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The Anchor Bag-Alone® Tissue Retrieval System™ by CONMED is a sterile disposable pouch used for the encapture and removal of an organ or tissue from the body cavity during laparoscopic or minimally invasive surgery.

The Anchor Bag-Alone® Tissue Retrieval System™ by CONMED is a sterile, disposable retrieval pouch for the encapture and removal of an orqan or tissue from the body cavity during laparoscopic surgery. The Anchor Bag-Alone® Tissue Retrieval System"" by CONMED consists of a ripstop nylon pouch with polyurethane laminate and a braided drawstring that loops through the ripstop nylon pouch.

The provided text describes a 510(k) submission for a medical device called the "Anchor Bag-Alone Tissue Retrieval System by CONMED." This is a tissue retrieval system and not an AI/ML device, therefore the standard acceptance criteria, study design, and performance metrics typically associated with AI/ML devices (such as sensitivity, specificity, MRMC studies, training set data, etc.) are not applicable here.

Instead, the document focuses on demonstrating substantial equivalence to a previously cleared predicate device through non-clinical performance testing. The "acceptance criteria" in this context refer to the successful completion and outcomes of these engineering and performance tests, showing the device performs as intended and is safe and effective.

Here's a breakdown of the available information:

1. Table of Acceptance Criteria and Reported Device Performance

Since this is a non-AI/ML device, the "acceptance criteria" are not reported as specific performance metrics (like sensitivity, specificity, or AUC) but rather as successful completion of various engineering and performance tests ensuring the device's functionality, material properties, and safety. There isn't a direct table of these with specific pass/fail values in the document, but the text states:

Study Proving Device Meets Acceptance Criteria:

The study that proves the device meets the acceptance criteria is described as non-clinical bench and simulated use testing.

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

• Sample Size for Test Set: Not explicitly stated in terms of a "test set" as one would define it for an AI/ML device. The testing involved various quantities of the device to perform the bench and simulated use tests. The document does not specify the number of individual units tested for each specific test (e.g., how many bags were subjected to tensile strength testing).
• Data Provenance: Not applicable in the context of clinical data provenance. The testing was performed in a laboratory/engineering environment on the manufactured devices.

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

Not applicable. For a non-AI/ML device, ground truth is established through engineering specifications, validated test methods, and compliance with recognized standards. There were no human experts establishing a "ground truth" for a diagnostic or predictive task.

4. Adjudication method for the test set:

Not applicable. There was no need for adjudication as the tests performed were objective engineering and performance tests with predefined pass/fail criteria based on standards and comparison to the predicate device.

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/ML device, so MRMC studies involving human readers and AI assistance are irrelevant.

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

Not applicable. This is not an AI/ML algorithm.

7. The type of ground truth used:

The "ground truth" for this device's performance is based on:

• Engineering specifications and design requirements.
• Performance of the predicate device (K172940).
• Compliance with recognized international standards (e.g., ISO 11607-1:2006 for packaging, ISO 11135:2014 for sterilization, ISO 10993-1:2009 for biocompatibility, ISO 14971 for risk management).

8. The sample size for the training set:

Not applicable. This device does not involve machine learning and therefore does not have a "training set."

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

Not applicable. This device does not involve machine learning and therefore does not have a "training set" with ground truth to be established.

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