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The SSU Flexible Video-Uretero-Renoscope System is indicated for endoscopic examination in the urinary tract and can be used to examine the interior of the kidney, and using additional accessories, to perform various diagnostic and therapeutic procedures.
E-Box: the product serves as an adaptor for operating the flexible single-use videoscope on the compatible CCU.

The videoscopes in the modified Flexible Video-Uretero-Renoscope SSU System are sterile single-use, flexible video-endoscopes. The distal tip houses the CMOS (Complementary Metal Oxide Semiconductor) imaging sensor and the LED light source. The raw data captured at the distal tip CMOS imaging censor is transferred to the E-Box adaptor, where it is converted to a standard NTSC (National Television System Committee) video signal by the PCB (Printed Circuit Board), which is then driven into one of the CCUs (C-MAC, C-HUB II, or X-LINK + IMAGE 1S Connect) for further processing and video formatting for output to a display monitor. The videoscopes and E-Box are powered by the CCUs through the connecting cords.

Here's a summary of the acceptance criteria and study information based on the provided text, focusing on the performance of the SSU Flexible Video-Uretero-Renoscope System:

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

The document does not explicitly state acceptance criteria in numerical terms for the performance characteristics. Instead, it compares the subject device's performance characteristics to those of a predicate device (KARL STORZ Flexible Video-Uretero-Renoscope System K141250) to demonstrate substantial equivalence. The "Acceptance Criteria" column below will reflect the predicate device's corresponding specifications where applicable, or indicate "Met design specifications" if specific targets for the subject device are not detailed but performance testing was done.

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

The document states, "Clinical testing was not required to demonstrate the substantial equivalence to the predicate devices. Non-clinical bench testing was sufficient to establish the substantial equivalence of the modifications." This indicates that no clinical test set (i.e., human patient data) was used.

The performance was evaluated through non-clinical bench testing. The sample size for these bench tests is not explicitly stated in the provided text, nor is the provenance of the data from these bench tests (e.g., if specific components were sourced from different countries or if testing was done in a specific lab location). The submitter, KARL STORZ SE & Co. KG, is located in Tuttlingen, Germany.

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)

Since no clinical testing was performed, no experts were used to establish ground truth for a clinical test set. The performance was evaluated against design specifications and recognized consensus standards by the technical personnel conducting the bench testing.

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

As no clinical test set was used, no adjudication method was applicable or performed.

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 MRMC comparative effectiveness study was done. The device itself is an endoscope system, not an AI-assisted diagnostic tool.

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

Not applicable. This device is an endoscope system that requires human operation and interpretation. It is not an algorithm performing a standalone task.

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

For the non-clinical bench testing, the "ground truth" implicitly used would be engineering and physical measurements taken against established specifications and recognized consensus standards. For example, "on-axis resolution" would be measured using standardized targets and optical equipment, with the expected values derived from known optical principles and predicate device data.

8. The sample size for the training set

Not applicable. This device is a hardware medical device (an endoscope system), not a machine learning or AI algorithm that requires a "training set" in the conventional sense. Its design and performance are based on engineering principles and verified through bench testing.

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

Not applicable. As there is no training set for an AI algorithm.

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