The ACUSON Sequoia, Sequoia Select, and Origin are advanced multi-purpose diagnostic ultrasound systems by Siemens, designed for a wide range of clinical applications including fetal, abdominal, cardiac, vascular, and musculoskeletal imaging. They incorporate AI-based software applications (AI Measure, AI Assist, 2D HeartAI, 4D HeartAI) to assist clinicians in cardiac imaging by automating measurements, image annotation, and analysis, thereby improving workflow and diagnostic confidence.
The ACUSON Sequoia and Sequoia Select ultrasound imaging systems provide images or signals inside the body for fetal, abdominal, pediatric, neonatal cephalic, small parts, OB/GYN, cardiac, pelvic, vascular, adult cephalic, musculoskeletal and peripheral vascular applications. The system supports the Ultrasonically-Derived Fat Fraction (UDFF) measurement tool for hepatic steatosis management and provides measurement and calculation packages for diverse clinical indications. The ACUSON Origin system provides images or signals inside the body for abdominal, pediatric, OB/GYN, cardiac, transesophageal, intracardiac, vascular, adult cephalic, and peripheral vascular applications, including intracardiac and intra-luminal great vessel anatomy visualization during cardiac interventions.
The ultrasound systems are multi-purpose, software-controlled, with multiple imaging modes including 2D, 3D/4D volume imaging, Doppler modes, elastography, and harmonic imaging. They include new transducers and proprietary software features with AI algorithms (AI Measure, AI Assist, 2D HeartAI, 4D HeartAI) for automated cardiac image analysis and measurement. The systems reuse software and hardware technology previously cleared with additional options and improved image quality.
Performance testing includes clinical validation of AI algorithms on diverse datasets from multiple institutions, showing high accuracy and success rates (over 89% success for AI Measure, over 92% for AI Assist placement, and strong correlation coefficients for HeartAI modules). Testing included patient images across varying demographics and BMI categories, with independence maintained between training and testing datasets. Extensive safety and conformity testing to medical device standards were also performed.
No predicate devices specified
Submission
7/19/2023
FDA Approval
10/30/2023
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