ABSTRACT
The Advent of Artificial Intelligence (AI) has led to the use of auditory data for detecting various diseases, including COVID-19. SARS-CoV-2 infection has claimed more than six million lives to date and therefore, needs a robust screening technique to control the disease spread. In the present study we created and validated the Swaasa AI platform, which uses the signature cough sound and symptoms presented by patients to screen and prioritize COVID-19 patients. We collected cough data from 234 COVID-19 suspects to validate our Convolutional Neural Network (CNN) architecture and Feedforward Artificial Neural Network (FFANN) (tabular features) based algorithm. The final output from both models was combined to predict the likelihood of having the disease. During the clinical validation phase, our model showed a 75.54% accuracy rate in detecting the likely presence of COVID-19, with 95.45% sensitivity and 73.46% specificity. We conducted pilot testing on 183 presumptive COVID subjects, of which 58 were truly COVID-19 positive, resulting in a Positive Predictive Value of 70.73%. Due to the high cost and technical expertise required for currently available rapid screening methods, there is a need for a cost-effective and remote monitoring tool that can serve as a preliminary screening method for potential COVID-19 subjects. Therefore, Swaasa would be highly beneficial in detecting the disease and could have a significant impact in reducing its spread.
Subject(s)
COVID-19ABSTRACT
Ab s t r ac t Purpose: Pregnant women with coronavirus disease-2019 (COVID-19) are at an increased risk for preterm delivery, stillbirth, and severe acute respiratory illness which is mainly attributed to the physiological and immunological changes of pregnancy. The aim of this study was to assess the knowledge, attitude, and practice (KAP) regarding the health effects of the COVID-19 pandemic and preventive measures among pregnant women from South India. Materials and methods: A descriptive cross-sectional study was carried out among 505 antenatal women at the Department of Obstetrics and Gynecology, Government Medical College, Kozhikode, Kerala, India from July 2021 to September 2021. After taking informed written consent, the study participants were enrolled using a standardized and validated questionnaire. The data were analyzed in Statistical Package for Social Sciences (SPSS), version 15.0, for Windows (SPSSTM Inc., Chicago, IL, USA). Results: The mean age of the enrolled antenatal women was 26.53 years (SD ± 5.14). History of past or present COVID-19 was given by 97 (19.2%) study participants. The first and second doses of the COVID-19 vaccine were taken by 123 (24.3%) and 55 women (10.9%), respectively. About 491 women (97.2%) were perceived to have a piece of good knowledge, and 14 (34.7%) had a moderate knowledge. The attitudes of 468 (92.7%), 35 (6.9%), and two (0.4%) women were good, moderate, and poor, respectively. The preventive practice by all the participants was good. Conclusion: This study concludes that the KAP of pregnant women from South India regarding COVID-19 health effects and preventive measures are good. Antenatal clinics must ensure regular informative sessions stressing the importance of COVID-19 preventive behaviors. © The Author(s). 2022 Open Access.