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Erbe's CO2 Tubing/Cap Sets provide sterile water and CO2 or air (if CO2 is not used) to an endoscope for endoscopic procedures.

In general; Erbe's CO2 Tubing/Cap Sets will be manufactured with medical grade materials or agents used in the medical device industry such as plastics, silicone, nitrile rubber, acrylic, nylon, ink, solvent, adhesive, etc. The ERBEFLO CleverCap® CO2 devices provide a conduit for water for endoscopic irrigation and lens cleaning as well as air or CO2 for insufflation; whereas, the ERBEFLO AeroRinse® CO2 devices provide a conduit for water for endoscopic lens cleaning as well as air or CO2 for insufflation. There are three (3) types of Erbe's CO2 Tubing/Cap Sets for each group (i.e., ERBEFLO CleverCap® CO2and ERBEFLO AeroRinse® CO-) which each respectively interfaces with a specified brand of scope (i.e., Pentax, Olymous, and Fujinon Gastrointestinal video Endoscopes). There are two subsets of Sets for the Olympus scope in aech group; one that attaches to standard CO2 sources and the other which specifically attaches to an Olympus CO2 Unit Model UCR. The Sets consist of multiple tubing segments and a cap. The cap of a Set attaches with an air tight seal to a water source (i.e., a sterile water bottle). Then from the water bottle cap, irrigation tubing of a Set (as applicable- only for the ERBEFLO CleverCap® CO2 Sets) interfaces with a designated pump and via ERBEFLO 2 single use connector accessories to the specified endoscope for endoscopic lavage. The next segment, the air/water tubing (also coming from the same water bottle cap), connects to an air/water port of a specified scope for air insufflation as well as lens cleaning [Note: The air/water tubing is a tube within a tube in which the endoscope's processor or CO2 Source (if used) is used to pressurize the bottle for functionality (air and water to the endoscope). Also, for the Pentax Set there is an additional air inlet tube that directs air for endoscope functional use. Or if CO2 is used, pressurization (air and water function) occurs via a CQ2 Source through the Set's CO2 segment.]. The third and final segment for both the Olympus and Fujinon Sets is for connecting to a CO2 Source for CO2 insufflation. For each Set, both the irrigation (as applicable) as well as the air/water tubing segments has a backflow valve. The CO2 segment of the Sets have a standard female luer connector for access a CO₂ Source or in the case of specified Olympus Sets there is a connector on the CO2 segment designed to accessing the Olympus CO3 Unit Model UCR. All of the CO2 segments have a hydrophobic air/gas filter which filters particulates from the CO, Source and keeps fluid from flowing into the CO2 Source. The irrigation (as applicable), air/water, and CO2 segments of the Sets have a clamp to close off the tubing while not in use. Additionally, each Set has an air/water connector(s) for its specified endoscope. Erbe's CO2 Tubing/Cap Sets are provided sterile and are disposable.

This document is a 510(k) premarket notification for a medical device (Erbe's CO2 Tubing/Cap Sets), and as such, it does not describe a study involving an AI/Machine Learning device or a diagnostic accuracy study with expert readers and ground truth establishment. Instead, it demonstrates substantial equivalence to a predicate device through a series of engineering tests and evaluations.

Therefore, many of the requested elements for describing the acceptance criteria and the study that proves an AI device meets them (e.g., sample size for test set, data provenance, number of experts, MRMC studies, standalone performance, ground truth types for AI models, training set details) are not applicable to this type of regulatory submission.

The document discusses performance testing for a physical medical device (tubing and cap sets) rather than an AI algorithm.

However, I can extract information related to the device's performance evaluation and acceptance criteria as presented in this 510(k) summary, interpreting "acceptance criteria" as meeting performance specifications for substantial equivalence.

Here's a breakdown of the device performance evaluation based on the provided document, addressing the relevant points from your request:

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

The document doesn't provide a quantitative table of specific acceptance criteria values and measured performance results in the way one would for an AI model's accuracy. Instead, it lists the types of tests performed and states that the proposed products "met established performance specifications" or "demonstrated safety and efficacy."

• Package integrity testing
• Visual inspection
• Flow testing
• Back flow pressure testing
• Pressure decay testing
• Tensile strength testing
• Durability testing (ensuring no leaks and connectability upon stressing Sets)
• Clamp testing |
  | Packaging Evaluation | Demonstrated adequacy and integrity of the packaging. |
  | Sterilization Evaluation | Demonstrated product sterility and met ethylene oxide residual requirements per recognized standards. |

2. Sample size used for the test set and the data provenance

• Sample Size: Not specified in the document. The document refers to "evaluations and tests" but does not give the number of units tested for each evaluation.
• Data Provenance: Not applicable in terms of country of origin or retrospective/prospective for this type of device testing. These are engineering and sterilization validation tests performed on manufactured units.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• Not Applicable. This document is for a physical medical device (tubing/cap sets), not an AI/ML diagnostic device that requires expert-established ground truth. Performance is evaluated against engineering specifications and recognized standards.

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

• Not Applicable. See point 3.

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 diagnostic device.

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

• Not Applicable. This is not an AI diagnostic device.

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

• Not Applicable. The "ground truth" for this device's performance is adherence to established engineering specifications, material biocompatibility standards, and sterility assurance levels, as verified through various physical and chemical tests (e.g., pressure measurements, flow rates, sterility tests, material compatibility tests).

8. The sample size for the training set

• Not Applicable. There is no "training set" as this is not an AI/ML device.

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

• Not Applicable. There is no "training set" or its associated ground truth.

Summary of the Study (Device Performance Evaluation):

The "study" described in the 510(k) summary is a series of engineering and biological evaluations performed on the Erbe's CO2 Tubing/Cap Sets to demonstrate their safety and efficacy and establish substantial equivalence to a legally marketed predicate device (ERBEFLO CleverCap* Hybrid CO₂ Tubing/Cap Sets and Connector Tube, K132340).

The evaluations focused on:

• Biocompatibility: Ensuring materials used are safe for patient contact.
• Sterilization Validation: Proving the device can be consistently sterilized and that residuals are within limits.
• Packaging Integrity: Verifying the packaging maintains sterility and protects the device.
• Functional Performance: Testing various physical attributes like flow, pressure, tensile strength, durability, and leak prevention to ensure the device performs its intended function (providing sterile water, CO2/air to an endoscope) reliably.

The approach taken is purely for a traditional-hardware medical device and does not involve AI or machine learning components, human reader studies, or diagnostic performance metrics common in AI/ML submissions.

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