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MEDISISS Reprocessed ENT Ablation Wands (Coblators) are indicated for coagulation, ablation, resection, of soft tissue and hemostasis of blood vessels in otoloaryngology (ENT) procedures including: tonsillectomy/adenoidectomy and soft tissue reduction.

The device is a high frequency, bipolar ablation wand with suction capability that contains an integrated electrical cable and integrated saline delivery system.

The provided text is a 510(k) notification for MEDISISS Reprocessed ENT Ablation Wands (Coblators). This document is for a medical device (an ablation wand) and not for an AI/ML powered medical device. Therefore, the information requested such as "acceptance criteria and the study that proves the device meets the acceptance criteria", "sample size used for the test set and the data provenance", "number of experts used to establish the ground truth", "adjudication method", "multi reader multi case (MRMC) comparative effectiveness study", "standalone (i.e. algorithm only without human-in-the-loop performance)", "type of ground truth used", "sample size for the training set", and "how the ground truth for the training set was established" are not applicable to this document. These questions are typically relevant for discussing studies related to the performance of AI/ML algorithms.

The document focuses on demonstrating substantial equivalence to predicate devices through technological characteristics and performance specifications for regulatory clearance, rather than presenting a study with acceptance criteria in the manner requested for an AI/ML device.

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The PlasmaKinetic Generator section of the GII RF Workstation is indicated for ablation of coagulation of soft tissue and hemostasis of blood vessels in otorhinolaryngology (Head and Neck) surgery including: Adenoidectomy, Cysts, Head, Neck, Oral, and Sinus Surgery, Mastoidectomy, Myringotomy with effective Hemorrhage Control, Nasal Airway Obstruction by Reduction of Hypertrophic Nasal Turbinates, Nasopharyngeal / Laryngeal indications including Tracheal Procedures, Laryngeal Polypectomy, and Laryngeal Lesion Debulking, Neck Mass, Papilloma Keloids, Submucosal Palatal Shrinkage, Tonsillectomy, Traditional Uvulopalatoplasty (RAUP), Tumors, Tissue in the Uvula/Soft Palate for the Treatment of Snoring.

The Gyrus ENT Somnoplasty TCRF Generator section of the GII RF Workstation with the Temperature Controlled Radio-frequency (Somnoplasty®) Electrodes is indicated for coagulation of soft tissue including: The coagulation of enlarged tonsils in patients 13 years of age and older; the reduction of the incidence of airway obstructions, e.g., base of tongue, soft palate, etc., in patients suffering from UARS or OSAS; tissue coagulation in the inferior turbinates; and tissue coagulation in the uvula/soft palate which may reduce the severity of snoring in some individuals.

The Gyrus ENT G II RF Workstation is a combination of two previously cleared radio-frequency generators: 1. The Gyrus ENT Somnoplasty Temperature-Controlled Radio-frequency Generator -K020067, and 2. The Gyrus Medical Inc. (formerly Everest Medical Corporation) PlasmaKinetic™ Generator - K003060.

The Gyrus ENT G II Radio-frequency Workstation has two modes of operation: The monopolar mode has controls for maximum temperature and energy delivered. The unit has readouts for total energy delivered, impedance, temperature for two thermocouples and time of energy delivery. The bipolar mode has controls for output waveform type and power. The unit has readouts for set power and waveform. Connectors on the front panel include the monopolar connector for active electrode and dispersive electrode and separate dual bipolar connectors for PlasmaCision electrodes and bipolar instruments. The foot pedal is connected on the back panel. Accessories included with the G II Radiofrequency Workstation include the PlasmaCision electrodes, bipolar instruments, connector cable, footswitch and a power cable.

This document is a 510(k) Summary of Safety and Effectiveness for the Gyrus ENT G II Radio-frequency Workstation & Accessories, submitted in 2002. It describes the device, its intended uses, and its substantial equivalence to predicate devices. It does not contain information about pre-defined acceptance criteria or a specific study proving the device meets acceptance criteria in the manner applicable to AI/ML devices.

The information provided relates to a medical device from 2002, which is an electrosurgical generator, not an AI/ML device as implied by the detailed questions about ground truth, sample sizes for training/test sets, expert consensus, and MRMC studies. These types of studies and acceptance criteria are standard for AI/ML-driven diagnostics or assistive technologies, but not for a radio-frequency workstation as described.

Therefore, many of the requested fields cannot be directly answered from the provided text, as the underlying technology and regulatory requirements are different.

However, based on the context of a 510(k) submission, the "acceptance criteria" and "study" are interpreted as the demonstration of substantial equivalence to predicate devices through comparison and performance testing.

Here's an attempt to answer the questions based on the provided text, noting where the information is not applicable or not present for this type of device:

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

For a 510(k) submission of this type (electrosurgical generator), the "acceptance criteria" is typically demonstrating substantial equivalence to predicate devices in terms of intended use, technological characteristics, and safety/effectiveness. The "reported device performance" is the claim that this equivalence has been met through comparison and testing.

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 provided in the 510(k) Summary. For a device of this nature, performance testing would typically involve bench testing and possibly animal or cadaveric studies, rather than a "test set" of clinical data in the AI/ML sense. Clinical data (if any was used for specific performance claims beyond basic safety) details are not included in this summary document.

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/not provided. "Ground truth" in the context of expert consensus on diagnostic images is specific to AI/ML diagnostic tools. For an electrosurgical device, performance would be assessed through objective measurements of its physical output (e.g., power, temperature control, tissue effects) rather than human interpretation.

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

This information is not applicable/not provided. Adjudication methods are relevant for human interpretation tasks, not for the performance assessment of an electrosurgical generator.

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 is not applicable. This device is an electrosurgical generator, not an AI-assisted diagnostic or decision-support tool for human readers. Therefore, an MRMC study is outside the scope of its evaluation.

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

This is not applicable. The device is a physical electrosurgical workstation, not an algorithm. Its operation requires a human operator ("qualified medical personnel trained in the use of electrosurgery").

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

This is not applicable in the context of AI/ML ground truth. For an electrosurgical device, the "ground truth" equivalent for performance testing would be objective physical measurements and observations of tissue effects (e.g., coagulation depth, ablation rates) against predetermined specifications or compared to the predicate devices' known performance. The summary does not detail these specific "ground truths" but rather refers to "performance testing."

8. The sample size for the training set

This information is not applicable/not provided. Training sets are relevant for machine learning algorithms. The development of this electrosurgical generator would involve engineering design, prototyping, and testing, not algorithmic training on data.

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

This information is not applicable/not provided for the same reasons as #8.

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