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The enlightenVue Microendoscopy System is intended for visualization of body cavities, hollow organs, and canals. The surgiVue microendoscope is designed to be introduced through natural body cavities or surgical incisions through introducers, trocars, needles, sheaths, or other devices with lumens having inside diameters larger than the outer diameter of the microendoscope.

The enlightenVue Microendoscopy System is comprised of the surgiVue microendoscope and surgiTrac laser light source. This system is used to provide imaging capability of internal anatomy for both diagnostic and interventional procedures. The small laser light source focuses light onto a small fiber in the form of white light. The surgiTrac interfaces with the disposable surgiVue microendoscope and a PC to allow for illumination of the captured image.

The provided document is a 510(k) summary for the enlightenVue Microendoscopy System. It focuses on demonstrating substantial equivalence to a predicate device rather than outlining detailed acceptance criteria for an AI/ML-driven medical device and a study proving it meets those criteria.

Therefore, the document does not contain the information requested in the prompt regarding acceptance criteria for an AI/ML device and a study proving device performance against those criteria. The provided text is for a traditional medical device (endoscopy system) and relies on functional, mechanical, and biocompatibility testing for substantial equivalence, not AI/ML performance metrics.

Here's a breakdown of why the requested information cannot be extracted from the provided text:

• No AI/ML Component: The enlightenVue Microendoscopy System is described as a system for visualization using a microendoscope, light source, and camera. There is no mention of artificial intelligence, machine learning, image processing algorithms for diagnostic aid, or similar features that would necessitate AI-specific acceptance criteria.
• Performance Metrics are for Device Functionality, Not AI Accuracy: The "Performance Testing" section (page 6-7) lists tests like "Accelerated Aging," "Real-Time Aging," "Cytotoxicity Test," "Dimensional Verification," "Electrical Characteristics," and "Functional Verification: surgiVue Microendoscope." These are standard tests for endoscopes to ensure their physical and functional integrity, not for evaluating the performance of an AI algorithm (e.g., sensitivity, specificity, AUC).
• No "Ground Truth" for AI/ML: Since there's no AI component, there's no need for establishing ground truth from expert consensus, pathology, or outcomes data, which are crucial for validating AI models.
• No Test Set/Training Set Sizes: Similarly, the concepts of training and test sets are absent because there's no AI model being developed or validated.
• No Multi-Reader Multi-Case (MRMC) Study: MRMC studies are used to assess how AI impacts human reader performance, which isn't applicable here.
• No Standalone Performance: Standalone performance refers to the AI algorithm's performance without human intervention, which is not relevant to this device.

In summary, the provided document describes information relevant to a traditional medical device K-clearance, not an AI/ML-powered device. Therefore, a table of acceptance criteria for an AI/ML device and details of a study proving its performance against those criteria cannot be extracted.

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