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510(k) Data Aggregation
(646 days)
The Loktal Medical Electronics ind. E com. Ltda EPP - Wavetronic 5000 Digital HF Surgical Unit is intended for resection, ablation, excision of soft tissue, coagulation, and desiccation of soft tissue in patients requiring surgery.
The Wavetronic 5000 Digital HF Surgical Unit is a non-sterile, reusable electrosurgical generator, which is designed to generate high frequencies (RF) of high voltage and low amperage current, operating at high frequency (4 MHz) to be employed by a variety of electrosurgical procedures. This action is achieved by front panel selection of waveforms and power levels. All selections are effected through push buttons and dials, and lamps, which give the operator feedback of status.
The Wavetronic 5000 Digital HF Surgical Unit is an electrosurgical cutting and coagulation device intended to remove tissue and control bleeding by use of high-frequency electrical current.
The device has four modes of operation;
- Cut a basic cutting mode that produces minimal heat.
- Blend (cut and coagulation) surface coagulation takes place simultaneously with cutting, with wave ratio being approximately 50/50.
- Coagulation a mode with high lateral heat emission.
- Bipolar (bipolar coagulation mode) bipolar coagulation mode is intended to remove tissue and control bleeding by use of high-frequency, electrosurgical current.
The control unit of the device front panel control provides the user with buttons, enabling to operate the device during therapy, and a display, which shows the set and indicated parameters. The therapeutic parameters can be set or adjusted any time through surgery, and displays the therapeutic method, the set power, and other necessary data required throughout the treatment.
The Wavetronic 5000 Digital HF Surgical Unit consists of the following main components:
- Wavetronic 5000 Digital high-frequency electromagnetic energy generator
- Optional wave selection (cut, blend, coag, bipolar)
- Footswitch
- Power cable
This document is a 510(k) Pre-market Notification for the Wavetronic 5000 Digital HF Surgical Unit. It aims to demonstrate substantial equivalence to legally marketed predicate devices.
Based on the provided text, the device itself (Wavetronic 5000 Digital HF Surgical Unit) is an electrosurgical cutting and coagulation device. The acceptance criteria and supporting studies outlined here refer to the performance of this hardware device, not to an AI/ML algorithm for diagnosis or prognosis.
Therefore, most of the requested AI/ML specific information (like AI-assisted human reader improvement, standalone AI performance, expert qualifications for AI ground truth, etc.) are not applicable to this submission.
Here's the information that can be extracted:
1. A table of acceptance criteria and the reported device performance:
The document doesn't explicitly present a "table of acceptance criteria" with corresponding "reported device performance" in the typical format of a clinical study for a diagnostic AI/ML device. Instead, it describes acceptance based on compliance with voluntary standards and successful completion of non-clinical tests. The "performance" is demonstrated by meeting these standards and passing tests.
Acceptance Criteria Category | Reported Device Performance |
---|---|
Overall Design | Meets all requirements as shown by acceptable results obtained in testing. |
Biocompatibility | Complies with applicable voluntary standards; passed testing. |
Electrical Safety | Meets all requirements; passed testing. |
Output | Confirmed to meet design inputs and specifications; passed testing. |
Applicability of Standards | Complies with IEC 60601-1, IEC 60601-1-2, IEC 60601-2-2. |
Functional Equivalence (to predicates) | Demonstrated through comparative analysis of intended use, technological characteristics, and principles of operation, showing no new questions of safety or effectiveness. |
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
This information is not applicable as this is a hardware device submission, not a study involving a test dataset of medical images or patient data in the context of an AI/ML device.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)
This information is not applicable. Ground truth is not established by experts for a hardware electrosurgical unit in the way it would be for an AI/ML diagnostic device.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
This information is not applicable. No adjudication method for a test set of data is described.
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
This information is not applicable. This is a hardware device, not an AI-assisted diagnostic tool.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
This information is not applicable. This device is a manual electrosurgical unit, not an AI algorithm. Its performance is inherent to its physical operation and energy delivery.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
This information is not applicable in the context of typical AI/ML ground truth. For this hardware device, the "ground truth" (or verification) is based on:
- Compliance with recognized voluntary standards: Such as IEC 60601-1, IEC 60601-1-2, IEC 60601-2-2. These standards define safety and performance requirements.
- Engineering specifications and design inputs: The device's output and functionality are verified against its own design specifications.
- Biocompatibility testing: To ensure materials are safe for patient contact.
- Electrical safety testing: To ensure the device operates safely electrically.
8. The sample size for the training set
This information is not applicable. There is no "training set" for this type of hardware device.
9. How the ground truth for the training set was established
This information is not applicable. There is no "training set" or corresponding ground truth establishment in the AI/ML sense for this device.
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