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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, electrode, and attached extension cable for electrode connectivity.

The AcQRef Introducer Sheath consists of the following components:

• One (1) 7Fr Introducer Sheath with electrode and connection cable
• One (1) 7Fr Vessel Dilator
• One (1) Guidewire .038 in, J-tip.

The electrode is connected to the yellow cable and connector. The AcQRef Introducer Sheath is compatible for use with the AcQMap® High Resolution Imaging and Mapping System Models 900000, cleared under 510(k) K181577 and 900100, cleared under 510(k) K190131.

This document refers to an AcQRef Introducer Sheath, a medical device for facilitating venous access and sensing intravenous signals. The information provided is a 510(k) premarket notification summary to the FDA, which focuses on demonstrating substantial equivalence to a predicate device rather than presenting a detailed study proving the device meets specific acceptance criteria in the sense of a clinical trial or AI performance study.

Therefore, many of the requested categories for AI/clinical studies are not applicable or cannot be extracted from this type of document.

Here's an analysis based on the provided text, focusing on the available information:

1. A table of acceptance criteria and the reported device performance

This document does not provide a table of specific numerical acceptance criteria and reported device performance values for the new AcQRef Introducer Sheath. Instead, it states that "All necessary bench testing was conducted... to support a determination of substantial equivalence...Verification and validation studies demonstrated that the modified device met the pre-determined acceptance criteria. The results passed to support a determination of substantial equivalence."

The "Acceptance Criteria" here are implicitly linked to demonstrating substantial equivalence to the predicate device (K171557) across various features, as shown in the "Substantial Equivalence Table." The "Reported Device Performance" is summarized as having "met the pre-determined acceptance criteria" and "passed to support a determination of substantial equivalence," without specifying the exact quantitative results.

Here's a reinterpretation of "acceptance criteria" based on the provided "Substantial Equivalence Table":

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

This document details bench testing for a medical device (introducer sheath) aiming for 510(k) clearance via substantial equivalence to a predicate device. It is not an AI or clinical study with a "test set" of patient data.

• Sample Size for Test Set: Not applicable in the context of clinical/AI studies. The text mentions "bench testing" and "verification and validation studies," which would involve testing individual device units, but specific sample sizes for these tests are not provided.
• Data Provenance: Not applicable. The testing would be laboratory-based and device-specific.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

Not applicable. This is not a study involving expert-established ground truth on patient data. The "truth" or "standard" for the device's performance would be engineering specifications and predicate device characteristics.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

Not applicable. No adjudication method for a test set of patient data is described.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

Not applicable. This is not an AI-assisted diagnostic device, nor is it a multi-reader, multi-case study.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

Not applicable. This is a physical medical device, not an algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

Not applicable. The "ground truth" for this type of device submission is adherence to engineering specifications, safety standards, and functional equivalence to the legally marketed predicate device. This is typically established through bench testing (e.g., mechanical strength, material compatibility, electrical performance for the sensing function, sterility, etc.) rather than clinical endpoints or expert consensus on patient data.

8. The sample size for the training set

Not applicable. This is not an AI/machine learning device that requires a training set.

9. How the ground truth for the training set was established

Not applicable.

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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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