Authors: Moshe A Rancier, Igor Israel, Vimalson Monickam, John Prince, Ben Verschoore and Caroline Currie
Background: Clinically significant valvular heart disease (VHD) affects 1 in 10 adults over 65. Cardiac auscultation, the current point of care clinical standard, has shown a 44% sensitivity for detecting VHD, leaving most patients undiagnosed. A deep learning based artificial intelligence (AI) platform that detects cardiac murmurs associated with clinically significant VHD (moderate severity or higher, confirmed by echocardiogram) was used to assess its real-time impact on the point of care.
Methods: This blinded, prospective study enrolled 369 patients aged 50 and older without a prior VHD diagnosis or history of murmur from three different primary care clinics. Four-point cardiac auscultation was performed on each patient by (1) PCP with a standard stethoscope, and (2) coordinator with an Eko digital stethoscope to collect phonocardiogram (PCG) data for analysis by the AI. Each patient received an echocardiogram to confirm whether clinically significant VHD was present, and their PCG recordings were reviewed by an outside expert panel to confirm whether audible murmurs were present. PCP and expert panel were blinded to AI and echocardiogram results. Performance metrics were calculated for both PCP auscultation and the AI in detecting audible VHD.
Results: The AI showed over twofold improvement in sensitivity vs. PCP auscultation for detecting audible VHD (94.1% vs. 41.2%) with limited impact on specificity (84.5% vs. 95.5%). The AI identified 22 patients with moderate-or-greater VHD who were previously undiagnosed, while PCPs identified eight previously undiagnosed patients with VHD.
Conclusion: When applied in a primary care setting, a digital stethoscope with structural murmur detection AI showed meaningful impact on new discovery of VHD as compared to conventional practice. These results suggest that AI-powered auscultation at the point of care may significantly increase earlier VHD discovery, facilitate appropriate patient care, and improve outcomes.
MKT-0002786