Evaluating Listening Performance for COVID-19 Detection by Clinicians and Machine Learning: Comparative Study - Jing Han; Marco Montagna; Andreas Grammenos; Tong Xia; Erika Bondareva; Chloë Siegele-Brown; Jagmohan Chauhan; Ting Dang; Dimitris Spathis; R Andres Floto; Pietro Cicuta; Cecilia Mascolo (JMIR Publications, 2023)
BackgroundTo date, performance comparisons between men and machines have been carried out in many health domains. Yet machine learning (ML) models and human performance comparisons in audio-based respiratory diagnosis remain largely unexplored. ObjectiveThe primary objective of this study was to compare human clinicians and an ML model in predicting COVID-19 from respiratory sound recordings. MethodsIn this study, we compared human clinicians and an ML model in predicting COVID-19 from respiratory sound recordings. Prediction performance on 24 audio samples (12 tested positive) made by 36 clinicians with experience in treating COVID-19 or other respiratory illnesses was compared with predictions made by an ML model trained on 1162 samples. Each sample consisted of voice, cough, and breathing sound recordings from 1 subject, and the length of each sample was around 20 seconds. We also investigated whether combining the predictions of the model and human experts could further enhance the performance in terms of both accuracy and confidence. ResultsThe ML model outperformed the clinicians, yielding a sensitivity of 0.75 and a specificity of 0.83, whereas the best performance achieved by the clinicians was 0.67 in terms of sensitivity and 0.75 in terms of specificity. Integrating the clinicians' and the model's predictions, however, could enhance performance further, achieving a sensitivity of 0.83 and a specificity of 0.92. ConclusionsOur findings suggest that the clinicians and the ML model could make better clinical decisions via a cooperative approach and achieve higher confidence in audio-based respiratory diagnosis.