Airmod

Airmod is a non-invasive software medical device designed for continuous respiratory rate monitoring in adult patients undergoing procedural sedation or anesthesia. It leverages an AI-based algorithm to analyze acoustic breathing sounds via an electronic stethoscope, demonstrating non-inferiority to reference systems in clinical trials and meeting regulatory safety and effectiveness standards.

Airmod, a software medical device developed by Heroic Faith International Ltd., received a determination for marketability on February 28, 2025. This device is engineered for the continuous, non-invasive monitoring of respiratory rates in adult patients undergoing procedural sedation or anesthesia. Its core functionality relies on an AI-based algorithm that detects and analyzes acoustic signals generated by breathing sounds, collected via an electronic stethoscope, to provide real-time respiratory rate measurements. Airmod is specifically designed for use by healthcare professionals within hospital and healthcare facility settings, operating on Android-based devices. Following a comprehensive review, Airmod was deemed substantially equivalent to other legally marketed devices, supported by extensive non-clinical evaluations including software validation, cybersecurity assessments, and bench performance tests. Clinical trials involving 270 participants further demonstrated the device's non-inferiority in respiratory rate measurement when compared to a reference system, successfully meeting predefined performance goals. Based on these findings, it was concluded that Airmod does not introduce new safety or effectiveness concerns and is permissible for marketing, contingent upon adherence to applicable general controls, quality system requirements, and labeling standards.

AI Regulatory Concerns

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Jev reads the full FDA letter and returns calibrated concern probabilities per regulatory category. These are AI estimates, not regulatory advice.

• Q1. What specific performance metrics (e.g., accuracy, bias, precision) were achieved in the clinical trials, and how do they compare against the reference system under various patient conditions?; How robust is the AI-based algorithm to different types of ambient noise, patient movement artifacts, or variations in breathing patterns that might affect acoustic signal quality?; What are the specific cybersecurity protocols and data encryption standards implemented to ensure the integrity and privacy of patient data collected by Airmod on Android devices?; Are there any limitations or contraindications for Airmod's use in specific patient populations, or plans for expanding its indications beyond procedural sedation and anesthesia?; What kind of training is required for healthcare professionals to effectively use Airmod, and how does the device mitigate potential user errors or misinterpretations of the respiratory rate measurements?