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The Nurochek System is intended for prescription use in healthcare facilities or clinical research environments for subjects ages 14 years and older. The Nurochek System is indicated for the generation of visual evoked potentials (VEPs) and to acquire, transmit, display and store electroencephalograms (EEGs) during the generation of VEPs. The Nurochek System only acquires and displays physiological signals: no claims are being made for use as a diagnostic criterion or for the analysis of the acquired signals with respect to the accuracy, precision and reliability.

The Nurochek System combines hardware, firmware and software to generate and acquire physiological signals, specifically, VEPs. These VEPs are generated by a visual stimulus delivered through the Nurochek headset worn by the subject. This visual stimulus is a short-duration flash of white light. The Nurochek headset acquires the VEPs from the rear of the head and transmits the resulting EEG to the Nurochek software application to be displayed to the user and stored. These acquired signals are intended to be analyzed by a Physician. The Nurochek System operates on the principles of generating VEPs via photic simulation and acquiring the VEPs via EEG. Photic stimulation is provided through short-duration flashes of white light from multiple LEDs located in the front of the headset to direct the stimulus into the subject's eyes. The VEPs are acquired by an EEG comprising of a total of 5 electrode interfaces with hydrophilic foam cylinders saturated with saline solution to provide electrical contact to the subject's scalp. A Bluetooth receiver and transmitter located within the Nurochek headset allows it to communicate with and be controlled by the Nurochek software application. The Nurochek software application provides a graphical user interface which allows: Collection of the subject details and consent, Initiation of a study and tracking of patient information, Acquisition and transmission of signals wirelessly to and from the headset, Display of the contact quality of electrodes to the subject's scalp, Recording, processing and display of EEG signals received from the headset, and Manage previous EEG recordings of VEPs.

The provided document, a 510(k) Premarket Notification for the Nurochek System, focuses on demonstrating substantial equivalence to predicate devices rather than proving the device meets specific acceptance criteria through a detailed clinical study with performance metrics. While it mentions non-clinical performance data and a small clinical study, it does not explicitly provide a table of acceptance criteria with corresponding performance data in the format requested.

The document states that "The Nurochek System only acquires and displays physiological signals: no claims are being made for use as a diagnostic criterion or for the analysis of the acquired signals with respect to the accuracy, precision and reliability." This statement is critical as it limits the scope of claims and thus the type of performance data required for acceptance. The study described focuses on functional equivalence in signal acquisition rather than diagnostic accuracy.

Despite this, I can extract information related to the device's performance based on the functional capabilities and a small clinical study mentioned.

Here's an attempt to answer your request based on the provided text, acknowledging the limitations in scope for a device that "only acquires and displays physiological signals and makes no claims in relation to diagnoses."

1. Table of acceptance criteria and the reported device performance:

Since the document defines the device as one that "only acquires and displays physiological signals" and makes "no claims ... for the analysis of the acquired signals with respect to the accuracy, precision and reliability," the acceptance criteria are not in terms of diagnostic performance (e.g., sensitivity, specificity). Instead, they are related to the functional equivalence of signal acquisition and display compared to a benchmark device, and safety/technical compliance.

• Dynamic Range: Nurochek: +/- 187,500 μV (superior) vs. Predicate: +/- 1000μV.
• Resolution: Nurochek: 0.02μV (superior/more accurate) vs. Predicate: 0.03μV.
• Peak to Peak Noise: Nurochek: 1.97μV (typical) (lower/superior) vs. Predicate: 3.7μV (typical).
• Common Mode Rejection Ratio: Nurochek: 110 dB vs. Predicate: 110 dB (same).
• Input Impedance: Nurochek: 1GOhm vs. Predicate: 100GOhm (Both exceed OSET recommendation of 10 MOhm).
• Impedance Check Functionality: Both have impedance check. |

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

• Test Set Sample Size: 20 participants for the clinical study comparing SSVEP detection.
• Data Provenance: The document does not explicitly state the country of origin for the clinical study data or if it was retrospective or prospective. Given the submitter (Cryptych Pty Ltd) is based in North Sydney, NSW, Australia, and the submission is to the FDA, it's possible the study was conducted in Australia, but this is not confirmed. It was a "clinical study," which typically implies prospective data collection, but this is also not definitively stated as "prospective."

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

• The document mentions using the "clinical EEG Compumedics Grael EEG as a reference device and benchmark" for the clinical study. It does not mention experts establishing a "ground truth" in the traditional sense of labeling data (e.g., for diagnostic AI). The comparison was presumably based on the raw or processed signals from the two devices themselves.
• Given the device "only acquires and displays physiological signals" and makes "no claims ... for the analysis of the acquired signals with respect to the accuracy, precision and reliability," the concept of "ground truth" for diagnostic accuracy is not applicable as per the device's intended use. The "ground truth" essentially refers to the output of the benchmark medical device.

