Corvair Monza

Corvair Monza is a software-as-a-medical-device (SaMD) that analyzes 10-second resting ECGs to provide automated rhythm, morphological, and interval determinations for healthcare professionals. The device uses an API library that can be integrated into target devices to assist in measuring and interpreting diagnostic ECGs, with all outputs requiring physician review before clinical use.

Corvair Monza uses signal processing and machine learning algorithms, specifically deep neural networks trained on approximately 1 million 12-lead ECGs from ~400,000 clinical patients at Emory University Hospital (1985–2010). It requires only 4 ECG leads (either Leads I, II, V2, V4 or I, II, V1, V4) and operates in two modes: Symptomatic Mode (for high pre-test probability) and Asymptomatic Mode (optimized for sensitivity/specificity). The device has identical technological characteristics to its predicate, using the same machine learning models with new determinations implemented via clinically accepted criteria applied to existing interval and axis measurements.

The device requires compatible signal input from devices compliant to IEC 60601-2-25 using gel/wet electrodes. No clinical testing was conducted; nonclinical performance testing evaluated standard ECG metrics (sensitivity, specificity, PPV, mean error, standard deviation of error, and mean absolute error) against a known reference and the predicate device.

Corvair Monza is substantially equivalent to predicate K231010 (Corvair) because both have identical intended uses (measuring and interpreting resting diagnostic ECGs for rhythm and morphological information), identical indications for use, and identical technological characteristics using the same machine learning models. Nonclinical testing demonstrated that Corvair Monza's performance is equivalent to the predicate across all evaluated ECG analysis metrics.