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The Medtronic® Cardioblate® Navigator™ Tissue Dissection Device is intended to dissect soft tissue during general, ENT, thoracic, urological, and gynecological surgical procedures. The device's battery-powered light source is used to navigate soft tissue for identification of the tip location around anatomic structures.

The Medtronic Navigator tissue dissection device is a single-use, hand-held surgical tissue dissector with an integral light source.

The provided document is a 510(k) summary for the Medtronic Cardioblate® Navigator™ Tissue Dissection Device. It describes the device's intended use and compares it to a predicate device to establish substantial equivalence. However, it does not contain information about acceptance criteria or a study proving the device meets those criteria in the format typically used for AI/ML-driven medical devices (e.g., performance metrics like sensitivity, specificity, or AUC).

The information provided relates to a traditional medical device (a tissue dissector with a light source), and the "study" mentioned is a verification and validation testing process to confirm functional characteristics are substantially equivalent to a predicate device.

Therefore, I cannot extract the specific information requested in the prompt because it is not present in the provided text.

Here's a breakdown of why the requested information cannot be provided based on the input:

• Acceptance Criteria & Reported Device Performance: This document states that "All test data obtained satisfied the documented product and performance specifications" and "Verification and validation testing confirms that functional characteristics are substantially equivalent to the predicate device cited." It does not list specific numerical acceptance criteria (e.g., minimum sensitivity of 90%) or reported performance values (e.g., sensitivity of 92%). The comparison is functional equivalency rather than statistical performance against specific metrics.
• Sample Size for Test Set and Data Provenance: No information about a "test set" in the context of imaging data or AI/ML evaluations is present. The validation testing likely involved bench testing and potentially animal or cadaver studies, but details on sample sizes or data provenance are not provided.
• Number of Experts & Qualifications for Ground Truth: No experts were used to establish ground truth in the context of interpreting images or data, as this is a physical surgical tool.
• Adjudication Method: Not applicable.
• Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study: This is not an AI/ML device, so an MRMC study comparing human readers with and without AI assistance is not applicable.
• Standalone Performance: The device is a physical tool used by a surgeon; standalone algorithm performance is not relevant.
• Type of Ground Truth Used: The "ground truth" for this device would be its physical and functional performance (e.g., light intensity, articulation, clip strength), not pathology or outcomes data in the context of a diagnostic AI.
• Sample Size for Training Set: Not applicable, as this is not an AI/ML device requiring a training set.
• How Ground Truth for Training Set was Established: Not applicable.

In summary, the provided text describes a 510(k) for a physical medical device. The requested information format is tailored for AI/ML-driven diagnostic or assistive devices, which is not what this document pertains to.

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