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

The LithoVue Elite Digital Flexible Ureteroscope System (cleared under K221515 on 02-FEB-2023 and K233645 on 12-DEC-2023) consists of the StoneSmart Connect Console, LithoVue Elite Single-Use Digital Flexible Ureteroscope with pressure monitoring, and LithoVue Elite Single-Use Digital Flexible Ureteroscope (without pressure monitoring). The proposed devices within the scope of this Special 510(k) premarket notification are the LithoVue Elite Single-Use Digital Flexible Ureteroscope with pressure monitoring and LithoVue Elite Single-Use Digital Flexible Ureteroscope (without pressure monitoring).

The proposed ureteroscope with pressure monitoring contains a redesigned distal tip to improve its durability during a ureteroscopy procedure. To maintain congruence between the two marketed configurations of the ureteroscope, the distal tip of the ureteroscope (without pressure monitoring) is also modified to match the one used in the ureteroscope with pressure monitoring, with the exception that it does not contain the specific modifications intended for pressure monitoring. Furthermore, test methods are also modified to be more representative of "challenging" working conditions as well as for better alignment with the intent of the specifications.

This document is a 510(k) summary for the LithoVue Elite Single-Use Digital Flexible Ureteroscope. It details the device, its intended use, and a comparison to predicate devices, focusing on the substantial equivalence argument for regulatory clearance. Since this is a submission for the clearance of a device (a ureteroscope), and not for an AI/ML algorithm or a diagnostic test, the typical acceptance criteria and study designs that would apply to software are not present. The document focuses on the physical and functional aspects of the hardware.

Therefore, many of the questions regarding AI/ML-specific details, such as data provenance, expert adjudication, MRMC studies, standalone algorithm performance, and training/test set ground truth, are not applicable to this document. The "device" in question is a physical endoscope.

However, I can extract the relevant performance testing information provided:

1. Table of acceptance criteria and the reported device performance:

The document implicitly defines acceptance criteria by stating that the proposed devices have the "Same" or "Identical" performance to the predicate devices across various characteristics. The specific numerical criteria mentioned are for "Pressure Measurement Accuracy" which applies to the ureteroscope with pressure monitoring.

Description of the Study:

The study performed was a "subset of the mechanical performance and durability testing (including pressure sensor durability testing) along with the 'Image Rotation Offset' testing." The purpose was to evaluate the "design changes" to the ureteroscopes, specifically a redesigned distal tip to improve durability. The document asserts that the proposed devices maintained the same performance characteristics as the predicate devices.

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 does not explicitly state the sample size for the mechanical performance and durability testing. It indicates that testing was performed on the "proposed ureteroscopes" to evaluate the design changes. The data provenance is not specified, but given it's for a medical device cleared in the US, it would generally be expected to be internal laboratory testing. It is a prospective evaluation of the new design.

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 information is not applicable and therefore not provided. The testing relates to the physical and functional performance of the device, not the interpretation of medical images or diagnoses.

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

This information is not applicable. Adjudication methods are typically used in clinical or image-based studies where expert consensus is needed. The testing described is physical performance testing against engineering specifications.

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:

This information is not applicable. The device is a ureteroscope (hardware), not an AI-powered diagnostic tool for human readers.

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

This information is not applicable. The device is a physical ureteroscope, not a standalone AI algorithm. It's a tool used by a human clinician.

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

The "ground truth" for this device would be its adherence to established engineering specifications and performance benchmarks for mechanical durability, optical clarity, and pressure measurement accuracy. This is based on objective measurements rather than expert consensus or pathology in a clinical sense.

8. The sample size for the training set:

This information is not applicable. The device is hardware; there is no AI/ML training set.

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

This information is not applicable. There is no AI/ML training set.

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