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The Flexset system is intended for prescription use in a healthcare facility, home, and specific transport environments to acquire, transmit, display and store EEG and auxiliary signals for adults and children, not including newborns. The Flexset system acquires, transmits, displays and stores electroencephalogram (EEG), and optionally electrocardiogram (ECG), electrooculogram (EOG), electromyogram (EMG), orientation sensor data, photic sensor data, external trigger signals and video.

The Flexset System is intended to acquire, transmit, display and store primarily EEG and optionally auxiliary signals. Specific transport environments in the Indications for Use include ambulances, cars, buses, trains, boats and via air, per stipulation in the user manual of the device. The Flexset headset is designed to record a full montage EEG, with optional external references and additionally up to 8 auxiliary channels using lead wires for EEG, EOG, ECG or EMG. The device consists of the following components:

• Flexset (Headset) .
• Electrodes
• Charger with cable ●
• Display Unit ●
• Extension Unit ●
• Lead wires ●
• Software ●
  • Display Unit firmware o
  • Data center application (same as K172735) O
  • O Client application (same as K172735)

The provided 510(k) summary for the Zeto, Inc. Flexset System does not contain the specific details about the acceptance criteria or a dedicated study proving the device meets those criteria in the way typically expected for an AI/ML-driven diagnostic device.

This document describes a device for acquiring, transmitting, displaying, and storing EEG and auxiliary signals. It focuses on demonstrating substantial equivalence to a predicate device (WR19 System) and a secondary predicate device (X-Series System) based on technological characteristics and intended use. The performance data section refers to compliance with general medical device standards (e.g., IEC 80601-2-26:2019 for EEG performance) rather than specific acceptance criteria for diagnostic performance outcomes.

Therefore, many of the requested items cannot be extracted directly from this document. However, I can infer some information based on the provided text.

Here's a breakdown of what can and cannot be answered:

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

Limitations: The document does not specify quantitative acceptance criteria (e.g., "EEG noise must be < 5 µV"). Instead, it states compliance with general standards, which inherently include such criteria. The reported performance metrics are directly from the device's technical specifications, not necessarily results of a comparative performance study against a ground truth for a diagnostic task.

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 document. The document primarily focuses on technical specifications, safety, and regulatory compliance (electrical safety, EMC, biocompatibility). It describes the device's ability to acquire and display signals, not its performance in a diagnostic task that would typically involve a "test set" with ground truth from patient data.

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. The device is an electroencephalograph for signal acquisition and display. It does not perform automated diagnoses or interpretations that would require expert-established ground truth for a test set in the context of diagnostic accuracy.

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

This information is not applicable/not provided for the same reasons as #3.

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/not provided. The Flexset System is described as acquiring, transmitting, displaying, and storing physiological signals. There is no mention of AI/ML components performing diagnostic interpretations or assisting human readers in a way that would necessitate an MRMC study.

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

This information is not applicable/not provided. The device's function is signal acquisition and display, not automated interpretation or standalone diagnostic performance.

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

This information is not applicable/not provided. As a device for signal acquisition and display, "ground truth" in the diagnostic sense (e.g., for disease detection) is not relevant to the described performance evaluation in this document. The "ground truth" for its performance would be the accuracy and fidelity of the acquired signals, which is assessed against engineering standards and specifications.

8. The sample size for the training set

This information is not applicable/not provided. The device is not an AI/ML-driven diagnostic algorithm that would require a "training set." Its performance is based on hardware and software engineering design and adherence to established physiological measurement principles.

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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