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LiPPS™ Intravascular Pressure Sensing System is intended to be used in an environment like cathlab and for use in blood vessels, including coronary and peripheral vessels, to measure intravascular pressure during angiography and/or interventional procedures. Pressurements are obtained to provide hemodynamic information, such as fractional flow reserve, for the diagnosis and treatment of blood vessel diseases.

The subject device LiPPSTM Intravascular Pressure Sensing System contains H2000 LiPPS™ Analyzer and LiPPS™ Wire Pressure Guide Wire.

The LiPPS™ Wire Pressure Guide Wire is used together with the H2000 LiPPS™ Analyzer in order to measure intravascular pressure. The LiPPS™ Wire contains an optical sensor and an optical fiber to induce the blood pressure and transmit signal. The LiPPS™ Wire can be connected to the analyzer through the companied handle (with an optical cable and an optical connector). The LiPPS™ Analyzer is an electronic signal processing and display units which can process signals received from the LiPPSTM Wire to display intravascular blood pressure and fractional flow reserve (FFR) values, and various connection cables. LiPPS™ Wire and companied handle (with an optical cable and an optical connector) are sterile, single-use devices. The LiPPS™ Analyzer and its accessories are reusable.

The provided text is a 510(k) Summary for the LiPPS™ Intravascular Pressure Sensing System, which details the device's characteristics and its comparison to predicate devices to demonstrate substantial equivalence. This document does not describe acceptance criteria for an AI/ML-driven device or a study involving human readers and AI assistance. It focuses on the non-clinical performance and substantial equivalence of a physical medical device (an intravascular pressure sensing system) to existing predicate devices.

Therefore, many of the requested items regarding AI/ML device performance, MRMC studies, ground truth establishment, sample sizes for AI training/testing, and expert adjudication are not applicable to the information provided in this document.

However, I can extract information related to the device's non-clinical performance that is analogous to "acceptance criteria" and "device performance" for a medical device that measures physical parameters.

Based on the provided text, here's what can be extracted and what cannot:

LiPPS™ Intravascular Pressure Sensing System Acceptance Criteria and Performance (Non-Clinical)

1. Table of Acceptance Criteria and Reported Device Performance:

The document outlines performance characteristics of the LiPPS™ Intravascular Pressure Sensing System and compares them to predicate devices to demonstrate substantial equivalence. For a device like this, the "acceptance criteria" are implied by the specifications listed and the successful completion of standard-based testing.

2. Sample Size Used for the Test Set and the Data Provenance:

• Sample Size: Not explicitly stated as a numerical count of patients or cases. For a medical device like this, "sample size" typically refers to the number of devices or components subjected to specific engineering and biocompatibility tests. These are bench tests and in-vitro/in-vivo biological evaluations.
• Data Provenance: The studies are non-clinical (bench testing, in-vitro, and animal studies for biological evaluation). The document does not specify a country of origin for the data (beyond the manufacturer's location in Beijing, China), nor does it describe trials as retrospective or prospective (as these are not clinical trials).

3. Number of Experts Used to Establish the Ground Truth for the Test Set and the Qualifications of Those Experts:

• Not Applicable. This is a non-clinical submission for a physical medical device. "Ground truth" in the context of expert consensus is relevant for diagnostic AI/ML devices where human interpretation provides the reference standard. For this device, ground truth is established through standardized measurements and validated test methodologies.

4. Adjudication Method for the Test Set:

• Not Applicable. As there are no human readers or diagnostic interpretations involved, there's no need for an adjudication method. Performance is measured against predefined engineering specifications and regulatory standards.

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 document describes a physical medical device for intravascular pressure sensing, not an AI/ML diagnostic tool meant to assist human readers.

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

• Not Applicable. While the device has "embedded software," its primary function is direct physical measurement (pressure sensing), not image interpretation or diagnosis via an algorithm that could be evaluated in a standalone manner like an AI application. The software processes signals, displays data, and ensures device functionality, as opposed to making diagnostic inferences.

7. The Type of Ground Truth Used:

• For performance metrics (e.g., accuracy, pressure range, zero drift): Measured values against calibrated reference standards using laboratory equipment and controlled environments.
• For biological safety: Results from standard biological compatibility tests (e.g., cytotoxicity, hemolysis, pyrogenicity) as defined by ISO 10993 series and USP standards.
• For physical/mechanical properties: Measurements and observations against design specifications and ISO 11070 standard limits.

8. The Sample Size for the Training Set:

• Not Applicable. This document does not describe an AI/ML device that requires a "training set" in the machine learning sense. The "training" of the device refers to its design, engineering tolerances, and calibration, which are informed by scientific principles and material properties rather than iterative machine learning from a dataset.

9. How the Ground Truth for the Training Set was Established:

• Not Applicable. (See #8)

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