Development and application of AI-assisted breathing sensors for the detection of pulmonary abnormalities in non-communicable diseases (BREATH)

Changes in human breathing pattern and rate are clinical manifestations of many diseases involving respiratory changes and failure, including asthma, anemia, septic shock, anxiety attacks, pulmonary embolisms, heart attacks, emphysema, pneumonia, hypothyroidism and so on. Current methods of detecting changes in breathing patterns are very limited and cumbersome in clinical settings. For instance, spirometry can be used to measure lung function and volume at a single point in time only, therefore it’s use in inpatient setting for continuous assessment is questionable. Plethysmography is bulky and requires patients to perform the test in a chamber with specific steps to follow for accurate results.