(50 days)
The Valleylab FX8 FX Series Energy Platform is a high frequency electrosurgical generator intended for use with monopolar and bipolar accessories for cutting and coagulating tissue.
Valleylab FX8 FX Series Energy Platform is a radio-frequency (RF) electrosurgical generator that delivers energy to compatible surgical instruments. The concentration of energy at the tip of the instrument in conjunction with tissue characteristics produces heat. The heating of tissue provides the desired surgical effect (cutting, coagulation, sealing). Variations in the waveform result in the different surgical effects achieved by different modes.
The provided text describes a 510(k) premarket notification for the Valleylab FX8 FX Series Energy Platform, an electrosurgical generator. The submission aims to demonstrate substantial equivalence to a predicate device, the Valleylab FT10 Energy Platform.
However, the documentation does not include a study proving the device meets specific performance acceptance criteria in the way one might expect for an AI/ML medical device, which would typically involve metrics like sensitivity, specificity, or AUC against a ground truth.
Instead, the provided text describes the verification and validation activities performed to demonstrate that the Valleylab FX8 FX Series Energy Platform performs as intended and is substantially equivalent to its predicate. These activities focus on safety, functionality, and performance equivalence, rather than a clinical effectiveness study with human readers or a standalone algorithm.
Here's a breakdown based on the information available, addressing the requested points:
Acceptance Criteria and Device Performance Study (as described in the document)
The document primarily focuses on demonstrating substantial equivalence through engineering and laboratory testing, rather than a clinical performance study with defined "acceptance criteria" and "reported device performance" in terms of classification metrics (e.g., sensitivity, specificity) against a clinical ground truth.
The "acceptance criteria" for this device are implicitly tied to:
- Compliance with specific electrical safety and EMC standards.
- Comparable performance to the predicate device in ex vivo tissue testing.
- Meeting system specifications.
- Successful software verification and validation.
1. Table of acceptance criteria and reported device performance:
Based on the provided text, a table like this would represent the types of tests done and the general outcomes, rather than quantitative performance metrics against a clinical outcome.
| Acceptance Criteria Category | Description of Acceptance Criteria (Implicit) | Reported Device Performance/Outcome |
|---|---|---|
| Electrical Safety & EMC Standards | Compliance with IEC 60601-1:2005/A1:2012, IEC 60601-1-2:2014, and IEC 60601-2-2:2009. | Met: "Compliance with Electrical Safety and EMC standards." |
| Ex Vivo Testing | Comparable performance to predicate (Valleylab FT10) regarding thermal effects on porcine tissue. | Met: "Ex vivo testing using porcine tissue showed comparable performance with regard to thermal effects." |
| System Functionality | All required functionality and meeting system specifications. | Met: "System verification showed that the Valleylab FX8 FX Series Energy Platform has all required functionality and that it meets system specifications." |
| Software Verification & Validation | Documentation and testing in accordance with FDA guidance for software in medical devices. | Met: "Software verification and validation testing was conducted and documentation provided in accordance with FDA’s, Guidance or the Content of Premarket Submissions for Software Contained in Medical Devices." |
| Overall Substantial Equivalence | Differences compared to predicate do not raise new questions of safety or effectiveness. | Met: "The new generator has similar performance when compared to the predicate device. The differences do not raise any new questions of safety and efficacy when compared with the predicate." |
2. Sample size used for the test set and data provenance:
- Test Set Sample Size: Not specified quantitatively. The ex vivo testing mentions "porcine tissue" but does not give a sample size (e.g., number of tissue samples, number of tests performed).
- Data Provenance: The ex vivo testing was performed using "porcine tissue," implying a laboratory setting. The country of origin and whether it was retrospective or prospective is not stated, but it's clearly a controlled, pre-market lab-based testing scenario, not a retrospective analysis of clinical patient data.
3. Number of experts used to establish the ground truth for the test set and their qualifications:
- Not Applicable. For this type of electrosurgical generator, "ground truth" concerning thermal effects or system functionality would be established through physical measurements, engineering specifications, and validated test methods, not by expert consensus on clinical images or diagnoses.
4. Adjudication method for the test set:
- Not Applicable. As there are no human experts classifying outcomes on a test set, no adjudication method (like 2+1 or 3+1) was used.
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, what was the effect size of how much human readers improve with AI vs without AI assistance:
- No. An MRMC study was explicitly not performed. The document states: "This premarket submission did not rely on the assessment of clinical performance data to demonstrate substantial equivalence." This type of study is typically done for diagnostic AI devices, not electrosurgical generators.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
- No, not in the context of an AI algorithm. This device is an electrosurgical generator, not a diagnostic algorithm. Its "performance" is evaluated based on its physical characteristics, energy delivery, safety, and functionality, not its ability to interpret data independently.
7. The type of ground truth used:
- For electrical safety and EMC: Engineering standards and measured electrical characteristics.
- For ex vivo testing: Physical measurements of thermal effects on porcine tissue.
- For system verification: Engineering specifications and functional tests.
- For software: Software requirements, design specifications, and standard software quality assurance testing.
8. The sample size for the training set:
- Not Applicable. This is not an AI/ML device that uses a "training set" in the conventional sense. The device's "training" is its design, engineering, and manufacturing process.
9. How the ground truth for the training set was established:
- Not Applicable. As this is not an AI/ML device, there is no "training set" or ground truth for it. Its operational parameters are determined by electrosurgical principles and engineering design.
In summary: The provided document is a 510(k) summary for an electrosurgical generator, which is a physical medical device, not a software algorithm or AI-powered diagnostic tool. Therefore, the "study that proves the device meets the acceptance criteria" is fundamentally different from what would be expected for an AI/ML product. The "acceptance criteria" discussed are related to engineering and safety standards, and performance equivalence to a predicate device, rather than diagnostic accuracy metrics.
§ 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.