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The D Camera Controller 5522101 has been designed for high-definition video endoscopy and can be used for both, diagnostic and therapeutic interventions. The Camera Controller is used in conjunction with other video equipment and endoscopic accessories.

The D Camera Controller 5522101 has been designed for high-definition video endoscopy and can be used for both, diagnostic and therapeutic interventions. The Camera Controller is used in conjunction with other video equipment and endoscopic accessories. The subject device is used in combination with sensor endoscopes and a monitor for visualization. In this context, the D Camera Controller is the control unit, its primary performance characteristics are signal processing of the image data and image recording. No further light source is required for the intervention. The D Camera Controller supplies the distal LED of the endoscope with light. The electrical signals provided by the sensor endoscope are processed and adequately converted to render a visible image of the endoscopic scene on a connected display device for the user.

This document is a 510(k) Summary for the Richard Wolf D Camera Controller (K211332), filed with the FDA. It declares substantial equivalence to a predicate device, the LOGIC HD LITE CAMERA CONTROLLER (K200617). The core argument for equivalence primarily rests on non-clinical performance testing and software verification.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

Important Note: This device, the "D Camera Controller," is a hardware component (a camera controller) for endoscopy, not an AI or imaging diagnostic device in the traditional sense. Therefore, many of the typical acceptance criteria and study types associated with AI/ML diagnostic tools (like those involving image classification, human reader performance, ground truth establishment by expert consensus, etc.) are not applicable or described in this document. The focus here is on the functional safety and effectiveness of the device as a video endoscopy component.

1. Table of Acceptance Criteria and Reported Device Performance

Since this is not an AI/ML diagnostic device, the acceptance criteria are largely related to functional performance, electrical safety, electromagnetic compatibility, and software validation. The document describes that the device met these criteria rather than providing a quantitative table of thresholds vs. observed performance in a diagnostic context.

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size: The document does not specify a "test set" sample size in terms of clinical cases or patient data. The testing described is primarily bench testing (performance, electrical safety, EMC) and software testing, not a clinical study involving a dataset of medical images or patient outcomes.
• Data Provenance: Not applicable in the context of clinical image data. The testing is described as internal verification and validation of the device's engineering performance.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

• Not applicable. The "ground truth" for this device is its adherence to engineering specifications, electrical safety standards, electromagnetic compatibility, and software functionality, as verified through standard testing procedures. It is not an AI diagnostic device where expert interpretation of clinical data constitutes "ground truth."

4. Adjudication Method for the Test Set

• Not applicable. As the performance testing is against technical specifications and standards, there is no need for expert adjudication of medical findings.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and Effect Size

• No. An MRMC study is relevant for AI image analysis tools where human reader performance (with and without AI assistance) is being evaluated. This device is a camera controller, not such an AI tool.

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

• Not applicable. This device is a hardware component with integrated software for image acquisition and processing. Its "standalone" performance is its ability to meet specified technical and functional requirements, which are detailed implicitly through the various compliance tests mentioned (EMC, electrical safety, software V&V, functional tests). It's not a standalone diagnostic algorithm that produces a finding independent of human interaction.

7. The Type of Ground Truth Used

• The "ground truth" for this device's performance is:
  • Engineering Specifications: The device must meet its design requirements for signal processing, image recording, user interface functions, etc.
  • Regulatory Standards: Compliance with electrical safety (e.g., EN/IEC 60601-1), electromagnetic compatibility (e.g., IEC 60601-1-2), and software development/validation standards (e.g., IEC 62304).
  • Predicate Device Equivalence: The ultimate "ground truth" sought by the 510(k) pathway is demonstrating that the device is "substantially equivalent" to a legally marketed predicate device in terms of safety and effectiveness. This is achieved by showing that any differences do not raise new questions of safety or effectiveness.

8. The Sample Size for the Training Set

• Not applicable. This device does not use machine learning or AI models that require a "training set" of data in the common sense. Its software performs fixed algorithms for image processing and control, not adaptively learning from data.

9. How the Ground Truth for the Training Set Was Established

• Not applicable. (See point 8).

In Summary:

The provided document describes the regulatory submission for a medical device (a camera controller for endoscopy) that is primarily a hardware component with integrated software. The "acceptance criteria" and "study" proving it meets them are focused on engineering and regulatory compliance (e.g., electrical safety, EMC, software validation) rather than the clinical performance metrics often associated with AI/ML diagnostic tools (like sensitivity, specificity, or human reader study endpoints). The substantial equivalence claim is built on non-clinical performance testing and software verification, demonstrating that any technological differences from the predicate device do not raise new questions of safety or effectiveness. No clinical or image-based diagnostic performance studies are mentioned or required for this type of device.

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