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The 1688 Video Camera is indicated for use in general laparoscopy, ear endoscopy, ear endoscopy, sinuscopy, neurosurgery and plastic surgery whenever a laparoscope/ arthroscope/ sinuscope/ sinuscope is indicated for use. The 1688 Video Camera is indicated for adults and pediatric patients.

A few examples of the more common endoscope surgeries are Laparoscopic cholecystectomy, Laparoscopic hernia repari, Laparoscopic appendectomy, Laparoscopic pelvic lymph node detection, Laparoscopically assisted hysterectomy, Laparoscopic and thorascopic anterior spinal fusion, Anterior cruciate ligament reconstruction, Knee arthroscopy, Small joint arthroscopy, Decompression fixation, Wedge resection, Lung biopsy, Dorsal sympathectomy, Pleurodesis, Internal mammary artery dissection for coronary artery bypass grafting where endoscopic visualization is indicated and Examination of the evacuated cardiac chamber during performance of valve replacement.

The 1688 4K Camera System with Advanced Imaging Modality is an endoscopic camera system that produces live video in the surgical field during surgical endoscopic procedures. The system is sensitive in the visible and infrared spectrum. The optical image is transferred from the surgical site to the camera head by a variety of rigid and flexible endoscopes, which are attached to the camera head. The 1688 4K Camera System consists of three main components: (1) a camera control unit (CCU); (2) a camera head with an integral cable that connects to the CCU; and (3) a coupler for attaching an endoscope to the camera head.

The provided text is a 510(k) Summary for the Stryker 1688 4K Camera System with Advanced Imaging Modality. This document primarily focuses on establishing substantial equivalence to a predicate device, as required for FDA clearance. It does not contain the kind of detailed performance study information typically associated with AI/ML device evaluations, such as specific acceptance criteria for algorithm performance, sample sizes for test sets, expert ground truth establishment, or MRMC studies.

Therefore, many of the requested fields cannot be filled from the provided text.

Here's a breakdown of what can and cannot be extracted:

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

The document doesn't provide acceptance criteria and reported performance in the typical sense of algorithm-specific metrics (e.g., sensitivity, specificity, AUC). Instead, it lists performance testing for general device functionality, electrical safety, and electromagnetic compatibility.

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

• Sample Size for Test Set: Not specified for any AI/ML performance evaluation. The "Performance Data" section refers to engineering tests, not a clinical or image-based test set.
• Data Provenance: Not applicable, as no data set for AI/ML evaluation is described.

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

• Not applicable, as no ground truth establishment for AI/ML performance evaluation is described.

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:

• No MRMC study is mentioned. The device is an endoscopic camera system, and the document focuses on its technical specifications and safety. There is no indication of AI assistance that would involve human readers.

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

• This filing is for an endoscopic camera system. While it has an "Advanced Imaging Modality" (AIM), the document does not describe it as an AI/ML algorithm requiring standalone performance evaluation in the context of clinical decision-making. The AIM likely refers to specific imaging capabilities (e.g., near-infrared fluorescence, transillumination) rather than an interpretative AI algorithm.

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

• Not applicable.

8. The sample size for the training set:

• Not applicable, as no AI/ML training is described.

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

• Not applicable.

Summary of what is present:

The document describes the Stryker 1688 4K Camera System with Advanced Imaging Modality (AIM). It states that the device is "substantially equivalent in design, intended use, principles of operation, technological characteristics and safety features to the predicate devices." (K211202).

The performance data listed pertains to general device safety and functionality tests:

• Environmental RF Interference: Pass
• Electrical Safety (according to standards IEC 60601-1, IEC 60601-1-6, IEC 60601-2-18): Pass
• Electromagnetic Compatibility (according to standard IEC 60601-1-2): Pass

The "Advanced Imaging Modality" in this context refers to capabilities like "Near-infrared fluorescence" and "Near-infrared transillumination," which are imaging techniques, not necessarily an AI/ML algorithm that predicts or diagnoses. The 510(k) focuses on the camera system itself as an endoscope and accessories, not an AI diagnostic tool.

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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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