AI algorithm interpreting data from a wearable sensor bracelet that monitors changes in physiologic parameters might allow early detection
THURSDAY, June 23, 2022 (HealthDay News) — Wearable sensor technology could facilitate detection of presymptomatic COVID-19, according to a study published online June 21 in BMJ Open.
Martin Risch, M.D., from the Dr. Risch Medical Laboratory in Vaduz, Liechtenstein, and colleagues conducted an interim analysis of a prospective cohort study to examine machine learning-based identification of presymptomatic COVID-19. A total of 1,163 participants wore the Ava-bracelet that measured respiratory rate (RR), heart rate (HR), HR variability (HRV), wrist-skin temperature (WST), and skin perfusion nightly; 1.5 million hours of physiological data were recorded.
The researchers found that COVID-19 was confirmed in 127 participants, 66 of whom had worn their device from baseline to symptom onset and were included in the analysis. Significant changes in RR, HR, HRV, HRV ratio, and WST device-measured physiological parameters were observed during the incubation, presymptomatic, symptomatic, and recovery periods of COVID-19 relative to baseline. Measurements were extracted from day 10 to day 2 before symptom onset from 66 participants; these measurements were incorporated into a training set (70 percent of participants) and tested in a test cohort (30 percent of participants). When detecting COVID-19 up to two days prior to symptom onset, the developed long short-term memory based recurrent neural network algorithm had a recall (sensitivity) of 0.73 and 0.68 in the training and testing sets, respectively.
“Our research shows how these devices, partnered with artificial intelligence, can push the boundaries of personalized medicine and detect illnesses prior to symptom onset, potentially reducing virus transmission in communities,” the authors write.
Several authors disclosed financial ties to pharmaceutical and medical technology companies, including Ava AG.
Abstract/Full Text (subscription or payment may be required)
2005 to 2023 Saw More U.S. Women Never Screened for Cervical Cancer
Machine Learning Model IDs Heart Failure With Reduced Ejection Fraction Using Routine Lab Indicators
Addition of CAC to PREVENT-ASCVD Equations May Improve Predictive Utility in Certain Patients
NEWS2 Scores Can ID Patients With Suspected Sepsis Needing Timely Treatment
Flu Treatment With Oseltamivir Linked to Reduced Risk for Pediatric ICU Admissions, Length of Stay
Therapeutic Hypothermia Duration Explored in Comatose OHCA Survivors
CHEST Issues Guidelines for Management of Adult Bronchiectasis
Autonomous Oxygen Titration Increases Proportion of Time in Normoxemia