(25 days)
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.
| Acceptance Criterion (Software) | Reported Device Performance (Summary) |
|---|---|
| Functional Performance (e.g., proper execution of treatment parameters, temperature control, time management, connectivity with catheter) | "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." (Implied successful completion of software verification and validation, meeting defined specifications) |
| Safety (e.g., absence of new hazards, proper error handling, electrical safety, EMC compliance) | "The results demonstrate that...it performs as safely...as the legally marketed predicate devices." (Implied successful safety testing) |
| Effectiveness (e.g., maintaining intended use and performance characteristics) | "The results demonstrate that...it performs as...effectively as the legally marketed predicate devices." (Implied successful effectiveness testing, maintaining intended function for endovascular coagulation) |
| Software Verification & Validation (e.g., adherence to software requirements, robust and reliable operation) | "Software Verification and Validation" was performed. Results demonstrated comparability to predicate devices. |
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.
§ 878.4400 Electrosurgical cutting and coagulation device and accessories.
(a)
Identification. An electrosurgical cutting and coagulation device and accessories is a device intended to remove tissue and control bleeding by use of high-frequency electrical current.(b)
Classification. Class II.