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The DLP™ Silicone Coronary Artery Ostial Cannulae, Models 30315, 30317, and 30320 are intended for use in conjunction with cardiopulmonary bypass surgery up to six hours or less for delivery of cardioplegia solutions directly to the coronary arteries.

The DLP™ Silicone Coronary Artery Ostial Cannulae, Models 30315, 30317, and 30320 feature a soft bulb beveled tip with a silicone body. The Cannulae terminate with a locking female luer fitting. The Cannulae are nonpyrogenic, single use, and sterile.

The provided text describes a 510(k) premarket notification for a medical device, the DLP Silicone Coronary Artery Ostial Cannulae. It discusses a material formulation change to an adhesive used in the device and the testing performed to demonstrate substantial equivalence to a predicate device.

However, the request asks for information related to a study proving device meets acceptance criteria for an AI/machine learning device, including aspects like:

• Acceptance criteria table and reported performance
• Sample size and data provenance for test sets
• Number and qualifications of experts for ground truth
• Adjudication methods
• MRMC studies for human reader improvement
• Standalone algorithm performance
• Type of ground truth
• Training set sample size and ground truth establishment

The provided FDA document is for a physical medical device (cannulae) and focuses on demonstrating substantial equivalence due to a material change, not on the performance evaluation of an AI or machine learning algorithm. Therefore, the document does not contain any of the information requested regarding acceptance criteria, study design, expert involvement, or AI performance metrics.

In summary, none of the specific information requested in the prompt can be extracted from the provided text because the text describes a physical medical device submission, not an AI/machine learning device submission.

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These Cannulae are intended for use in conjunction with cardiopulmonary bypass surgery up to six hours or less for delivery of cardioplegia solutions directly to the coronary arteries.

The DLP® Silicone Coronary Artery Ostial Cannulae feature a soft bulb beveled tip with a silicone body. The Cannulae terminate with a locking female luer fitting. The Cannulae are nonpyrogenic, single use, and sterile.

This document is a 510(k) Pre-Market Notification from the FDA regarding Medtronic's DLP® Silicone Coronary Artery Ostial Cannulae. It confirms the device's substantial equivalence to previously marketed predicate devices. The information provided heavily focuses on regulatory approval and comparisons to a predicate device, rather than a clinical study evaluating its performance against specific acceptance criteria for a new AI/software device. Therefore, much of the requested information regarding AI device studies is not applicable or present in this document.

However, I can extract the information that is present concerning the device itself and the testing performed for its regulatory approval.

1. Table of Acceptance Criteria and Reported Device Performance

This document does not present "acceptance criteria" in the traditional sense of a clinical trial's primary endpoints for a new AI device or a software's performance metrics (e.g., sensitivity, specificity). Instead, for this medical device (cannulae), the "acceptance criteria" are implied by the performance tests conducted to ensure substantial equivalence to a predicate device after a material change. The "reported device performance" reflects the successful completion of these tests.

Note: The document states "The following performance tests were conducted:", but then only lists the results of those tests under the "Verification/Validation" column for the specific component changes.

2. Sample Size for Test Set and Data Provenance

This document does not describe a "test set" in the context of clinical data for an AI/software device. The performance tests mentioned (Tensile Pull-Off Force, Dimensional analysis, Biocompatibility, Rub off test) are engineering/material tests performed on the device components. The sample size for these specific tests is not explicitly stated in this document.

Given that this is a 510(k) submission for a physical medical device (cannulae) and not a software/AI device, the concept of "data provenance (e.g. country of origin of the data, retrospective or prospective)" as it relates to patient data is not applicable here. The "data" refers to engineering test results.

3. Number of Experts and their Qualifications for Ground Truth

This information is not applicable and not provided in the document. The regulatory filing is for a physical medical device (cannulae) based on demonstrating substantial equivalence through engineering tests, not an AI/software device requiring human expert ground truth for clinical performance.

4. Adjudication Method for the Test Set

This information is not applicable and not provided in the document. Adjudication methods like 2+1 or 3+1 are typically used in clinical studies or for establishing ground truth in AI datasets, which is not the subject of this 510(k) submission.

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

This information is not applicable and not provided in the document. MRMC studies are used for evaluating the performance of imaging devices or AI algorithms interpreted by human readers, which is not relevant to this physical device submission.

6. Standalone Performance Study (Algorithm Only)

This information is not applicable and not provided in the document. This device is a physical medical instrument, not an algorithm, so a "standalone algorithm only" performance study is not relevant.

7. Type of Ground Truth Used

For the engineering tests conducted, the "ground truth" would be established by established engineering standards, material specifications, and validated test methods. For example, the "Pass" result for tensile pull-off force indicates that the component met pre-defined strength requirements, which serve as the "ground truth" for its mechanical integrity. This is not "expert consensus," "pathology," or "outcomes data" in a clinical sense.

8. Sample Size for the Training Set

This information is not applicable and not provided in the document. A "training set" refers to data used to train an AI algorithm, which is not relevant to this physical device submission.

9. How Ground Truth for the Training Set was Established

This information is not applicable and not provided in the document, as there is no training set for an AI algorithm.

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