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The GripTract-GI Endoscopic Tissue Manipulator (GripTract) Lower GI Models are accessories intended to assist in positioning the distal end of an endoscope from the mucosal surface and assist with optical visualization, diagnosis, and endoscopic treatment.

GripTract Lower GI Models are indicated for use in the large intestine with any standard endoscope as follows:

The GripTract-GI™ Endoscopic Tissue Manipulator (GripTract) Lower GI Models are disposable, non-sterile accessories intended to assist in positioning the distal end of an endoscope from the mucosal surface and assist with optical visualization, diagnosis, and endoscopic treatment. There are four (4) different GripTract Lower GI Models (GT-CL170, GT-CL130, GT-CM170, and GT-CM130) which are indicated for use in the large intestine with standard endoscopes that have specific tip outer diameters and working lengths.

The GripTract Handpiece attaches to the endoscope control body just below the working channel. The soft End Cap with two integrated Fingers for tissue manipulation is placed on the distal end of the endoscope. Two Control Knobs in the Handpiece each operate a corresponding Finger, permitting the User to rotate and extend/retract the Fingers for tissue manipulation and visualization independent of the endoscope's movement or the presence of other tools in the endoscope's working channel.

Here's a breakdown of the acceptance criteria and study information based on the provided document, addressing each of your points:

Important Note: The provided document is a 510(k) summary for a medical device (GripTract-GI Endoscopic Tissue Manipulator Lower GI Models). This type of document focuses on demonstrating substantial equivalence to an existing predicate device rather than proving clinical efficacy or diagnostic accuracy for an AI/software device. Therefore, many of your questions, particularly those related to AI-specific studies (MRMC, standalone AI performance, ground truth establishment for training AI, etc.), are not applicable to this device and information. The device described is not an AI/software device.

The document primarily details non-clinical performance data to support the substantial equivalence claim.

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't present a formal table of acceptance criteria with numerical performance targets against which the device was measured like typical AI/diagnostic studies. Instead, it lists various non-clinical tests performed and states whether the device "passed" or exhibited "equivalent force transmission" or "exceeded reported maximum pull forces."

Here's a summary derived from the "Summary of non-clinical performance testing" table and the subsequent paragraphs:

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

As this is a physical medical device (endoscopic tissue manipulator) and not an AI/software, the concept of a "test set" in the context of diagnostic data doesn't apply directly.

• Sample Size: The document does not specify particular "sample sizes" for the non-clinical tests in terms of number of devices tested. It refers to "worst-case simulated use," "repeated worst-case torsional load," and testing "at the dimensional extremes of the product line" for Torque Comparison. For Biocompatibility, it states "Final, finished devices tested."
• Data Provenance: Not applicable in the context of retrospective/prospective patient data or country of origin for such data. The data provenance is from bench performance testing conducted by the manufacturer, Actuated Medical, Inc.

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

Not applicable. "Ground truth" in the context of expert consensus on medical images or diagnostic outcomes is not relevant for this device's non-clinical performance testing. The "ground truth" for these tests would be the measured physical properties and performance characteristics against established engineering specifications and regulations.

4. Adjudication Method for the Test Set

Not applicable. Adjudication methods (e.g., 2+1, 3+1) are used for resolving discrepancies among expert readers in diagnostic AI studies. This is a non-clinical, bench testing document.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size of Human Readers Improvement with AI vs. Without AI Assistance

Not applicable. This device is a physical medical instrument, not an AI or software that assists human readers in diagnostic interpretation. An MRMC study would not be performed for this type of device.

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

Not applicable. This is not an algorithm or software. It is a physical device operated by a clinician.

7. The Type of Ground Truth Used

For the non-clinical performance tests, the "ground truth" is defined by:

• Engineering specifications and design requirements.
• Industry standards (e.g., ISO 10993-1 for biocompatibility).
• Quantitative measurements derived from bench testing (e.g., force measurements, dimensional checks, fatigue cycles).
• Simulated worst-case scenarios and interactions.

8. The Sample Size for the Training Set

Not applicable. This is not an AI/machine learning device, so there is no training set of data in that sense.

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

Not applicable. As there is no AI training set, there's no ground truth establishment process for it.

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