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The Phenom™ Catheters are intended for the introduction of interventional devices and infusion of diagnostic or therapeutic agents into the neuro, peripheral, and coronary vasculatures.

The Phenom™ Catheters are variable stiffness, single lumen catheters designed to access small, tortuous vasculature. They are available in a variety of lengths, stiffness and inner and outer diameters. The outer surface of the catheter is coated to enhance navigation in the vessel. The catheter also incorporates a liner to facilitate movement of introduction devices passing through its lumen. The distal tip has radiopaque marker(s) to aid visualization and positioning under fluoroscopy.

This document is a 510(k) premarket notification for the Phenom™ Catheters. As such, it's a submission to the FDA to demonstrate substantial equivalence to legally marketed predicate devices, not a study proving device meeting specific clinical acceptance criteria in the way a clinical trial would.

Therefore, many of the requested points related to a specific clinical study (like sample size for test/training sets, data provenance, number of experts for ground truth, adjudication methods, MRMC studies, standalone performance, and ground truth types) are not applicable or directly derivable from this type of regulatory document.

However, I can extract information regarding the bench testing performed to establish substantial equivalence, which serves as the "study" in this context to prove the device meets certain performance characteristics relative to predicate devices.

Here's the breakdown based on the provided text, addressing the applicable points and explaining why some are not applicable:

Description of Acceptance Criteria and Substantiating Study

The Phenom™ Catheters are submitted as substantially equivalent to predicate devices, meaning their performance characteristics must be comparable to those existing devices and demonstrate no new safety or effectiveness concerns. The "acceptance criteria" are implicitly defined by the safety and performance standards established by the predicate devices and general regulatory requirements for this class of device. The "study" proving the device meets these criteria is a series of bench tests.

1. Table of Acceptance Criteria and Reported Device Performance

The document describes various bench tests conducted to support the substantial equivalence. The "acceptance criteria" for these tests would typically be defined internally by the manufacturer to ensure the new device performs comparably to or within acceptable limits derived from the predicate devices. Since specific numerical acceptance criteria (e.g., "burst pressure > X bar") are not explicitly stated in this summary, the table will list the performance tests performed, implying that the acceptance was meeting the internal specifications aligned with the predicate 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: This refers to the number of devices or components tested for each bench test. This information is not provided in this summary. Bench testing typically involves a statistically significant number of samples for each test, but the exact numbers are not detailed here.
• Data Provenance: The bench tests are laboratory-based, performed by the manufacturer (Cathera, Inc., Mountain View, CA). This is not human data, so "country of origin" or "retrospective/prospective" does not directly apply in the usual clinical sense. The worst case representation was chosen considering neurovascular anatomy.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• Not applicable. Bench testing for substantial equivalence does not involve establishing ground truth from human expert interpretation in the way clinical studies with AI algorithms do. Performance is measured against engineering specifications and comparisons to predicate device data.

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

• Not applicable. See point 3.

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 device is a catheter, not an AI diagnostic algorithm. Therefore, MRMC studies involving human readers and AI assistance are not relevant to its regulatory approval in this context.

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

• Not applicable. This device is a catheter, not an AI algorithm.

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

• The "ground truth" for the bench tests are engineering specifications, material standards, and performance characteristics derived from predicate devices. For example, "burst pressure" has a defined threshold, and "biocompatibility" is assessed against ISO standards. The evaluation included performance in "more complex and higher risk neurovascular anatomy," implying simulated environments or models representing this.

8. The sample size for the training set

• Not applicable. As this is a physical medical device (catheter) undergoing bench testing for substantial equivalence, there is no "training set" in the context of machine learning or AI.

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

• Not applicable. See point 8.

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The Plato MICROCATH 27B Microcatheter is intended for the introduction of interventional devices and infusion of diagnostic or therapeutic agents to the peripheral system. The cather is not intended for use in cither the coronary or neuro vasculature.

The Plato MICROCATH® 27B Microcatheter is a single lumen microcatheter constructed with a flexible polymer shaft of varying stiffness to aide in accessing vasculature. The catheter is designed to be used with a guide catheter and a steerable guidewire for accessing the vasculature. The proximal end of the catheter has a diameter of 3.4F, tapering to a distal OD of 3.2F, which can be inserted into a 5F guide catheter. The inner diameter is constant throughout the shaft length and accommodates up to a 0.025" guidewire. The catheter is 150 cm in length with a straight tip configuration which can be steam-shaped to the doctor's preferred shape. A steam shaping mandrel is included in the packaging. The microcatheter has hydrophilic coating on the outer distal shaft to reduce friction during manipulation in vessels and has one radiopaque tip marker to facilitate fluoroscopic visualization.

