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The NeoMetrics Selectiva SB Guidewire is intended to facilitate the placement of devices for diagnostic and interventional procedures.

NOTE: These guidewires are not intended for PTCA use.

The Selectiva SB Guidewires are constructed of a nickel-titanium alloy with a PTFE polymer jacket. Devices are available in a diameter of 0.035 inches and in lengths ranging from 40 to 300 cm.

The provided text is a 510(k) summary for a medical device called the "Selectiva SB Guidewire." This is a regulatory filing for a medical device, and as such, it focuses on demonstrating substantial equivalence to a predicate device, rather than providing a detailed clinical study demonstrating device performance against specific acceptance criteria for an AI or software-based device.

Therefore, the information requested about acceptance criteria, detailed study design, sample sizes, ground truth establishment, expert qualifications, adjudication methods, and MRMC studies, is largely not applicable or present in this document. This document describes a physical medical device (a guidewire), not an AI or software-based medical device.

However, I can extract the relevant information regarding the "study" that proves the device meets the (implied) acceptance criteria of substantial equivalence.

1. Table of Acceptance Criteria and Reported Device Performance

Since this is a physical guidewire and not an AI/software device, there aren't quantitative "acceptance criteria" in the same way one would have for an AI's performance metrics (e.g., sensitivity, specificity). Instead, the acceptance is based on demonstrating substantial equivalence to predicate devices. The performance is assessed through bench testing comparing the functional and performance characteristics to the predicate.

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size for Test Set: Not explicitly stated. The document refers to "Representative samples of the device" used for bench testing. This typically implies a statistically relevant but undisclosed number of devices were tested to characterize their mechanical and performance properties.
• Data Provenance: The data is generated from bench testing conducted by the manufacturer (NeoMetrics, Inc.). This is a prospective data generation process from newly manufactured devices. The concept of "country of origin of the data" is not applicable as it's not clinical data.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

• This is not applicable. The "ground truth" for a physical guidewire's performance in bench testing would be defined by engineering specifications and objective measurements (e.g., force required to buckle, torque response, lubricity measurements). It would not involve expert consensus in the way clinical image interpretation or diagnosis would.
• The comparison is to existing predicate devices, which were previously cleared based on their own performance data and established safety and effectiveness.

4. Adjudication Method for the Test Set

• Not applicable. This is not a clinical study involving human interpretation of data where adjudication would be necessary. Bench testing results are typically objective measurements against predefined engineering specifications.

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 a physical medical device submission, not an AI or software device. Therefore, no MRMC study, human readers, or AI assistance is involved.

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

• Not applicable. This is a physical medical device.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

• The "ground truth" in this context refers to established engineering specifications, performance standards, and the known performance characteristics of the predicate devices. It is based on objective physical measurements and comparisons rather than expert consensus, pathology, or outcomes data.

8. The Sample Size for the Training Set

• Not applicable. This is a physical device, not an AI/ML model that requires a training set. The design and manufacturing process are informed by engineering principles and experience, but there isn't a "training set" in the computational sense.

9. How the Ground Truth for the Training Set was Established

• Not applicable, as there is no "training set." The design and development of the device would be based on engineering principles, materials science, and knowledge gained from predicate devices, rather than a data-driven "ground truth" for training.

In summary, the provided document is a 510(k) premarket notification for a physical medical device. Its "study" consists of bench testing to demonstrate substantial equivalence to predicate devices, not clinical trials or AI performance evaluations.

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