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The Grass MR Conditional/CT Cup Electrodes and Arrays are intended for use in the recording of the Electroencephalogram (EEG), the evoked potential (EP), or as a ground and reference in an EEG or EP recording.

This device is non-sterile for Single Patient Use Only and may remain on the patient in an MRI or CT environment under specific conditions.

Grass® MR Conditional/CT Cup Electrodes are for surface monitoring EEG, electromyogenic and physiologic data signals commonly recorded on the chest, extremities, head and faces of patients. Grass® MR Conditional/CT Array Cup Electrodes are also used in electrocardiography, evoked potentials, and polysomnography. The Grass® MR Conditional/CT Array Cup Electrodes are MR conditional/CT. The included extension cables are MR/CT Unsafe.

The Operating Principle and technological characteristics of the Grass MR Conditional Cup Electrodes are identical to the predicate device (K171102), with a few dimensional and material modifications that have been assessed to be equivalent to the predicate and therefore do not affect the fundamental scientific technology, safety, or effectiveness of the device (reference Substantial Equivalence of Technological Characteristics table, below). The test methods were identical to those used to assess the predicate device.

The provided text describes the 510(k) premarket notification for the Grass® MR Conditional/CT Cup Electrodes. This document focuses on demonstrating substantial equivalence to a predicate device rather than presenting a study proving a device meets specific acceptance criteria for AI/ML performance. As such, many of the requested details regarding AI/ML device performance studies (e.g., sample sizes for test/training sets, expert consensus for ground truth, MRMC studies, standalone performance) are not applicable or present in this specific regulatory submission.

The document primarily details the physical and functional characteristics of the electrodes and their compliance with safety standards and predicate device equivalency, particularly concerning MR conditional features.

However, I can extract the closest applicable information related to "acceptance criteria" and "device performance" based on the provided text, interpreting "performance" in the context of the device's electrical and physical properties as assessed for medical device regulatory submission.

Here's an attempt to fill in the table and answer the questions based on the provided non-AI/ML medical device submission:

Acceptance Criteria and Reported Device Performance (Non-AI/ML Device)

Study Details (Based on available information for a non-AI/ML device submission):

1. Sample size used for the test set and the data provenance:

   • The document does not specify a "test set" in the context of an AI/ML model. Instead, it refers to "non-clinical testing" and "bench verification & validation" performed on the physical device. The sample sizes for these tests (e.g., how many electrodes were tested for resistance or MR compatibility) are not explicitly stated.
   • Data provenance: "Internal requirements and specifications," "accredited MR testing laboratory on behalf of Natus Medicals." The country of origin is not specified for the test data, but the applicant's address is Ireland. The tests conducted are "non-clinical" bench tests, not clinical studies, thus the retrospective/prospective distinction for patient data is not applicable.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • This question is not applicable as the device is a physical medical electrode, not an AI/ML algorithm requiring expert ground truth for image or diagnostic interpretation. The "ground truth" here is established by physical measurement standards (e.g., resistance, impedance) and compliance with relevant ISO/IEC/ASTM standards. The "experts" would be the engineers and technicians performing and verifying these standard-based tests.

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

   • Not applicable for physical device testing against predefined engineering and safety standards. Adjudication methods are typically employed in clinical studies or expert labeling tasks for AI/ML datasets.

4. 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, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. This type of study is specifically designed for AI/ML devices that assist or replace human readers in diagnostic tasks. The Grass® MR Conditional/CT Cup Electrodes are physical electrodes for recording biosignals, not an AI/ML diagnostic tool.

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

   • Not applicable. This device is not an algorithm. Bench testing for its physical and electrical properties was performed, which could be considered analogous to "standalone" performance for a physical device (i.e., its intrinsic properties were measured without human interaction influencing the measurement itself, beyond setup).

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

   • The "ground truth" for this device's performance validation is based on established engineering and safety standards (e.g., IEC 60601-1, ASTM D4169, FDA Guidance for Cutaneous Electrodes for Recording Purposes). Performance metrics like resistance and impedance have objectively defined acceptable ranges. For MR compatibility, the "ground truth" is determined by measurements against the specified MR environment conditions.

7. The sample size for the training set:

   • Not applicable. This device is not an AI/ML algorithm, so there is no "training set."

8. How the ground truth for the training set was established:

   • Not applicable, as there is no training set for an AI/ML model.

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