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

Negative pressure wound therapy system

The provided document is a 510(k) premarket notification for a medical device (Prevena Incision Management System). It does not describe a study involving an AI/Machine Learning device or outline particular acceptance criteria usually associated with such devices (e.g., sensitivity, specificity, AUC). Instead, it discusses the substantial equivalence of the device to a predicate device, focusing on functional and safety aspects for a medical device.

Therefore, I cannot extract the acceptance criteria, study details, sample sizes, expert qualifications, adjudication methods, MRMC study results, standalone performance, or ground truth information relevant to an AI/ML device from the provided text.

The document primarily focuses on:

• Device Description and Indications for Use: The Prevena Incision Management System manages surgical incisions by maintaining a closed environment and removing exudate via negative pressure wound therapy.
• Comparison to Predicate Device: The current device is substantially equivalent to a previously cleared Prevena Incision Management System (K121883). The core technology (negative pressure wound therapy) and primary components are the same.
• Technological Differences: Minor changes were made to the software to reduce "nuisance alerts" related to the Leak Alert and Low Battery Alert mute times and a slight increase in the air leak threshold.
• Non-Clinical Testing: Bench tests were conducted to confirm the therapy unit delivers negative pressure within specifications, software verification and validation, and battery life testing.
• Clinical Testing: No clinical tests were deemed necessary for substantial equivalence. However, usability testing was conducted with patient users to assess the impact of labeling changes due to software modifications, and all usability goals were met.

In summary, this document is for a traditional medical device demonstrating substantial equivalence, not an AI/ML device.

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The Prevena Incision Management System is intended to manage the environment of surgical incisions that continue to drain following sutured or stapled closed environment and removing exudate yia the application of negative pressure wound therapy.

The Prevena Incision Management System provides surgical incision management via the application of negative pressure wound therapy over an incision site that has been surgically closed with sutures or staples. The Prevena System is applied to the incision site immediately after surgery for a minimum of 2 days up to a maximum of 7 days depending on the surgeon's preference. The Prevena Incision Management System consists of: A Prevena Dressing (Prevena Peel & Place Dressing or Prevena Customizable Dressing and A source of negative pressure wound therapy, which may be one of the following KCI therapy units with its associated canisters: Prevena 125 Therapy Unit ActiV.A.C. Therapy Unit V.A.C. Freedom Therapy Unit InfoV.A.C. Therapy Unit

The provided text describes the Prevena Incision Management System and its substantial equivalence to predicate devices. It presents a "Prevena Dressing Pressure Manifold Test" as the primary performance data, but the description of this test does not align with the typical structure of acceptance criteria and reported performance for an AI/ML device, nor does it provide the detailed information requested in the prompt.

Based on the provided information, it is not possible to fully answer the request as it pertains to an AI/ML device. The document is a 510(k) summary for a medical device that uses negative pressure wound therapy, not an AI/ML-driven diagnostic or prognostic tool. Therefore, many of the requested fields, such as "sample size used for the test set," "number of experts used to establish ground truth," "adjudication method," "MRMC study," "standalone performance," "training set size," and "how ground truth for training set was established" are not applicable in this context.

However, I can extract the information that is present concerning the device's performance testing for the non-AI device.

1. Table of acceptance criteria and the reported device performance

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

• Test set sample size: Not specified. The test involved a "simulated wound bed."
• Data provenance: Not specified, but likely laboratory testing. This was a non-clinical test.

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)

• Number of experts: Not applicable. This was a physical performance test of a mechanical device.
• Qualifications of experts: Not applicable.

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

• Not applicable. This was a physical performance test, not one requiring expert human judgment or adjudication.

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 not an AI device. The document mentions usability testing with clinicians and patients for instructional materials, but this is not an MRMC study comparing AI performance.

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

• Not applicable. This is not an AI algorithm. The pressure manifold test is inherently a "standalone" test of the device's physical performance, but not in the context of AI.

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

• The ground truth was based on the physical measurement of negative pressure distribution on a simulated wound bed. This is a functional performance metric, not a clinical "ground truth" in the diagnostic sense.

8. The sample size for the training set

• Not applicable. This is not an AI device, so there is no training set for an algorithm.

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

• Not applicable. There is no training set for an AI algorithm.

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