Search Results

The ENDOCAM Flex HD Controller has been designed for video endoscopy and can be used for both diagnostic and therapeutic interventions. The camera controller has an HDMI output suitable for connecting an image output device (monitor). This product is designed exclusively for use by specialized medical personnel and may only be applied by medically qualified and adequately trained persons.

The 1-chip HD Camera Head 5521902 is used for diagnostic operations in conjunction with ENDOCAM Flex HD Controller 5521101.

This product is designed exclusively for use by specialized medical personnel and may only be applied by medically qualified and adequately trained persons.

The Richard Wolf ENDOCAM® Flex HD Camera System 5521 is an endoscopic camera system for rigid, semi-rigid, and flexible endoscopes.

The camera system consists of:

• . ENDOCAM® Flex HD Controller (5521101)
• 1-Chip ENDOCAM® Flex HD Camera Head (5521902) ●

The ENDOCAM® Flex HD Camera System is used to capture and display still and video images of endoscopic or general surgical applications. Devices are used in conjunction with other ancillary equipment such as endoscopes, light sources, and monitors.

The ENDOCAM® Flex HD Camera System 5521 does not have a graphical user interface (GUI). All available functions are controlled via two buttons on the 1-Chip ENDOCAM® Flex HD Camera Head 5521902.

Devices included in the ENDOCAM® Flex HD Camera System 5521 are reusable and do not require sterilization before use because there is no direct / in-direct patient contact. Methods of cleaning. disinfection, and sterilization are detailed in the Instruction for Use Manual.

The provided document describes a 510(k) premarket notification for the ENDOCAM® Flex HD Camera System 5521, claiming substantial equivalence to predicate devices. As such, the study design focuses on demonstrating equivalence rather than proving the device meets specific acceptance criteria through a comparative clinical study with human readers or standalone AI performance.

Here's an analysis based on the provided text, addressing your questions:

1. Table of Acceptance Criteria and Reported Device Performance

The submission does not provide a table of acceptance criteria or specific performance metrics in the way one might expect for a diagnostic AI device (e.g., sensitivity, specificity, AUC). Instead, the acceptance criteria are implicitly met by demonstrating compliance with safety standards and functional equivalence to predicate devices. The "performance" reported is related to meeting these standards and functional specifications.

• Field of application: Same as predicates.
• Basic Design and Materials: Same/equivalent to predicates.
• Ancillary Equipment Use: Used in conjunction with other video equipment and endoscopic accessories (same as predicates).
• Reusable: Yes (same as predicates).
• Camera Head Features: 1-Chip non-autoclavable Camera Head (same as predicates).
• Bench Testing: Met all requirements and demonstrated substantial equivalence to predicate devices in bench comparison testing. Minor differences in technical specifications (e.g., TV standard, power consumption, weight, dimensions, sensor type, I/O sockets, GUI absence) do not raise new safety or effectiveness issues. |
  | Reprocessing Effectiveness | Cleaning, disinfection, and sterilization methods detailed in the Instruction for Use Manual were developed using standards outlined in ANSI/AAMI ST81: 2004/ (R) 2010 and FDA's Guidance "Reprocessing Medical Devices in Health Care Settings: Validation Methods and Labeling", issued on: March 17, 2015. (This indicates methods are established and presumably effective for reusability, but specific performance data like log reduction is not provided in this summary). |

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

The document explicitly states: "No comparison of clinical performance data was used for demonstration of substantial equivalence."

Therefore, there is no test set in the context of clinical performance evaluation (e.g., patient images or data used to evaluate diagnostic accuracy). The testing was non-clinical (bench testing) and focused on engineering specifications, safety, and functionality.

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

As there was no clinical test set, there were no experts used to establish ground truth in this context.

4. Adjudication Method for the Test Set

Not applicable, as there was no clinical test set requiring ground truth adjudication.

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

No, an MRMC comparative effectiveness study was not done. The submission explicitly states: "No comparison of clinical performance data was used for demonstration of substantial equivalence." This means there was no study comparing human readers with and without AI assistance, nor any clinical performance study.

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

This refers to a camera system, not an AI algorithm. Therefore, the concept of "standalone performance" in the context of an AI algorithm is not applicable. The device's performance was evaluated through non-clinical bench testing to ensure it met its engineering specifications and was functionally equivalent to predicate devices.

7. The Type of Ground Truth Used

For the non-clinical testing, the "ground truth" was established by engineering specifications, regulatory standards, and the functional characteristics of the predicate devices. The new device's performance was measured against these predefined technical benchmarks and the behavior of the previously approved devices.

8. The Sample Size for the Training Set

This refers to an image acquisition system, not an AI model that requires a training set. Therefore, the concept of a "training set" and its sample size is not applicable here.

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

Not applicable, as there is no training set for this device.

In summary, the provided documentation focuses on demonstrating substantial equivalence through non-clinical (bench) testing, compliance with recognized safety and usability standards, and comparison of technical specifications with legally marketed predicate devices. It does not involve clinical performance studies, AI algorithms, or the extensive data-driven validation typically associated with AI-powered medical devices.

Ask a specific question about this device