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The nephroscope, with its accessories, is used, with suction, for the disintegration and removal / extraction of kidney and bladder stones. The stones are removed, under endoscopic control, through percutaneous or transurethal passages, in conjuction with intercorporeal pneumatic, ultrasound, electrohydraulic or laser lithotriptors.

For diagnosis and therapy with endoscopic accessories in the upper and lower urogenital tract when performed by trained and qualified medical personnel.

The universal nephroscope has a reduced sheath diameter and an expanded working channel to accommodate auxiliary instruments and irrigation. The submitted nephroscope design has been modified to optimize user handling.

The submitted nehproscope can be held by the elongated right angled light connected which has been designed with a recessed grip and a thumb ring. An automatic valve for instrument insertion and an automatic locking mechanism between the endoscope and sheath is part of the submitted design. Accessory instruments have been adapted to the longer nephroscope.

Here's an analysis of the provided text regarding the acceptance criteria and supporting study for the K994223 Nephroscope Set:

Based on the provided K994223 510(k) summary, the device is a conventional medical instrument, not a software algorithm or AI-driven diagnostic device. Therefore, many of the typical performance study elements requested for AI/ML devices (like sample sizes for training/test sets, ground truth establishment, MRMC studies, standalone performance, etc.) are not applicable to this submission.

The 510(k) process for traditional devices primarily focuses on demonstrating substantial equivalence to legally marketed predicate devices, meaning it has the same intended use, technological characteristics, and raises no new safety or effectiveness questions.

Here's a breakdown of the requested information based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

Note: For traditional medical devices like this nephroscope, "acceptance criteria" and "device performance" are typically demonstrated through design verification and validation (often in-house testing) and adherence to recognized standards, rather than clinical performance metrics like sensitivity, specificity, or AUC as would be seen for AI/ML diagnostic tools. The document explicitly states "Clinical tests were not performed."

2. Sample size used for the test set and the data provenance

Not applicable. This device is a surgical instrument. There is no "test set" in the context of data for an AI/ML algorithm. Performance is demonstrated through in-house lab testing, design verification, and adherence to standards, not human data sets. The document explicitly states "Clinical tests were not performed" and "No known performance standards" in the context of human data.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

Not applicable. There is no "ground truth" establishment in the context of human data for this type of device submission. The safety and effectiveness are established through engineering design, material selection, manufacturing controls, and bench testing to demonstrate functionality and meet design specifications.

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

Not applicable. There is no "test set" or human data requiring adjudication for this device type.

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

Not applicable. This device is not an AI/ML diagnostic tool and does not involve "human readers" or AI assistance.

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

Not applicable. This device is a manual surgical instrument, not an algorithm.

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

Not applicable. For this type of medical device, "ground truth" is defined by engineering specifications, material properties, and functional performance benchmarks demonstrated through non-clinical testing (e.g., mechanical strength, fluid flow rates, optical clarity, compatibility with accessories).

8. The sample size for the training set

Not applicable. There is no "training set" as this is not an AI/ML algorithm.

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

Not applicable. There is no "training set" or "ground truth" in the AI/ML sense for this device.

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