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This product is indicated for use in the extracorporeal circuit during cardiopulmonary bypass (CPB) surgical procedures.

Medtronic Tubing Packs are used in cardiopulmonary bypass for connecting the primary devices of the bypass circuit. The functionality and intended use of these devices are the same as those for the coated (Cortiva - Coated Tubing Packs(K891687), Trillium BioPassive Surface Coated Tubing Packs (K012538), and Balance BioSurface Coated Tubing Packs(K122811) and uncoated tubing, connectors, and accessories that are FDA cleared.

Medtronic Tubing Packs consist of coated and uncoated tubing, connectors and various medical devices that are pre-assembled into user-specified extracorporeal cardiopulmonary bypass perfusion circuits. Tubing Packs are intended for use within the extracorporeal blood pathway during cardiopulmonary bypass surgical procedures.

The provided text describes a 510(k) premarket notification for a medical device called "Tubing Pack" by Medtronic, Inc. This submission focuses on a packaging change and does not involve the development or evaluation of an AI/ML powered device. Therefore, many of the requested elements for describing the acceptance criteria and study proving an AI device meets them are not applicable to this document.

However, I can extract and present the information that is relevant to the device and its testing as described in the document, even though it's not specific to AI.

Here's a breakdown based on the information available:

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

The document does not explicitly state quantitative "acceptance criteria" in a table format with corresponding "reported device performance" in the context of an AI/ML model's output metrics (e.g., sensitivity, specificity, AUC). Instead, it discusses demonstrating equivalence to a predicate device through various tests, implying that the acceptance criterion is successful completion of these tests without adverse outcomes.

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

The document does not provide details on sample sizes for these tests, nor does it specify data provenance in terms of country of origin or retrospective/prospective nature. These kinds of details are typically provided for clinical studies involving patient data, not for engineering and material testing of packaging components.

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. The tests performed are engineering and material science evaluations, not clinical assessments requiring expert human interpretation of data for ground truth.

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

Not applicable. Adjudication methods are typically use for resolving discrepancies in expert interpretations in clinical studies.

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/ML device, and no MRMC study was conducted.

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

Not applicable. This is not an AI/ML device.

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

The "ground truth" for these tests would be established through scientific and engineering standards and validated test methods. For example:

• Shelf Life: Stability of the product over time, measured through established degradation testing.
• Sterilization: Achievement of a specified Sterility Assurance Level (SAL) confirmed by validated biological indicators and methods.
• Biocompatibility: Absence of adverse biological reactions as per ISO 10993 standards and associated testing.
• Packaging Performance: Adherence to ISO or ASTM packaging integrity test standards.

8. The sample size for the training set

Not applicable. There is no AI/ML model involved, and thus no training set.

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

Not applicable. There is no AI/ML model involved.

In summary, the provided document details a regulatory submission for a packaging change to a medical device. It thoroughly outlines the scope of the change (material, supplier, mold, and slight dimension changes to the tray) and the types of tests performed to ensure that these changes do not negatively impact the device's safety and effectiveness, and that it remains substantially equivalent to its predicate. However, it does not involve an AI/ML component and therefore lacks the information requested for AI device validation.

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