Heo, Seokhyeon Uhm, Kyeong Eun Yuk, Doyoung Kwon, Bo Mi Yoo, Byounghyun Kim, Jisoo Lee, Jongmin 2024-09-14T06:30:35Z 2024-09-14T06:30:35Z 2024-09-13 2024-08 https://pubs.kist.re.kr/handle/201004/150587 Dysphagia, a disorder affecting the ability to swallow, has a high prevalence among the older adults and can lead to serious health complications. Therefore, early detection of dysphagia is important. This study evaluated the effectiveness of a newly developed deep learning model that analyzes syllable-segmented data for diagnosing dysphagia, an aspect not addressed in prior studies. The audio data of daily conversations were collected from 16 patients with dysphagia and 24 controls. The presence of dysphagia was determined by videofluoroscopic swallowing study. The data were segmented into syllables using a speech-to-text model and analyzed with a convolutional neural network to perform binary classification between the dysphagia patients and control group. The proposed model in this study was assessed in two different aspects. Firstly, with syllable-segmented analysis, it demonstrated a diagnostic accuracy of 0.794 for dysphagia, a sensitivity of 0.901, a specificity of 0.687, a positive predictive value of 0.742, and a negative predictive value of 0.874. Secondly, at the individual level, it achieved an overall accuracy of 0.900 and area under the curve of 0.953. This research highlights the potential of deep learning modal as an early, non-invasive, and simple method for detecting dysphagia in everyday environments. English Nature Publishing Group Deep learning approach for dysphagia detection by syllable-based speech analysis with daily conversations Article 10.1038/s41598-024-70774-z 1 Scientific Reports, v.14, no.1 Scientific Reports 14 1 Y scie scopus 001304007200022 2-s2.0-85202872228 Multidisciplinary Sciences Science & Technology - Other Topics Article ASPIRATION VOICE Dysphagia Deep learning Conversations Syllable-based speech analysis Speech-to-text model Artificial intelligence