Artificial intelligence based fusion imaging streamlining mitral transcatheter edge-to-edge repair

Area of Science:

• Cardiovascular Interventions
• Medical Imaging Technology
• Artificial Intelligence in Medicine

Background:

• Mitral transcatheter edge-to-edge repair (M-TEER) demands precise imaging for procedural success.
• Conventional fluoroscopy and echocardiography can lead to communication issues and suboptimal device deployment.
• Accurate visualization is crucial for navigating complex cardiac anatomy and device placement.

Purpose of the Study:

• To evaluate the clinical utility of DeviceGuide EchoNavigator SmartVue, an AI-based fusion imaging software.
• To assess the software's ability to track the PASCAL Ace device and align 3D transoesophageal echocardiography with fluoroscopy.
• To determine the impact of AI-enhanced imaging on procedural workflow and team communication during M-TEER.

Main Methods:

• Prospective multi-centre study involving 51 M-TEER procedures with the PASCAL Ace device across four international sites.
• Utilized dedicated imaging modes: target mode for navigation and device mode for continuous visualization.
• Collected qualitative feedback from interventional teams via structured questionnaires on workflow, image quality, and software usefulness.

Main Results:

• Clinical teams rated the AI software as helpful or very helpful in guiding M-TEER in most cases.
• Reported improved team discussions regarding optimal procedural strategy and enhanced awareness of device trajectory.
• Perceived advantages included real-time feedback, automated 3D transoesophageal echocardiography (TEE) views, and continuous, auto-aligned device imaging.

Conclusions:

• AI-based fusion imaging demonstrated high perceived utility in M-TEER procedures using the PASCAL Ace device.
• The software appeared to streamline workflow and enhance interdisciplinary team communication.
• Further studies are warranted to confirm the impact of this technology on clinical outcomes.