Use of artificial intelligence to develop predictive algorithms of cough and PCR-confirmed COVID-19 infections based on inputs from clinical-grade wearable sensors.

There have been over 769 million cases of COVID-19, and up to 50% of infected individuals are asymptomatic. The purpose of this study aimed to assess the use of a clinical-grade physiological wearable monitoring system, ANNE One, to develop an artificial intelligence algorithm for (1) cough detection and (2) early detection of COVID-19, through the retrospective analysis of prospectively collected physiological data from longitudinal wear of ANNE sensors in a multicenter single arm study of subjects at high risk for COVID-19 due to occupational or home exposures. The study employed a two-fold approach: cough detection algorithm development and COVID-19 detection algorithm development. For cough detection, healthy individuals wore an ANNE One chest sensor during scripted activity. The final performance of the algorithm achieved an F-1 score of 83.3% in twenty-seven healthy subjects during biomarker validation. In the COVID-19 detection algorithm, individuals at high-risk for developing COVID-19 because of recent exposures received ANNE One sensors and completed daily symptom surveys. An algorithm analyzing vital parameters (heart rate, respiratory rate, cough count, etc.) for early COVID-19 detection was developed. The COVID-19 detection algorithm exhibited a sensitivity of 0.47 and specificity of 0.72 for detecting COVID-19 in 325 individuals with recent exposures. Participants demonstrated high adherence (≥ 4 days of wear per week). ANNE One shows promise for detection of COVID-19. Inclusion of respiratory biomarkers (e.g., cough count) enhanced the algorithm's predictive ability. These findings highlight the potential value of wearable devices in early disease detection and monitoring.

A new wearable diagnostic home sleep testing platform: comparison with available systems and benefits of multinight assessments.

STUDY OBJECTIVES: We assessed the real-world performance of the ANNE Sleep system against 2 Food and Drug Administration-cleared home sleep testing platforms and the intraindividual night-to-night variability of respiratory event index measured by ANNE Sleep. METHODS: We evaluated the home performance of the ANNE Sleep system compared with 2 Food and Drug Administration-cleared home sleep testing platforms (WatchPAT: n = 29 and Alice NightOne: n = 46) during a synchronous night with unsupervised patient application. Additionally, we evaluated night-to-night variability of respiratory event index and total sleep time using the ANNE Sleep system (n = 30). RESULTS: For the diagnosis of moderate and severe obstructive sleep apnea, the ANNE Sleep system had a positive percent agreement of 58% (95% confidence interval, 28-85%) and a negative percent agreement of 100% (95% confidence interval, 80-100%) compared to WatchPAT. The positive and negative percent agreement for ANNE Sleep vs Alice NightOne was 85% (95% confidence interval, 66-96%) and 95% (95% confidence interval, 74-100%). There were no differences in mean total sleep time or respiratory event index across multiple nights of monitoring with ANNE. There were no differences consistent with a first-night effect but testing multiple nights reclassified obstructive sleep apnea severity in 5 (17%) individuals and detected 3 additional cases of moderate disease, with only a 12% (standard deviation, 28%) mean fluctuation in respiratory event index from the first night of testing compared to a mean of multiple nights. Overall, 80% of users found ANNE comfortable and easy to use. CONCLUSIONS: ANNE Sleep exhibited stronger concordance with Alice NightOne compared to WatchPAT. While we illustrated low night-to-night variability for ANNE Sleep, the results suggest multiple nights increased detection of moderate or severe obstructive sleep apnea. CLINICAL TRIAL REGISTRATION: Registry: ClinicalTrials.gov; Name: ANNE Diagnostic Agreement With Home Sleep Testing; URL: https://clinicaltrials.gov/ct2/show/NCT05421754; Identifier: NCT05421754. CITATION: Walter J, Lee JY, Blake S, et al. A new wearable diagnostic home sleep testing platform: comparison with available systems and benefits of multinight assessments. J Clin Sleep Med. 2023;19(5):865-872.

A single-arm, open-label, multicenter, and comparative study of the ANNE sleep system vs polysomnography to diagnose obstructive sleep apnea.

STUDY OBJECTIVES: Evaluate per-patient diagnostic performance of a wireless dual-sensor system (ANNE sleep) compared with reference standard polysomnography (PSG) for the diagnosis of moderate and severe obstructive sleep apnea (OSA) with a minimum prespecified threshold of 80% for both sensitivity and specificity. METHODS: A multicenter clinical trial was conducted to evaluate ANNE sleep vs PSG to diagnose moderate and severe OSA in individuals 22 years or older. For each testing approach, apnea-hypopnea index (AHI) was manually scored and averaged by 3 registered sleep technologists blinded to the other system. Average variations > 15% were adjudicated by a sleep medicine physician. RESULTS: In a total of n = 225 participants (mean age 53 years, range 22-88 years), PSG diagnosed 30% (n = 68) of participants with moderate or severe OSA (AHI ≥ 15 events/h) compared to 29% (n = 65) diagnosed by ANNE sleep (P = .55). The sensitivity and specificity for ANNE sleep were 90% (95% confidence interval: 80-96%) and 98% (95% confidence interval: 94-99%), respectively. Strong correlation was shown in terms of final AHI (r = .93), with an average AHI bias of 0.5 (95% limits of agreement: -12.8 to 11.8). The majority of users noted comfort with using the ANNE sleep in the home setting. No adverse events were noted. CONCLUSIONS: Using PSG as the gold standard, ANNE sleep demonstrated high sensitivity and specificity for the diagnosis of moderate or severe OSA. CLINICAL TRIAL REGISTRATION: Registry: ClinicalTrials.gov; Name: Comparative Study of the ANNE™ One System to Diagnose Obstructive Sleep Apnea; URL: https://clinicaltrials.gov/ct2/show/NCT04643782; Identifier: NCT04643782. CITATION: Davies C, Lee JY, Walter J et al. A single-arm, open-label, multicenter, and comparative study of the ANNE sleep system vs polysomnography to diagnose obstructive sleep apnea. J Clin Sleep Med. 2022;18(12):2703-2712.