Use of Transfer Learning for the Automated Segmentation and Detection of Swallows via Digital Cervical Auscultation in Children.

ABSTRACT: Digital cervical auscultation (CA) has high diagnostic test accuracy in the detection of aspiration in children. However, the clinical application of digital CA is limited because swallow sound recordings require manual segmentation by trained experts, which is time consuming and not feasible in clinical practice. The automated detection of swallowing sounds in adults from sound recordings have reported accuracies between 76 and 95%. No equivalent literature exists for the automated detection of swallowing sounds in children. This study aimed to establish whether automated machine learning using a transfer learning approach can accurately detect and segment swallows from digital CA recordings in children. Swallow sounds were collected from 16 typically developing children, median age 18 mo

SUBMITTER: So S 

PROVIDER: S-EPMC12662899 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature