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The PORT-A-CATH® II Regional Arterial Portal System will be indicated when patient therapy requires prolonged or repeated intra-arterial access for injection or infusion regimes.

A system consists of a portal with a self-sealing silicone septum, accessible by percutaneous needle puncture, a single lumen silicone catheter, a hemostasis sy assembly, and a micro-catheter assembly

The provided text describes a 510(k) submission for the PORT-A-CATH® II Regional Arterial System. However, it does not contain information typically associated with AI/ML device studies, such as:

• Acceptance criteria and reported device performance in terms of metrics like sensitivity, specificity, or accuracy. The document focuses on functional and material equivalence to predicate devices.
• Sample sizes for test sets, training sets, or data provenance relevant to AI model development.
• Number and qualifications of experts or adjudication methods for ground truth establishment.
• Details about multi-reader multi-case (MRMC) comparative effectiveness studies.
• Information on standalone algorithm performance.
• Specific types of ground truth (e.g., pathology, outcomes data) as they relate to AI model training/testing.

Therefore, I cannot populate the table or provide detailed answers to most of the requested points as they are not present in the provided document.

The study described is primarily a pre-market notification (510(k)) for a medical device, not an AI/ML diagnostic or therapeutic device study. The "studies" mentioned are largely functional, in-vitro, and in-vivo non-clinical tests to demonstrate the physical properties and performance of the device, along with biocompatibility testing. Clinical studies were not deemed necessary due to the similarity to existing predicate devices.

Here's what can be extracted based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Sample Size for Test Set: Not specified in terms of number of devices or clinical subjects, as the testing described is primarily in-vitro and in-vivo feasibility in a non-clinical context.
• Data Provenance: Not applicable in the context of clinical data for AI/ML validation. The testing appears to be internal to Deltec, Inc.

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. Ground truth establishment by human experts for performance metrics (like accuracy for a diagnostic AI) is not relevant to the functional and material testing described.

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

• Not applicable. Adjudication methods are not described as there are no expert evaluations of performance being evaluated in this manner.

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:

• No. This was not an AI/ML device.

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

• Not applicable. This was not an AI/ML device.

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

• The "ground truth" for this device would be its ability to meet engineering specifications and material properties through various functional, mechanical, and biocompatibility tests. It's not a clinical diagnostic or AI-driven "ground truth."

8. The sample size for the training set:

• Not applicable. This was not an AI/ML device that requires training data.

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

• Not applicable. This was not an AI/ML device.

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