4. Adjudication method for the test set:

• Not applicable as the study involved comparing raw physiological signal acquisition between two devices, not interpretation or diagnosis requiring expert consensus/adjudication. The statement "both systems functioned identically in their ability to detect SSVEPs" suggests a direct comparison of the acquired signals or derived metrics.

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:

• No, a multi-reader multi-case (MRMC) comparative effectiveness study was not performed.
• This device is not an AI-assisted diagnostic tool, but rather a device for acquiring and displaying physiological signals (VEPs and EEGs). Therefore, a study on human reader improvement with AI assistance is not relevant to its stated indications for use.

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

• The non-clinical performance data (electrical safety, light safety, biocompatibility, cleaning, mechanical, firmware/software) represent standalone testing of the device's technical aspects and capabilities.
• The clinical study was a comparison of signal acquisition between the Nurochek System and a reference EEG device; it wasn't an "algorithm-only" performance study in an AI context, but rather a verification of the physiological signal acquisition system.

7. The type of ground truth used:

• The "ground truth" for the clinical study was the performance of a cleared predicate/reference medical device (Compumedics Grael EEG) in detecting SSVEPs. This falls under the category of "comparison to a reference standard medical device."
• For the non-clinical tests, the "ground truth" was compliance with established international and national standards (e.g., IEC, ISO, ANSI, FDA guidances).

8. The sample size for the training set:

• The document implies that the device "only acquires and displays physiological signals," and the software manages and displays these. It does not describe a machine learning model that would require a "training set" in the sense of supervised learning for classification or prediction. Therefore, the concept of a training set for an AI model is not applicable here. The software validation refers to standard software engineering verification and validation activities (IEC 62304), not AI model training.

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

• As a training set for an AI model is not applicable, this question is not relevant.

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The Precision Screw is intended for establishing fixed reference point(s) in patients undergoing Stereotactic Surgery for procedures in the cortical bone of the vertebrae. The Precision Screw can be used as an accessory with frameless navigation systems.

The Precision Screw is a self-drilling metallic fixation device used only as a reference point during stereotactic procedures. Commonly known as a Fiducial Screw or a stereotaxic instrument, the Precision Screw can be used as an accessory with frameless navigation systems, as appropriate, from other manufacturers.

The provided document pertains to the 510(k) premarket notification for the "Precision Screw" device and focuses on establishing substantial equivalence to a predicate device rather than presenting a study with specific acceptance criteria and detailed performance data in the typical sense of a clinical or standalone AI performance study.

Therefore, many of the requested sections (e.g., sample size for test set, number of experts for ground truth, MRMC study, training set details) are not applicable or cannot be extracted directly from this type of regulatory submission. The document emphasizes non-clinical testing for safety and technological equivalence.

Here's an attempt to answer the questions based only on the provided text, noting where information is not available:

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

The document does not explicitly state "acceptance criteria" in a quantitative, pass/fail table format. Instead, it refers to fulfilling standards and demonstrating equivalence to a predicate device. The performance is reported in terms of meeting these standards and being "highly visible and fit for the purpose."

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Sample Size for Test Set: Not specified for any of the non-clinical tests mentioned. For the "Visual Acuity" test, it mentions "inserting Precision Screws into a Spinal Bone Model" but does not give a number of screws or models used.
• Data Provenance: Not explicitly stated. The submitter is Cryptych Pty Ltd. based in Australia. The testing standards (ASTM, ISO) are international. The location of the testing laboratories is not provided. The data is non-clinical (bench testing), not 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)

Not applicable. The tests performed are non-clinical (biocompatibility, mechanical strength, visibility on imaging models) and do not involve expert consensus on diagnoses or interpretations of patient data. For the "Visual Acuity" test, no mention of expert readers or their qualifications is provided; it states "The visibility of the Precision Screw on O-Arm images... was assessed."

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

Not applicable. This type of adjudication method is used in clinical studies involving multiple human readers for diagnostic tasks, which is not what was performed.

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

No, an MRMC comparative effectiveness study was not done. This device is a physical medical device (a screw for navigation), not an AI-powered diagnostic or assistive tool. The document states: "clinical testing was not required to support substantial equivalence."

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

No, a standalone algorithm performance study was not done. The device is a physical screw.

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

The concept of "ground truth" (in the sense of a gold standard for a diagnostic algorithm) is not directly applicable to the non-clinical tests conducted for this physical device. Instead, the "truth" is established by:

• Adherence to recognized international standards (e.g., ASTM F136 for material, ASTM F543-13 and ISO 6475:1989 for mechanical properties, ISO 10993 for biocompatibility).
• Validation against expected performance for a physical device (e.g., "Sterilization has been validated," "Screws are highly visible and fit for the purpose").

8. The sample size for the training set

Not applicable. This device is a physical medical device, not an AI or machine learning algorithm that requires a training set.

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

Not applicable, as there is no training set for a physical device.

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