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The Flexible Ureterorenoscope is intended to be used to visualize organs, cavities and canals in the urinary tract (urethra, bladder, ureter, calyces and renal papillae) via transurethral access routes. It can also be used in conjunction with endoscopic accessories to perform various diagnostic and therapeutic procedures in the urinary tract.

The Flexible Ureterorenoscope (Model: US31E-12-EU; US31E-12-US) is intended to be used with the Video Processor (cleared in K211169). The Flexible Ureterorenoscope is inserted through the natural orifice urethra and when used with the compatible Video Processor and monitor, the endoscope system can be operated as intended and indicated. The Flexible Ureterorenoscope is a single-use endoscope, which consists of a Handle, an Insertion Section, and an Endoscope Connector. The handle includes a deflection lever, a working channel port for accessory devices and a Luer port for irrigation. The insertion section contains one working channel and wiring to transmit the image signals to the Video Processor. The distal bending section of the insertion is controlled by the user via the deflection lever on the handle. The distal end of the insertion contains a CMOS sensor for capturing image and transmitting it to the Video Processor, LEDs for illumination, and the distal opening of the working channel. The endoscope connects the endoscope handle to the video processor, which provides power and processes video signals from the endoscope. Same as the predicate, the subject device is also provided in 2 deflection versions (US/EU deflection), which is the only difference between the two proposed models.

The provided text describes a 510(k) premarket notification for a medical device called "Flexible Ureterorenoscope." This document is a regulatory submission to the FDA proving substantial equivalence to a predicate device, not a study of an AI/ML algorithm. Therefore, many of the requested details, such as AI-specific acceptance criteria, test set Ground Truth establishment, MRMC studies, or training set information, are not applicable to this kind of device and its regulatory pathway.

The device discussed is a standard medical instrument, an endoscope, not an AI-powered diagnostic or therapeutic device. The "study" mentioned refers to non-clinical performance data and testing to demonstrate that the modified device performs comparably to its predicate and meets safety standards, not a clinical trial involving AI.

Here's an attempt to answer the questions based on the provided text, highlighting where the requested AI-specific information is not applicable:

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

The document states that the device passed all testing in accordance with internal requirements and applicable standards. The acceptance criteria are generally implied to be the successful demonstration that the device's characteristics (mechanical, optical, material, etc.) are equivalent to or better than the predicate device and meet relevant regulatory standards. Specific numerical acceptance criteria were not explicitly provided in the summary, but rather implied by the statement "passed all the testing in accordance with internal requirements and applicable standards to support substantial equivalence of the subject device."

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

The text describes non-clinical performance testing of a physical device. It does not refer to a "test set" in the context of a dataset for an algorithm. There is no information regarding sample sizes for human subjects or origin of data (e.g., retrospective/prospective, country of origin) because clinical studies were not required for this submission. The testing was performed on physical samples of the device.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

This question is not applicable. "Ground truth" in the context of expert review for medical image analysis or similar AI applications is not relevant here. The ground truth for device performance is based on established engineering standards, material science, and biocompatibility protocols.

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

This question is not applicable. Adjudication methods like 2+1 or 3+1 refer to agreement among human readers for establishing ground truth in clinical data, which is not what was done for this 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

No. An MRMC study was not stated or implied. This type of study would be relevant for an AI-assisted diagnostic or decision-support system, which this device is not. The document explicitly states: "The subject of this premarket submission, did not require clinical studies to support substantial equivalence."

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

No. This question is not applicable. The device is a physical endoscope that is used by a human operator; it is not a standalone algorithm.

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

The "ground truth" for demonstrating substantial equivalence for this device is based on:

• Compliance with recognized international standards (e.g., ISO, ASTM).
• Demonstration of physical and optical characteristics being equivalent or better than the predicate device.
• Successful completion of specific mechanical, biocompatibility, sterilization, and packaging tests based on established laboratory protocols.

No diagnostic or clinical outcomes ground truth was established by experts for this submission.

8. The sample size for the training set

This question is not applicable. There is no "training set" as this is a physical medical device, not an AI/ML algorithm requiring training data.

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

This question is not applicable for the same reason as above.

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