilized feature extraction method, while Support Vector Machines were the predominant AI model. The analysis of pediatric voice using AI demonstrates promise as a non-invasive, cost-effective biomarker for a broad spectrum of pediatric conditions. Further research is necessary to standardize the feature extraction methods and AI models utilized for the evaluation of pediatric voice as a biomarker for health. Standardization has significant potential to enhance the accuracy and applicability of these tools in clinical settings across a variety of conditions and voice recording types. Further development of this field has enormous potential for the creation of innovative diagnostic tools and interventions for pediatric populations globally.

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Section: Model Accuracymentioning

confidence: 99%