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

The scope of this 510(k) is only the Venclose™ digiRF Generator as the generator software has been modified. The Venclose™ Maven Catheter is not in the scope of this submission as the changes discussed within this submission are only applicable to the Venclose™ digiRF Generator as used with the Venclose™ EVSRF Catheter. There is no change to the Venclose™ EVSRF Catheter, as previously cleared via K160754.

Based on the provided 510(k) Clearance Letter, the device in question is the "Venclose digiRF Generator" and the modifications are related to its software when used with the "Venclose EVSRF Catheter." The clearance letter states that the scope of this 510(k) is only the Venclose digiRF Generator as the generator software has been modified, and there is no change to the Venclose EVSRF Catheter.

The document explicitly states that the device is an "Electrosurgical Cutting And Coagulation Device And Accessories" and the testing performed was "Software Verification and Validation." There is no mention of a study involving human subjects, interpretation of medical images by experts, or any kind of diagnostic performance evaluation typically seen with AI/ML-based diagnostic devices.

Therefore, many of the requested criteria regarding acceptance criteria for diagnostic performance, ground truth establishment, expert adjudication, MRMC studies, and training/test set details for AI/ML models are not applicable to this submission. This is a clearance for a software modification to an electrosurgical generator, not a medical imaging AI/ML diagnostic aid.

Here's the breakdown of what can be gathered from the provided text, addressing the points where information is available and indicating where it is not applicable or not provided.

Device: Venclose digiRF Generator (VCRFG1) with Venclose EVSRF Catheter (VC10A256F60, VC10A256F100)
Type of Modification: Software Modification to the Venclose digiRF Generator.

1. Table of Acceptance Criteria and Reported Device Performance

Given that this is a software modification to an electrosurgical generator, the acceptance criteria are not related to diagnostic performance metrics like sensitivity, specificity, or AUC, but rather to software functionality, safety, and effectiveness. The document states that internal risk assessments procedures were used.

Note: Specific numerical acceptance values are not detailed in this public 510(k) summary, as they are typically proprietary and part of detailed engineering and software validation reports submitted to the FDA.

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

This is not applicable in the context of an AI/ML diagnostic performance test set. The "test set" here refers to the software verification and validation activities. The document does not specify exact "sample sizes" (e.g., number of test cases or iterations) for the software testing. Data provenance is also not applicable in the context of clinical data for AI/ML, as the testing relates to engineering and software validation.

• Software Verification and Validation: This typically involves rigorous testing against defined requirements, including unit testing, integration testing, system testing, and perhaps regression testing. The "sample size" would relate to the number of test cases executed, input parameters varied, and error conditions simulated. Specific numbers are not provided in this summary.
• Data Provenance: Not applicable as no clinical data for diagnostic performance was used in this clearance for a software modification to an electrosurgical device.

3. Number of Experts Used to Establish Ground Truth and Qualifications

Not applicable. Ground truth, in the sense of expert annotation of medical data, is not established for an electrosurgical generator's software modification. The "ground truth" for this device's performance would be its adherence to engineering specifications and its ability to safely and effectively deliver RF energy for its intended purpose, as measured by calibrated equipment and functional tests.

4. Adjudication Method for the Test Set

Not applicable. Adjudication methods (e.g., 2+1, 3+1) are for consensus building among human experts for ground truth label generation in diagnostic studies. This process is not part of software verification and validation for an electrosurgical device.

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

No. An MRMC study is relevant for evaluating the impact of an AI/ML diagnostic device on human reader performance. This 510(k) is for a software modification to an electrosurgical generator, not a diagnostic AI/ML device. Therefore, no MRMC study was performed or reported.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

No. "Standalone performance" refers to the diagnostic accuracy of an AI/ML algorithm by itself. This device is an electrosurgical generator. Its performance is measured by its ability to generate and deliver RF energy according to specifications, not by its diagnostic capabilities. The software's "performance" was evaluated through verification and validation activities.

7. The Type of Ground Truth Used

Not applicable in the AI/ML diagnostic sense. For this device, the "ground truth" for software validation would be derived from:

• Design Specifications: The documented requirements and expected behavior of the software and the device.
• Predicate Device Performance: The existing performance characteristics of the previously cleared predicate devices, to which the modified device is being compared for substantial equivalence.
• Engineering Standards and Measurements: Data from calibrated test equipment, electrical measurements, temperature readings, and time controls.

8. The Sample Size for the Training Set

Not applicable. This device is not an AI/ML algorithm that requires a training set. The software was likely developed using traditional software engineering methodologies.

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

Not applicable. As there is no AI/ML training set, there is no ground truth to establish for such a set.

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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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