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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 procedures for connecting the primary devices of the bypass circuit. 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. Medtronic Tubing Packs are currently packaged into qualified clear tray and sterile packaging systems. This submission is to add additional tubing and connectors for use in Tubing Pack and is changing as part of continual process improvement efforts. The component additions include tubing and connectors that may be coated or uncoated. The coating types include: Cortiva, Trillium and Balance and these three types of coatings are identical to the cleared existing components for Tubing Packs (K891687, K012538 and K122811). The components being added do not have any changes in materials, technology or labeling than previously cleared components for Tubing Packs. These additional components are representative of currently cleared components and do not add any additional risk to patients therefore, Medtronic believes these components may be added to the Tubing Packs. All packaging components remain unchanged (peel pouches, ties, bands, trays, lids and shipper). There is no change to the sterile barrier, sterilization and test methods used for these components.

This looks like information for a regulatory submission for a medical device. Based on the provided text, here's a breakdown of the acceptance criteria and the study conducted to prove the device meets those criteria:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size and Data Provenance

The document does not explicitly state the sample size used for the test set in quantitative terms (e.g., number of units tested). Instead, it mentions that "worse case testing for tubing and connectors" was performed and "verification results were leveraged based on most challenging components." This suggests that a subset of components representing the most challenging scenarios was selected for testing.

The data provenance is from Medtronic, Inc.'s internal testing. It is prospective in the sense that the testing was conducted specifically for this submission to verify the performance of the added components. The country of origin of the data is implicitly the United States, as Medtronic is based there and submitting to the FDA.

3. Number of Experts and Qualifications for Ground Truth

This device (Medtronic Tubing Pack) is a Class II medical device, specifically "Cardiopulmonary Bypass Vascular Catheter, Cannula, or Tubing." The "ground truth" in this context refers to established engineering and biocompatibility standards and performance requirements for such medical components, rather than clinical interpretation by medical experts (like radiologists for imaging devices).

The document does not mention the use of experts to establish a "ground truth" for a test set in the way one would for an AI-powered diagnostic device. The "ground truth" for this device's performance is derived from established industry standards, internal product specifications, and regulatory requirements that define acceptable limits for pressure integrity, biocompatibility, etc. The document refers to "product specification" and "rationalization documentation" as the basis for evaluation.

4. Adjudication Method

An adjudication method (like 2+1, 3+1) is typically used in clinical studies where there's a need to resolve discrepancies in expert opinions on imaging or clinical assessments. For this type of device (tubing and connectors), the "adjudication" is based on objective measurement against predefined specifications and standards. There is no mention of human adjudication in the traditional sense for reconciling expert interpretations for a test set.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. MRMC studies are relevant for devices that involve human interpretation, particularly in diagnostic imaging, to assess improvements in reader performance with AI assistance. This device is a passive component for cardiopulmonary bypass and does not involve human interpretation in a diagnostic capacity or AI assistance.

6. Standalone Performance Study

The studies described are essentially standalone performance studies for the device components. The verification and validation testing assesses the "algorithm only" (if one considers the manufacturing process and design as the "algorithm" for a physical device) against predetermined performance specifications. The tests for pressure integrity, spallation, kink resistance, pull force, tubing life, and biocompatibility are all evaluations of the device's inherent performance characteristics without human interaction being part of the primary performance metric.

7. Type of Ground Truth Used

The ground truth used is primarily engineering specifications, material science standards, and biocompatibility standards, as outlined in the "product specification" and various "rationalization documentation" (e.g., Mechanical Requirements, Coating Requirements, Biocompatibility per product specifications). Essentially, the "ground truth" is established industry and regulatory benchmarks for the safety and efficacy of cardiopulmonary bypass components.

8. Sample Size for the Training Set

This document does not mention a "training set" as this is not an AI/machine learning device. The "training" for a physical medical device typically refers to the design, manufacturing process, and quality control procedures, which are developed and refined over time based on engineering principles and previous device iterations. There isn't a "training data" set in the context of an algorithm.

9. How Ground Truth for the Training Set Was Established

As there is no "training set" in the AI/machine learning sense, there is no ground truth established for it. The development and refinement of the device's design and manufacturing processes (analogous to "training" for a physical product) would be based on:

• Engineering design principles
• Material science knowledge
• Previous iterations of similar devices (predicates)
• User feedback
• Regulatory requirements and standards for medical devices.

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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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