Screening COVID-19 by Swaasa AI platform using cough sounds: a cross-sectional study.

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.

Development and clinical validation of Swaasa AI platform for screening and prioritization of pulmonary TB.

Acoustic signal analysis has been employed in various medical devices. However, studies involving cough sound analysis to screen the potential pulmonary tuberculosis (PTB) suspects are very few. The main objective of this cross-sectional validation study was to develop and validate the Swaasa AI platform to screen and prioritize at risk patients for PTB based on the signature cough sound as well as symptomatic information provided by the subjects.

A volumetric analysis of soft-tissue changes in the aging midface using high-resolution MRI: implications for facial rejuvenation.

Background: The present study evaluated volumetric changes in the aging midface. Both young and old living subjects were studied using high-resolution magnetic resonance imaging (MRI) to investigate the distribution and volume of the muscle and subcutaneous components of the midface.

Methods: MRI with a customized radiofrequency coil was performed in 20 healthy Caucasian female volunteers equally divided between young (16 to 30 years) and old (>59 years) age groups.