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ASC High Pressure Braided Tubes are accessory devices to angiographic catheters intended to provide an extended luminal connection between an angiographic injector and an angiographic catheter for the purpose of delivering radiopaque contrast medium to selected sites in the vascular system.

The HPBT are sterile, single use, disposable devices, that are delivered non-toxic and non-pyrogenic. The HPBT consists of braided polymeric tubing terminated at both ends with luer connectors. The luer connector may be one of three types: male rotating, or female. A typical HPBT is shown below. Cross-sectional views of the three luer connectors are shown on the next page. The tubing has three sub-components: extrusion base coat, reinforcement nylon braid, and extrusion top coat.

The description you've provided is for a 510(k) premarket notification for a medical device called "ASC High Pressure Braided Tubing" (HPBT), aiming to demonstrate substantial equivalence to a predicate device. This type of regulatory submission typically focuses on functional and safety performance rather than clinical efficacy studies often found with novel devices.

Given this context, the document doesn't describe a clinical study in the way a diagnostic AI device's performance would be evaluated. Instead, it describes bench testing to ensure the device meets engineering specifications and safety standards. Therefore, many of your requested points (sample size of test set, data provenance, ground truth, expert qualifications, MRMC studies, standalone performance, training set details) are not applicable in this context.

Here's how to interpret and answer your questions based on the provided document:

Acceptance Criteria and Reported Device Performance

The document describes several performance characteristics and standards the HPBT must meet. The "reported device performance" is integrated into the descriptions of compliance with these standards and the bench testing results, which aim to demonstrate equivalence or superiority to the predicate device.

Study Details (as applicable to a 510(k) bench testing submission)

1. Sample size used for the test set and the data provenance:
   The document states "extensive bench testing of both devices" was performed. It does not specify the exact sample sizes (number of units tested) for individual tests like tensile strength, burst strength, or luer compatibility.
   Data Provenance: The testing was conducted internally by Adam Spence Corporation, implying the data is from in-house lab testing, not clinical data from a specific country. This is a prospective set of tests designed specifically for this submission.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
   This question is not applicable in the context of this type of submission. "Ground truth" established by human experts is relevant for diagnostic AI devices where human interpretation is the benchmark. For this device (HPBT), ground truth is established by engineering specifications, validated test methods (e.g., ISO standards), and physical measurements. No human experts were used to establish a "ground truth" for the performance characteristics of the tubing itself.

3. Adjudication method (e.g. 2+1, 3+1, none) for the test set:
   This question is not applicable. Adjudication methods are used in studies involving human interpretation or clinical endpoints. For bench testing, results are objective measurements against predefined criteria/standards.

4. 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:
   This question is not applicable. MRMC studies are specific to evaluating diagnostic systems, especially those involving human interpretation (e.g., radiologists reading images with or without AI assistance). This document describes a medical device (tubing) for fluid delivery, not a diagnostic AI system.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
   This question is not applicable. This device is not an algorithm or an AI system. Its performance is entirely mechanical/physical.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
   For the bench testing, the "ground truth" is defined by engineering specifications, regulatory standards (e.g., ISO for luer fittings, AAMI for sterilization), and physical measurement thresholds. For example, the "ground truth" for rated pressure is 1200 PSI, which is a design specification and a quantifiable test outcome. The "ground truth" for biocompatibility is compliance with ISO-10993-1.

7. The sample size for the training set:
   This question is not applicable. There is no "training set" as this is not an AI/machine learning device. The device is manufactured based on design specifications and then tested against those specifications and relevant standards.

8. How the ground truth for the training set was established:
   This question is not applicable as there is no training set.

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ASC Maxi-Torque Plus Softip Angiographic Catheters are designed to deliver contrast medium to selected sites in the vascular system.

The ASCMPSACs are classified as Class II in 21 CFR §870.1200. The ASCMPSACs are sterile, single use, disposable devices, that are delivered non-toxic and non-pyrogenic. The ASCMPSACs consist of five components: catheter hub with luer connector, catheter shaft, catheter stem, soft distal tip, and catheter shaft braid wire. The catheter shaft has three sub-components: extrusion base coat, reinforcement braid wire, and extrusion top coat. The braiding is intended to provide torque control and strength. All versions of the catheter are designed to accommodate a maximum guidewire diameter of 0.038 inches. The strain relief is imprinted with three descriptive dimensions: the French size of the catheter, minimum inside diameter, and the catheter working length.

The document provided describes a 510(k) submission for the ASC Maxi-Torque Plus Softtip Angiographic Catheters (ASCMPSACs), asserting their substantial equivalence to a predicate device. The information focuses on safety and performance testing for a medical device rather than an AI/ML diagnostic tool. Therefore, many of the requested categories related to AI/ML study design (such as sample size for test/training sets, data provenance, number of experts for ground truth, adjudication methods, MRMC studies, and standalone performance) are not applicable to this submission.

Here's the breakdown of the available information:

Acceptance Criteria and Reported Device Performance

The acceptance criteria for the ASCMPSACs are based on compliance with specific international standards and successful bench testing demonstrating equivalence to the predicate device. The performance is reported through a comparison of technological characteristics and successful completion of various physical and material tests.

Non-Applicable Sections for this Device Submission:

Since this document describes a traditional medical device (angiographic catheter) and its substantial equivalence claims, rather than an AI/ML-driven diagnostic or treatment device, the following categories are not relevant and thus no information is provided in the source text:

• Sample size used for the test set and the data provenance: Not an AI/ML study.
• Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not an AI/ML study.
• Adjudication method for the test set: Not an AI/ML study.
• If a multi-reader multi-case (MRMC) comparative effectiveness study was done: Not an AI/ML study.
• If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not an AI/ML study.
• The type of ground truth used: For mechanical device testing, the "ground truth" is typically defined by engineering specifications, material properties, and physical test results, not expert consensus or pathology in the diagnostic sense.
• The sample size for the training set: Not an AI/ML study.
• How the ground truth for the training set was established: Not an AI/ML study.

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