Key result
Airmod CNN-BiGRU model achieves ~0.84 F1 score for real-time inhalation detection.
Why the study?
Tracheal auscultation is recommended for monitoring ventilation during procedural sedation and monitored anesthesia care, but continuous auscultation with a conventional stethoscope is impractical, susceptible to ambient noise, and difficult to interpret.
Does the Airmod active noise cancellation and CNN-BiGRU deep learning system accurately detect inhalation and exhalation events from continuous tracheal sounds in patients undergoing procedural sedation?
Observational (n=369)
Yes
Does the Airmod active noise cancellation and CNN-BiGRU deep learning system accurately detect inhalation and exhalation events from continuous tracheal sounds in patients undergoing procedural sedation?
The FDA-cleared Airmod system demonstrates high sensitivity (0.828) and positive predictive value (0.832) for real-time acoustic detection of inhalations during procedural sedation by combining active noise cancellation with a CNN-BiGRU deep learning model.
Answers are grounded in this paper and cite their sources.
May aid real-time respiratory monitoring in sedation; leaves open prospective clinical validation before adoption.
Tracheal auscultation is recommended by clinical practice guidelines, and required by some U.S. state regulations, for monitoring ventilation during procedural sedation and monitored anesthesia care. However, continuous auscultation with a conventional stethoscope is impractical, susceptible to ambient noise, and difficult to interpret. We developed the Artificial Intelligence Respiratory MOnitoring Device (Airmod), an FDA-cleared software as a medical device (510(k) K242798) that continuously monitors tracheal sounds and estimates breath-to-breath respiratory rate in real time. In this paper, we describe its software architecture and key processing modules. Ambient noise is suppressed by active noise control, using a normalized least-mean-square adaptive filter with a dual-microphone electronic stethoscope. A multiclass convolutional bidirectional gated recurrent unit (CNN-BiGRU) model detects inhalations and exhalations in 15-s inference windows. It was trained on HF_Tracheal_V2, a new database of 373 continuous tracheal sound recordings (8,521 min; 107,644 labeled inhalations) from 369 subjects under sedation. To handle continuous acoustic streaming, Airmod runs inference on overlapping 15-s windows. It refines the detected inhalations by removing incomplete events, low-energy false positives, and double counts, concatenates them into a history list, and estimates respiratory rate from the mean period of the last three breaths. On the test set, at the default 2-s prediction interval with refinement enabled, inhalation detection achieved a sensitivity of 0.828, a positive predictive value of 0.832, and an F1-score of 0.830. Airmod also broadcasts the noise-cancelled tracheal sound and displays a real-time spectrogram to help clinicians recognize respiratory abnormalities.
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Huang et al. (2026) conducted an observational in Diagnostic or therapeutic procedures under monitored anesthesia care (MAC) or procedural sedation (n=369). Airmod (CNN-BiGRU deep learning and active noise cancellation system for tracheal sound analysis) vs. Expert-labeled tracheal sound ground truth was evaluated on Inhalation event detection F1 score (with prediction refinement at 5-second prediction interval). Airmod's CNN-BiGRU model achieved an F1 score of 0.838 (sensitivity 0.828, PPV 0.848) for real-time inhalation detection at a 5-second interval with prediction refinement.
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