Machine Learning-based Voice Assessment for the Detection of Positive and Recovered COVID-19 Patients
| Autor: | Carlo Robotti, Giovanni Costantini, Giovanni Saggio, Valerio Cesarini, Anna Calastri, Eugenia Maiorano, Davide Piloni, Tiziano Perrone, Umberto Sabatini, Virginia Valeria Ferretti, Irene Cassaniti, Fausto Baldanti, Andrea Gravina, Ahmed Sakib, Elena Alessi, Filomena Pietrantonio, Matteo Pascucci, Daniele Casali, Zakarya Zarezadeh, Vincenzo Del Zoppo, Antonio Pisani, Marco Benazzo |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Coronavirus disease 2019 (COVID-19)
Screening test Settore ING-INF/01 Multiple Loci VNTR Analysis Machine learning computer.software_genre Asymptomatic Article Speech and Hearing Sensitivity Medicine NS nasal swab Phonation ML machine learning SS serum sample Accuracy Sustained vowel MLVA machine learning-based voice assessment Surveillance business.industry SARS-CoV-2 Healthy subjects voice LPN and LVN Voice assessment Test (assessment) machine learning Otorhinolaryngology Cough Artificial intelligence medicine.symptom business computer recovered |
| Zdroj: | Journal of Voice |
| Popis: | Many virological tests have been implemented during the Coronavirus Disease 2019 (COVID-19) pandemic for diagnostic purposes, but they appear unsuitable for screening purposes. Furthermore, current screening strategies are not accurate enough to effectively curb the spread of the disease. Therefore, the present study was conducted within a controlled clinical environment to determine eventual detectable variations in the voice of COVID-19 patients, recovered and healthy subjects, and also to determine whether machine learning-based voice assessment (MLVA) can accurately discriminate between them, thus potentially serving as a more effective mass-screening tool. Three different subpopulations were consecutively recruited: positive COVID-19 patients, recovered COVID-19 patients and healthy individuals as controls. Positive patients were recruited within 10 days from nasal swab positivity. Recovery from COVID-19 was established clinically, virologically and radiologically. Healthy individuals reported no COVID-19 symptoms and yielded negative results at serological testing. All study participants provided three trials for multiple vocal tasks (sustained vowel phonation, speech, cough). All recordings were initially divided into three different binary classifications with a feature selection, ranking and cross-validated RBF-SVM pipeline. This brough a mean accuracy of 90.24%, a mean sensitivity of 91.15%, a mean specificity of 89.13% and a mean AUC of 0.94 across all tasks and all comparisons, and outlined the sustained vowel as the most effective vocal task for COVID discrimination. Moreover, a three-way classification was carried out on an external test set comprised of 30 subjects, 10 per class, with a mean accuracy of 80% and an accuracy of 100% for the detection of positive subjects. Within this assessment, recovered individuals proved to be the most difficult class to identify, and all the misclassified subjects were declared positive; this might be related to mid and short-term vocal traces of COVID-19, even after the clinical resolution of the infection. In conclusion, MLVA may accurately discriminate between positive COVID-19 patients, recovered COVID-19 patients and healthy individuals. Further studies should test MLVA among larger populations and asymptomatic positive COVID-19 patients to validate this novel screening technology and test its potential application as a potentially more effective surveillance strategy for COVID-19. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|