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The Venclose System (Venclose digiRF Generator with EVSRF Catheter) is intended for endovascular coaqulation of blood vessels in patients with superficial vein reflux.

The Venclose Maven System (digiRF Generator and Maven Catheter) is intended for endovascular coagulation of blood vessels in patients with perforator and tributary vein reflux.

The Venclose™ digiRF Generator is a multi-voltage energy delivery system with touchscreen control that automatically sets the non-adjustable treatment parameters for the catheter to be used with the generator (time, temperature, etc.), which is connected via a triaxial catheter connector port. The Venclose™ digiRF Generator is intended to be used with Venclose™ RF Catheter(s) (either the Venclose™ EVSRF Catheter or the Venclose™ Maven Catheter) as a system. The Venclose™ RF System uses resistive radiofrequency ablation via energy delivery to heat the wall of an incompetent vein with temperature-controlled RF energy to cause irreversible luminal occlusion, followed by fibrosis and ultimately resorption of the vein.

This FDA 510(k) summary for the Venclose digiRF Generator (K250068) focuses on substantial equivalence based on software modifications to an existing device.

Here's an analysis of the provided information concerning acceptance criteria and the study that proves the device meets them:

1. A table of acceptance criteria and the reported device performance:

The document does not provide a table of specific acceptance criteria with corresponding performance metrics for the software modifications. It generally states that "the results demonstrate that the technological characteristics and performance criteria of the modified Venclose™ digiRF Generator is comparable to the predicate devices and that it performs as safely and as effectively as the legally marketed predicate devices."

The acceptance criteria implied are that the modified software continues to allow the device to perform its intended function (endovascular coagulation of blood vessels) safely and effectively, with no adverse effects introduced by the software changes.

2. Sample size used for the test set and the data provenance:

The document mentions "Software Verification and Validation" as a performed non-clinical test. However, it does not specify the sample size used for this test set (e.g., number of test cases, number of simulated scenarios). It also does not detail the data provenance (e.g., retrospective or prospective tests, or country of origin of data if real-world data was used, which is unlikely for software verification of this type of device).

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

This information is not provided in the document. For software verification and validation, ground truth might be defined by expected outputs for given inputs, established through engineering specifications and design documents rather than clinical expert consensus. Clinical experts would likely be involved in defining the original device's performance requirements, but not necessarily in the technical software verification changes.

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

The document does not specify any adjudication method. It's highly probable that for software verification and validation, the "ground truth" (expected behavior or output) is defined by the device's design specifications and tested against these specifications, rather than through a human adjudication process for subjective interpretation as would be common in image analysis AI.

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 MRMC comparative effectiveness study was done or mentioned. This type of study is typically performed for AI-powered diagnostic or assistive devices where human interpretation is involved. The Venclose digiRF Generator is an electrosurgical device; the software modification is presumably to its control and operational parameters, not its interpretation of patient data for a human.

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

The non-clinical test mentioned is "Software Verification and Validation." This type of testing is inherently a standalone (algorithm only) performance evaluation against predefined specifications. The document doesn't explicitly label it as such, but it's the nature of verifying software changes in a medical device.

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

The ground truth for "Software Verification and Validation" tests would typically be the design specifications and expected behavior of the software as defined by the engineering and quality teams. This is not clinical ground truth like pathology or outcomes data, but rather technical correctness and adherence to specified functional and non-functional requirements.

8. The sample size for the training set:

Not applicable and not provided. This device is not an AI/ML device that requires training data in the conventional sense. The "software modifications" refer to changes in the control software of the electrosurgical generator, not a learning algorithm.

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

Not applicable and not provided, as there is no training set for this type of device modification.

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