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The Acutus Medical AcQRef Introducer Sheath is indicated for use in percutaneous procedures to facilitate venous access from the lower extremities for introduction of catheters and other devices, and may be used to sense intravenous signals.

The Acutus Medical AcQRef Introducer Sheath is a sterile, single use, venous access device that consists of a straight shaft with lumen, hemostasis valve, flush port, electrodes, and attached extension cable for electrode connectivity. The AcQRef Introducer Sheath consists of the following components:

• One (1) 7Fr Introducer Sheath with electrodes and connection cable
• One (1) 7Fr Vessel Dilator
• One (1) Guide wire - 0.038 in, J-tip
  The AcQRef Introducer Sheath is compatible for use with the AcQMap® High Resolution Imaging and Mapping System.

It appears you've provided documentation for a 510(k) premarket notification for a medical device: the AcuQRef Introducer Sheath. This type of submission focuses on demonstrating substantial equivalence to a previously cleared predicate device, rather than proving a device meets specific "acceptance criteria" through a comprehensive study in the way an AI/ML device would.

The provided text does not contain the information requested in your bullet points regarding AI/ML device performance, ground truth establishment, expert adjudication, or MRMC studies. This is because the AcuQRef Introducer Sheath is a physical medical device (an introducer sheath with electrodes), not an AI/ML diagnostic or prognostic tool.

Therefore, I cannot fulfill your request to "describe the acceptance criteria and the study that proves the device meets the acceptance criteria" in the context of AI/ML, as the provided document pertains to a different type of medical device submission.

Here's why the provided text doesn't fit your request, and why it's structured differently:

• Device Type: The AcuQRef Introducer Sheath is a physical, invasive medical device used for vascular access and sensing electrical signals. It's not a software algorithm that analyzes data to make a diagnostic or prognostic inference.
• Regulatory Pathway: The 510(k) pathway is for devices that are "substantially equivalent" to an existing, legally marketed device (a predicate). This means demonstrating it's as safe and effective as the predicate, not necessarily meeting entirely new, performance-based acceptance criteria in the AI/ML sense.
• "Acceptance Criteria" in 510(k): For this device, "acceptance criteria" are typically met through:
  • Bench Testing: Mechanical, electrical, material, and performance tests (e.g., integrity, leak tests, pull strength, electrical safety, corrosion resistance). The document lists many such tests (Packaging and Shelf-Life, Sterilization, Biocompatibility, Surface, Dimensional, and Electrical Integrity, Valve Air Leak Test, Simulated Use Test, Pressure Leak Test, Flexion and Fatigue, Electrical Safety, Corrosion Resistance, Pull Strength, Design validation testing in an animal model).
  • Comparison to Predicate: Demonstrating that its technological characteristics, intended use, and indications for use are "similar" to the predicate, and any differences don't raise new questions of safety or effectiveness.
• Lack of AI/ML Specifics: There are no mentions of:
  • Sensitivity, specificity, AUC as performance metrics.
  • Test sets, training sets in the context of data for an algorithm.
  • Experts establishing ground truth for image/data interpretation.
  • Adjudication methods for disagreements among experts.
  • MRMC studies to evaluate human readers with/without AI assistance.
  • Standalone algorithm performance.

In summary: The provided document demonstrates substantial equivalence for a physical medical device through comparison to a predicate and non-clinical bench testing. It does not contain information relevant to AI/ML device acceptance criteria or performance studies.

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