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Language Development

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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

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Using Machine Learning for the Automated Segmentation and Detection of Swallows Obtained by Digital Cervical

Stephen So¹, Timothy Tadj¹, Belinda Schwerin¹

¹School of Engineering and Built Environment, Griffith University, Parklands Dr, Southport, QLD, 4215, Australia.

|September 11, 2025

Summary

Machine learning accurately identifies preterm infant swallows using digital cervical auscultation (CA). This automated approach aids objective CA in special care nurseries.

Keywords:

Cervical auscultation Deglutition Machine learning Neonate Preterm Signal processing Swallowing sounds

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Area of Science:

Biomedical Engineering
Neonatal Medicine
Machine Learning

Background:

Clinical use of digital cervical auscultation (CA) for swallowing is hindered by manual segmentation.
Machine learning (ML) shows promise for automated swallow sound identification in adults and children.
No ML data exists for swallow sound analysis in preterm neonates.

Purpose of the Study:

To assess the accuracy of ML with transfer learning for identifying and segmenting swallow sounds in preterm neonates.
To develop an automated method for analyzing swallowing sounds in a vulnerable population.

Main Methods:

Collected thin fluid swallow sounds from 78 preterm neonates (25-36 weeks gestation).
Employed a deep convolutional neural network (DCNN) pre-trained for audio event classification.
Used DCNN embeddings to train a feedforward neural network for swallow detection.

Main Results:

Achieved high overall accuracy (94%) in identifying preterm infant swallows.
Demonstrated superior performance for bottle-feeding swallows (95% sensitivity, 96% specificity) versus breastfeeding swallows (95% sensitivity, 92% specificity).

Conclusions:

Transfer learning enables accurate identification and segmentation of digital swallowing sounds in preterm neonates.
This ML model can support a digital CA application for objective clinical use in special care nurseries.