Deep learning model based on voice data from crowd-sourced audio samples uses subtle voice changes to detect COVID-19 cases<\/p>\n<\/h3>\n
<\/b><\/p>\n
<\/b><\/p>\n
FRIDAY, Sept. 9, 2022 (HealthDay News) — A deep learning model may help identify COVID-19 patients using voice data, according to a study presented at the European Respiratory Society International Congress 2022, held from Sept. 4 to 6 in Barcelona, Spain. The study was posted on a preprint server online and has not been published in a peer-reviewed journal.<\/p>\n
Wafaa Aljbawi, from Maastricht University in the Netherlands, and colleagues developed a deep learning model to identify COVID-19 using voice data from the Cambridge University dataset consisting of 893 audio samples, crowd-sourced from 4,352 participants using a COVID-19 Sounds app. The deep learning classification models included Long-Short Term Memory (LSTM) and Convolutional Neural Network. Their predictive power was compared to that of baseline classification models.<\/p>\n
The researchers found that the highest accuracy (89 percent) was achieved with the LSTM based on a Mel-frequency cepstral coefficients feature, which had sensitivity and specificity of 89 and 89 percent, respectively. Compared with results obtained in the state-of-the-art tests, such as the lateral flow test, the results of the proposed model suggest improved prediction accuracy for COVID-19 diagnosis.<\/p>\n
“The lateral flow test has a sensitivity of only 56 percent, but a higher specificity rate of 99.5 percent,” Aljbawi said in a statement. “This is important as it signifies that the lateral flow test is misclassifying infected people as COVID-19 negative more often than our test.”<\/p>\n
Abstract No. OA1626<\/a><\/p>\n