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The Neptune 2 Waste Management System is intended to be used in the operating room, pathology, surgical centers, and doctor's offices to collect and dispose of surgical fluid waste as well as collect smoke generated from electrocautery or laser devices.

The Neptune 2 Waste Management System is a fluid waste management system with smoke evacuation. It collects surgical waste fluid within a closed suction system, then disposes of it through a docking station. The system is comprised of a rover and a docker (also referred to as a docking station).

The Neptune 2 Rover (Model Number: 0702-001-000) is a mobile, AC-powered surgical fluid waste collection device. It is intended to collect waste fluids and evacuate smoke during surgical procedures.

The Neptune 2 Docking Station (Model Number: 0702-014-000) is a stationary device for automated rover cleaning and waste offloading. It provides a fixed connection to the hospital's water supply, sewer, and electricity.

The provided text describes the Stryker® Neptune 2 Waste Management System, a fluid waste management system with smoke evacuation, submitted for 510(k) clearance. The submission focuses on demonstrating substantial equivalence to predicate devices rather than proving a new device meets specific acceptance criteria through a comparative study with a ground truth established by experts.

Therefore, many of the requested elements (e.g., specific acceptance criteria, sample sizes for test/training sets, number of experts for ground truth, adjudication methods, MRMC studies, standalone performance, type of ground truth) are not explicitly detailed in this 510(k) summary. The summary primarily relies on bench testing and comparison to predicate devices to establish safety and effectiveness.

Here's an analysis based on the available information:

1. Table of Acceptance Criteria and Reported Device Performance

The submission does not present a formal table of explicit, quantitative acceptance criteria that the device was tested against and then reports its performance against those criteria in a "study" as one might expect for a new diagnostic or AI-driven insights device. Instead, the "Testing" section states:

"The Neptune 2 Waste Management System meets the specification and performance characteristics as identified in Stryker's internal design control procedures. Bench testing was performed to demonstrate the performance of the device. Testing was completed to verify and validate the device as outlined in the device description. Testing included electrically powered suction safety, electrical safety, vacuum cycling, software, smoke evacuation and filtration. Human factors analysis and usability testing was performed on the user interface, labeling, and training materials."

The "Table 1: Device Comparison Matrix" provides comparative specifications against predicate devices, implying that performance within a similar range or meeting similar functional attributes to the predicates would be considered acceptable. Below is a table summarizing key performance characteristics (rather than explicit acceptance criteria with pass/fail thresholds) of the Neptune 2 and its predicates, as presented in the document.

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

• Sample Size for Test Set: The document explicitly states "Bench testing was performed...Testing was completed to verify and validate the device." However, specific sample sizes (e.g., number of test articles, number of test runs) for each component of the bench testing (electrical safety, vacuum cycling, smoke evacuation, filtration, software, human factors) are not detailed.
• Data Provenance: The testing was conducted internally by Stryker. No information is provided regarding the country of origin of the data or whether it was retrospective or prospective, as this is a device for fluid and smoke management, not an AI or diagnostic device that typically uses patient data.

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

This information is not applicable and not provided in the 510(k) summary. This type of detail is typically associated with studies for diagnostic devices or AI algorithms where expert consensus establishes a "ground truth" against which device performance is measured (e.g., presence or absence of a disease). For a waste management system, the "ground truth" is typically defined by engineering specifications and functional testing (e.g., vacuum pressure achieved, fluid volume collected, smoke particles filtered).

4. Adjudication Method for the Test Set

This is not applicable and not provided in the 510(k) summary for the same reasons as #3.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done

No, an MRMC comparative effectiveness study was not done. MRMC studies are typically used to assess the performance of diagnostic devices or AI algorithms when interpreted by multiple human readers, often comparing performance with and without AI assistance. This device is a surgical waste management system and does not involve human interpretation of cases.

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

No, a standalone algorithm-only study was not done because this is a physical medical device. It does not employ an "algorithm" in the sense of a software component making diagnostic or analytical decisions. Its performance is related to its mechanical and electrical functions.

7. The Type of Ground Truth Used

The "ground truth" for this device would be defined by engineering specifications, regulatory standards (e.g., electrical safety, biocompatibility), and functional performance targets (e.g., flow rates, vacuum levels, filtration efficiency, fluid capacity). The device was tested to verify it met these internal specifications and regulatory requirements. The biocompatibility testing specifically referenced ISO-10993 standards.

8. The Sample Size for the Training Set

This is not applicable and not provided. "Training set" refers to data used to train machine learning algorithms. The Neptune 2 is a physical medical device, not an AI/ML algorithm.

9. How the Ground Truth for the Training Set Was Established

This is not applicable as there is no training set for an AI/ML algorithm for this device.

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