Smartwatch-based algorithm for early detection of pulmonary infection: Validation and performance evaluation.

Abstract

The proliferation of smart devices provides the possibility of early detection of the signs of pulmonary infections (PI). This study validates a smartwatch-based algorithm to monitor the risk of PI in adults. An algorithm that runs on smartwatches was developed and tested in 87 patients with PI and 408 healthy subjects. The algorithm examines heart rate variability, respiratory rate, oxygen saturation, body temperature, and cough sound. It was embedded into the Respiratory Health Study app for a smartwatch to detect the risk of PI and was further validated in the hospital. Doctors diagnosed PI using a clinical evaluation, lab tests, and imaging examination, the gold standard for diagnosis. The accuracy, sensitivity, and specificity of the algorithm predicting PI were evaluated. In all, 80 patients with PI and 85 healthy volunteers were recruited to validate the accuracy of the algorithm. The area under the curve of the algorithm for predicting PI was 0.86 (95% confidence interval: 0.82-0.91) (P < 0.001). Compared to the gold standard, the overall accuracy of the algorithm was 85.9%, the sensitivity was 81.4%, and the specificity was 90.4%. The algorithm for heart rate, respiratory rate, oxygen saturation, and body temperature had an accuracy of 68.2%, and the accuracy of the algorithm including cough sound was 82.6%. Our wearable system facilitated the detection of risk of PI. Multi-source features were useful for enhancing the performance of the lung infection screening algorithm. Chinese Clinical Trial Registry of the International Clinical Trials Registry Platform of the World Health Organization ChiCTR2100050843; https://www.chictr.org.cn/showproj.html?proj = 126556.