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The SPY Cystoscopes/Hysteroscopes are intended to provide visualization in general urological and gynecological surgery through the minimally invasive approach, by utilizing natural orifices to access the surgical site.

The SPY Cystoscope/Hysteroscope is part of Stryker's rigid endoscope product portfolio. The SPY Cystoscope/Hysteroscope is an optical instrument used to visualize or image a patient's anatomy during minimally invasive, endoscopic procedures for examination, diagnosis or therapy. The SPY Cystoscope/Hysteroscope transmits light in the visible spectrum to illuminate the anatomy, then forms and relays the image of the surgical site to a camera system for image processing and display.

The provided text describes a 510(k) premarket notification for the Stryker Endoscopy SPY Cystoscope/Hysteroscope. It focuses on demonstrating substantial equivalence to a predicate device, rather than a standalone study proving specific clinical performance metrics with a test set, ground truth, or human reader involvement for an AI/ML device.

Therefore, the requested information regarding "acceptance criteria and the study that proves the device meets the acceptance criteria" in the context of an AI/ML device with details like sample sizes, expert ground truth, adjudication methods, MRMC studies, or standalone algorithm performance, is not applicable to this document.

This document details:

• Device Type: A traditional medical device (optical instrument for visualization in surgery), not explicitly an AI/ML powered device.
• Regulatory Pathway: 510(k) Pre-market Notification, which focuses on demonstrating substantial equivalence to an existing legally marketed predicate device.
• Performance Testing: Primarily non-clinical bench testing and compliance with recognized voluntary consensus standards.

Here's a breakdown of what is provided, framed as closely as possible to your request, but highlighting the absence of AI/ML specific criteria:

1. Table of Acceptance Criteria and Reported Device Performance

The document lists various performance tests and their "Pass" results, indicating that the device met the criteria set by the respective standards or comparative testing. The acceptance criteria themselves are implicitly defined by compliance with these standards (e.g., "In accordance with FDA-recognized voluntary consensus standard IEC 60601-1:2020").

2. Sample size used for the test set and the data provenance: Not applicable. This document describes non-clinical testing of a physical medical device, not a performance study on a test set of data (e.g., images for an AI algorithm).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for an AI/ML algorithm is not relevant to the described testing.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable.

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. The device is a direct visualization tool, not an AI-assisted diagnostic or interpretative system for human readers.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Not applicable.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable. The "ground truth" here is compliance with established engineering and safety standards, and equivalence to a predicate device's performance.

8. The sample size for the training set: Not applicable. The device does not involve a training set as it's not an AI/ML product.

9. How the ground truth for the training set was established: Not applicable.

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The use of the SDC4K Information Management System with Device and Voice Control Package is to allow for voice control and remote control of medical device settings by surgeons or operating room personnel, thereby eliminating the need to manually operate those devices compatible with the SDC4K Information Management System with Device and Voice Control Package or to rely on verbal communication between the surgeon and other operating room personnel in order to adjust the surgical equipment. It also has additional digital documentation functionality capture, transfer, store and display medical device data (non-medical device function), which is independent of the functions or parameters of any attached Stryker device.

The SDC4K Information Management System with Device and Voice Control Package is a network compatible hardware platform that carries out Medical Device Data System (MDDS) functionalities and allows the user to control the state, selection, and settings of compatible connected endoscopic and general surgery devices both wired and wirelessly.

The SDC4K Information Management System with Device and Voice Control Package consists of the following components:

• 1. SDC4K Console which includes:
  • a) Class I Medical Device Data System (MDDS) functionality
  • b) Optional Device Control feature
  • c) Optional Voice Control feature
• 2. Device Control Package (software activation USB dongle and a handheld Infrared (IR) remote control)
• 3. Voice Control Package (software activation USB dongle and a wireless headset and base station)
• 4. Connected OR Spoke (Class I MDDS)

The SDC4K console carries out the Medical Device Data System (MDDS) functionalities (i.e. Class I device function or Non-medical function) and can be marketed as a standalone device. When upgraded with the Device Control and/or Voice Control package, the SDC4K Console extends its functionality to control compatible devices from its touchscreen graphical user interface (GUI), spoken commands via headset (voice control input), and an IR remote control or directional keypad from a camera head (device control input). The received user commands are then processed and communicated with the connected controllable devices, allowing the user to control the state, selection, and settings of those devices. In addition, the SDC4K Information Management System with Device and Voice Control Package also provides compatibility with the Connected OR Spoke (also referred to as "Spoke") which is a standalone Class I Medical Device Data System. Once the SDC4K is connected to the Spoke, Device Control can be extended to compatible devices connected to the Spoke.

The provided FDA 510(k) summary for the SDC4K Information Management System with Device and Voice Control Package outlines its performance data through various tests. However, it does not include a detailed table of acceptance criteria and reported device performance for specific functional metrics, nor does it describe a study design that would prove the device meets such criteria in terms of accuracy or clinical effectiveness.

Instead, the document focuses on demonstrating substantial equivalence to a predicate device (Connected OR Hub with Device and Voice Control, K212055) by verifying compliance with recognized standards.

Here's an attempt to answer your questions based only on the provided text, highlighting what is included and what is explicitly not included:

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

The document provides a table of tests conducted and their outcomes, indicating "Pass" for each. These are related to safety, EMC, software validation, usability, and bench performance based on specifications and intended use. Specific quantitative performance metrics (e.g., accuracy, latency, success rate for voice control commands) against defined acceptance criteria are not provided.

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 specify sample sizes for any test sets mentioned (e.g., for usability or bench performance). It also does not mention data provenance, as the tests are primarily engineering and compliance-based rather than involving patient data or clinical studies. The document explicitly states: "the subject device does not require clinical studies to support the determination of substantial equivalence."

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

This information is not provided in the document. The tests described are largely against technical standards and internal specifications, rather than requiring expert consensus on a "ground truth" derived from clinical data.

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

Adjudication methods are not described, as the type of studies conducted (compliance and engineering tests) typically do not involve such processes.

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

A multi-reader multi-case (MRMC) comparative effectiveness study was not mentioned and is not applicable to this device, as it is an information management and control system, not an AI-assisted diagnostic or interpretative tool. The document states "the subject device does not require clinical studies to support the determination of substantial equivalence."

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

The device itself (SDC4K Information Management System with Device and Voice Control Package) includes human interaction (remote control, voice control, GUI). Therefore, a "standalone algorithm only" performance without human-in-the-loop is not directly relevant or described. The performance bench tests would assess the functionality of the system components and their integration, but not in a purely algorithmic, non-interactive context.

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

The concept of "ground truth" as typically understood in the context of diagnostic or AI performance evaluation (e.g., against pathology reports or clinical outcomes) is not applicable to the type of testing described. The "ground truth" in this context would be the successful execution of device commands, adherence to electrical safety, EMC, software, and usability standards, and meeting internal device input specifications.

8. The sample size for the training set

This information is not applicable and not provided. The device is an information management and control system, not a machine learning or AI algorithm that relies on a "training set" for its core function (beyond potentially voice recognition models, which are likely integrated commercial solutions and their training data is not discussed here).

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

This information is not applicable and not provided for the reasons stated in point 8.

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