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The PREVENA DUO Incision Management System is intended to manage the environment of surgical incisions that continue to drain following sutured or stapled closure by maintaining a closed environment and removing exudate va the application of negative pressure wound therapy.

The PREVENA PLUS DUO Incision Management System is intended to manage the environment of surgical incisions that continue to drain following sutured or stapled closure by maintaining a closed environment and removing exudate via the application of negative pressure wound therapy.

Both the PREVENA DUO™ Incision Management System (with the PREVENA 125 Therapy Unit) and the PREVENA PLUS DUO™ Incision Management System (with the PREVENA PLUS 125 Therapy Unit) are negative pressure wound therapy systems designed for the management of two surgical incisions simultaneously with one PREVENA Therapy Unit. Both systems provide two PREVENA PEEL & PLACE Dressings (which can be for linear incisions up to 13 or 20 cm in length) which are connected to their respective therapy units via a new PREVENA Y-Connector. Therapy is continuously applied to each incision at -125 mmHg for 2 to 7 days. Length of therapy is per surgeon preference.

This document is a 510(k) summary for medical devices, specifically the PREVENA DUO and PREVENA PLUS DUO Incision Management Systems. It details the device, its intended use, and argues for its substantial equivalence to previously cleared devices. However, this document does not contain acceptance criteria for device performance, nor does it describe a study proving the device meets such criteria in the way typically expected for an AI/algorithm-based medical device.

The devices in question are mechanical negative pressure wound therapy systems, not AI algorithms. Therefore, the questions posed, which are highly relevant to the evaluation of AI/ML-based medical devices, are not directly addressed in this regulatory submission.

Here's a breakdown based on the provided document and the non-applicability of the questions:

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

• Not Applicable. This document pertains to a mechanical device, not an AI/ML algorithm. There are no "acceptance criteria" in the sense of performance metrics (e.g., sensitivity, specificity, AUC) for an AI model.
• The document mentions "Negative pressure performance testing indicates that the PREVENA DUO and PREVENA PLUS DUO Incision Management Systems are able to provide negative pressure within specification to each dressing over a 7 day use test and under test conditions of maximum air leak and simulated wound fluid input." This describes engineering performance testing, not clinical or AI performance.

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective):

• Not Applicable. No computational "test set" in the context of AI/ML is mentioned or used. The performance testing described is engineering/bench testing of the physical device.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g., radiologist with 10 years of experience):

• Not Applicable. No ground truth establishment for an AI model is relevant to this device.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

• Not Applicable. No test set adjudication is relevant.

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:

• Not Applicable. This is a mechanical device, not an AI system. No human reader interaction or improvement is relevant.

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

• Not Applicable. There is no standalone algorithm.

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

• Not Applicable. No ground truth for an AI model is used. The "performance data" refers to validation that the device physically generates negative pressure as designed and functions correctly.

8. The sample size for the training set:

• Not Applicable. No training set for an AI model is mentioned or used.

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

• Not Applicable. No training set or associated ground truth establishment is relevant to this device.

In summary: The provided document is a 510(k) submission for a non-AI medical device. The questions are tailored for AI/ML-based devices and thus do not apply to the information presented in this document. The "study" mentioned is "Negative pressure performance testing," which is an engineering validation to ensure the device maintains specified negative pressure and alerts correctly.

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