Philips Data Shows AI Echo Tool Outperforms 2D Method for Mitral Regurgitation

Philips released late-breaking preliminary data at the European Society of Cardiology (ESC) showing that its AI-powered 3D Auto CFQ (Color Flow Quantification) tech measured mitral regurgitation with accuracy more closely matching cardiac MRI than conventional, 2D proximal isovelocity surface area (2D PISA) methods.

The company said the findings suggest the ultrasound-based assessment can offer a more physiological approach to evaluate mitral regurgitation. 3D Auto CFQ uses 3D transesophageal echocardiography to dynamically track the regurgitant convergence zone throughout systole. By accounting for changes in the regurgitant orifice and multiple jets, the tech hopes to offer a more comprehensive measure of regurgitant volume.

In its ongoing prospective, multicenter international study, investigators compared mitral regurgitation volume measurements from Philips 3D Auto CFQ and conventional 2D PISA with CMR imaging, the established reference method. Preliminary findings indicated that 3D Auto CFQ produces lower regurgitant volumes and better agreement with CMR than conventional methods, which do not account for changes in the regurgitant orifice during systole (bias: +7 mL vs. +14 mL; regurgitant volume ρ=0.77).

The study also assessed, for the first time, mitral regurgitation severity reclassification using conventional and 3D Auto CFQ methods.

“Accurate quantification of mitral regurgitation is essential for clinical decision making, yet conventional approaches have well-recognized limitations,” said Victoria Delgado, MD, PhD, Hospital University Germans Trias i Pujol, Badalona, Spain. “By providing measurements that more closely match cardiac magnetic resonance than conventional 2D PISA-based methods, these findings suggest AI-powered 3D Auto CFQ may help clinicians more consistently quantify mitral regurgitation across a wider variety of disease presentation.”

Earlier this week, Philips announced that its COMBINE-CT project has moved from technology development to clinical validation of its CT-based integrated care pathway for coronary artery disease (CAD) patients.