The provided document describes the FDA 510(k) summary for the Plato MICROCATH® 27B Microcatheter, focusing on demonstrating substantial equivalence to predicate devices, not on proving effectiveness for a new diagnostic or interventional AI device. Therefore, the traditional acceptance criteria, study design parameters, and concepts like 'human readers improve with AI' are not applicable to this submission.

Instead, the document details performance data collected from non-clinical testing to show that the new microcatheter is as safe and effective as existing, legally marketed predicate devices. The acceptance criteria essentially derive from established standards for medical devices and comparisons to the predicate devices.

Here's an adaptation of your requested table and information, tailored to the context of this medical device submission:

1. Table of Acceptance Criteria and Reported Device Performance

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

This document primarily reports on non-clinical bench and lab testing, and some limited biological/simulated use testing. The "test set" here refers to the number of devices or samples tested for each specific non-clinical performance attribute.

• Sample Sizes: Not explicitly stated for each test, but implied by the nature of bench testing where multiple samples are typically used to ensure reliability. For example, "all catheters" met specifications implies multiple catheters were tested. For animal studies (thrombogenicity) and cadaver studies, the sample size would be small (e.g., number of dogs, cadavers). For Radiopacity and Simulated Use, "physicians," plural, suggests more than one device per observer.
• Data Provenance: The data is primarily from bench-top laboratory testing and in vitro studies, in accordance with ISO and ASTM standards. Some tests involve animal models (e.g., thrombogenicity in dogs) and human cadavers. The context implies these studies were conducted by the manufacturer or their contracted labs. No specific country of origin for the data is mentioned, but the submission is to the U.S. FDA, and the company is US-based. This is all prospective testing, specifically designed to demonstrate compliance for the 510(k) submission.

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

• Experts: The document mentions "physicians" evaluated radiopacity and participated in cadaver testing. For simulated use, "physicians" also provided Likert scores.
• Number: Not explicitly stated how many physicians were involved in these evaluations. The plural "physicians" indicates more than one.
• Qualifications: Not explicitly stated in terms of years of experience or specific subspecialty (e.g., interventional radiologist), but given the context of medical device evaluation, it is implied that these are qualified medical professionals with expertise relevant to microcatheters and their use.

4. Adjudication Method for the Test Set

• Adjudication Method: Not explicitly detailed. For tests involving "physicians" (Radiopacity, Simulated Use, Cadaver Testing), it is stated that comparisons were made or scores obtained. The document does not specify if there was a formal adjudication process (e.g., 2+1, 3+1), rather it presents the aggregate findings or an overall determination of "comparable" or "acceptable."

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

• No, an MRMC comparative effectiveness study was not done. This type of study (often used for diagnostic imaging AI) is not applicable to a premarket notification for a physical medical device like a microcatheter. The comparison is primarily against established performance standards and a predicate device's existing characteristics. The "simulated use" and "cadaver testing" involve human readers/users (physicians) providing feedback, but this is to assess usability and performance in a simulated environment, not an MRMC study comparing AI with and without human assistance.
• Effect Size of Human readers with/without AI: Not applicable, as this is not an AI device.

6. If a Standalone Study (Algorithm Only Without Human-in-the-Loop Performance) Was Done

• Not applicable. This submission is for a physical medical device (microcatheter), not an algorithm or AI product. The performance evaluations are intrinsic to the device itself and its interaction with biological and simulated environments.

7. The Type of Ground Truth Used

The concept of "ground truth" as it applies to diagnostic or AI studies is not directly applicable here. Instead, the basis for comparison and acceptance is:

• Established Industry Standards: ISO 10993 (biocompatibility), ISO 10555 (intravascular catheters), ASTM standards.
• FDA Guidance: FDA guidance for Short-Term and Long-Term Intravascular Catheters, FDA Special Controls Document for PTCA Catheters.
• Predicate Device Performance: Direct comparison to the ev3 Marksman™ Catheter (K111490, K091559) and the Scientia Vascular, LLC Plato MICROCATH 27 (K121734). The goal is to demonstrate that the new device is "substantially equivalent" in safety and effectiveness.
• Expert Observational Consensus: For aspects like radiopacity and simulated use feedback from physicians.

8. The Sample Size for the Training Set

• Not applicable. This is not a machine learning or AI device, so there is no "training set." The device design and materials are based on engineering principles and prior device knowledge.

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

• Not applicable. As there is no training set for an AI model, this question does not apply. The "ground truth" for the device's design and expected performance comes from established engineering principles, regulatory standards, and the performance characteristics of predicate devices.

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