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FISSO Holding System consists of a table-mounted endoscope holder system intended for use by surgeons to hold endoscopes and arthroscopes with a diameter of 4mm to 10mm during general diagnostic and therapeutic procedures. The device is also intended for use by qualified surgeons for holding endoscopes during diagnostic and therapeutic neurologic procedures

Table-mounted self-retaining endoscope holder system consisting of stainless steel tubular, articulated arms that are connected to a vertical stand and are freely adjustable within the articulating radius according to the requirements of the particular surgical procedure. All joints are locked in position simultaneously via the adjustment knob located on the central joint. Accessories include endoscope holders for 4mm to 10mm scopes as well as multiple class-l holding devices. The device is reusable and provided non-sterile. It must be cleaned and sterilized before use.

The provided 510(k) summary for the FISSO Holding System describes a medical device, specifically an endoscope holder. The information outlines its intended use and comparison to predicate devices, but it does not include information about acceptance criteria or a study proving that the device meets specific performance criteria in the way that an AI/ML device would.

This device is not an AI/ML diagnostic or therapeutic system. It is a physical medical device (an endoscope holder). Therefore, the typical "acceptance criteria" and "study" questions relevant to AI/ML performance metrics (like sensitivity, specificity, AUC) and ground truth establishment are not applicable in this context.

Here's a breakdown of why many of your requested items cannot be answered from the provided text, and what information is available:

1. Table of Acceptance Criteria and Reported Device Performance:

• Not Applicable in the AI/ML sense. The document states: "Design analysis and comparison as well as verification testing confirm that basic functional characteristics are substantially equivalent to the predicate devices cited and raise no new issues of safety and effectiveness."
• Implied Acceptance Criteria (for a physical device): The device is expected to:
  • Hold endoscopes and arthroscopes with a diameter of 4mm to 10mm securely.
  • Be table-mounted and self-retaining.
  • Allow for free adjustment within its articulating radius.
  • Have all joints lock simultaneously via a central knob.
  • Be reusable and sterilizable.
  • Be substantially equivalent in basic features and intended uses to the predicate devices.
• Reported Device Performance: The document confirms "verification testing" was done, and the conclusion is that the device is "substantially equivalent" to predicate devices, implying it met the functional requirements for holding endoscopes as intended. No specific quantitative performance metrics (e.g., maximum load, stability under specific forces, duration of hold) are provided in this summary.

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

• Not Applicable. There is no "test set" in the context of an AI/ML algorithm evaluating data. The "testing" referred to is likely mechanical or functional testing of the physical device. The document does not specify a sample size for such tests or geographical origin, as it's a hardware device.

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

• Not Applicable. Ground truth, in the sense of expert annotation of data, is not relevant for this physical device. The "ground truth" for a physical device is its ability to perform its mechanical function.

4. Adjudication Method for the Test Set:

• Not Applicable. There is no "test set" requiring adjudication.

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

• No, not in the AI/ML sense. An MRMC study is relevant for evaluating human performance with and without an AI aid. This is a physical device, not an AI.

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

• Not Applicable. This is not an algorithm.

7. The Type of Ground Truth Used:

• For a physical device, "ground truth" would relate to its engineering specifications and functional performance. The ground truth would be whether it holds the endoscope securely, locks properly, withstands sterilization, etc., as determined by engineering tests and comparisons to predicate devices. This isn't pathology, outcomes data, or expert consensus on diagnostic images.

8. The Sample Size for the Training Set:

• Not Applicable. This is not an AI/ML device requiring a training set.

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

• Not Applicable. This is not an AI/ML device requiring a training set.

In summary, the provided document describes a traditional medical device (an endoscope holder) and its 510(k) summary for substantial equivalence review. It does not provide the kind of information requested for an AI/ML medical device submission. The "performance data" section indicates that "design analysis and comparison as well as verification testing" were performed to confirm equivalence to predicate devices, but no specific details of those tests or their results, or any explicitly stated acceptance criteria (beyond meeting the functional requirements of an endoscope holder), are given in this summary.

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