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Use of the Mag View Biomagnetometer is indicated for the patient whose physician believes that information about the magnetic fields produced by that patient's brain and information about the sources of those magnetic fields could contribute to diagnosis or therapy planning. The intended patient populations are neonates and those children with head circumferences of 50 cm or less.

The Tristan Technologies MagView Biomagnetometer (hereinafter referred to as the "MagView") utilizes superconducting signal pickup coils and Superconducting Quantum Interference Devices (SQUIDs) to detect and amplify magnetic fields produced by electrical activity in brain. The MagView consists of a sensor unit, an electronics subsystem for preliminary amplification, filtering, and analog to digital conversion of the signals from each SOUID, an electronics rack containing power supplies to power the electronics subsystem, a computer to control the operation of the electronic subsystem and the SQUIDs and to acquire and store the signal values collected by the system.

This document is a 510(k) Premarket Notification from the FDA regarding the Tristan Technologies MagView Biomagnetometer. It does not contain information about a study proving the device meets acceptance criteria in the typical sense of a clinical trial with performance metrics like sensitivity, specificity, etc., as would be expected for an AI/CAD-driven device today.

Instead, this submission focuses on demonstrating substantial equivalence to a previously legally marketed predicate device (Magnes 2500 WH Biomagnetometer System). This means the "acceptance criteria" and "study" are geared towards showing that the new device is as safe and effective as the predicate, based on technological characteristics and non-clinical testing.

Here's a breakdown of the requested information based on the provided text, and where gaps exist because the document is a 510(k) for substantial equivalence rather than a detailed performance study:

1. Table of acceptance criteria and the reported device performance

2. Sample sized 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 applicable in the context of human subjects or clinical data. The "test set" here refers to the device itself and its components undergoing non-clinical technical testing.
• Data Provenance: The testing was "non-clinical testing of the system" using "external calibrated signal sources." This implies laboratory-based, engineering validation rather than patient data. No country of origin for patient data is mentioned as no patient data was used for performance assessment in this submission. The testing appears to be "prospective" in the sense that the device was specifically tested to demonstrate its technical performance.

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

• Number of Experts: Not applicable. Ground truth for non-clinical technical performance (like magnetic sensitivity) is established by calibrated measurement tools and engineering principles, not human expert consensus.

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

• Adjudication Method: Not applicable. There is no human interpretation or decision-making process described that would require adjudication for this type of technical performance testing.

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

• MRMC Study: No. This document describes a biomagnetometer, which is a measurement device for brain activity. It is not an AI/CAD device, and therefore, an MRMC study related to human reader performance with or without AI assistance is not relevant or described.

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

• Standalone Performance: The "non-clinical testing of the system" on its own demonstrates standalone technical performance (e.g., sensitivity, bandwidth) of the device components. There is no "algorithm" in the sense of an AI model being evaluated here. The device itself is the "standalone" entity being described.

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

• Type of Ground Truth: For the non-clinical testing, the ground truth was provided by "external calibrated signal sources." This would involve known, precisely generated magnetic fields used to verify the device's measurement accuracy and sensitivity.

8. The sample size for the training set

• Sample Size for Training Set: Not applicable. This device is a biomagnetometer, not a machine learning or AI algorithm that requires a training set of data.

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

• Ground Truth for Training Set: Not applicable, as there is no training set for this type of device.

Summary of the "Study" mentioned for meeting "Acceptance Criteria":

The "study" referenced in the document is the non-clinical performance testing of the MagView Biomagnetometer. This testing involved:

• Comparing the technological characteristics (method of operation, sensor sensitivity, bandwidth, cryogenics, output processing, etc.) of the MagView to those of the predicate device (Magnes 2500 WH).
• Conducting direct measurements on the MagView using "external calibrated signal sources" to verify its magnetic signal detection performance (specifically, its sensitivity and bandwidth).
• Ensuring compliance with relevant safety standards (IEC and CISPR).

The acceptance criteria were primarily defined by demonstrating that the MagView's technological characteristics and measured performance were either identical, equivalent, or improved compared to the predicate device, and that it met established safety standards. The conclusion was that the MagView is "as safe, as effective, and performs as well as the Magnes 2500 WH."

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The Tristan Technologies Model 621/624 Biomagnetometer is intended for use as a tool that noninvasively measures and displays the magnetic signals produced by the electric currents in the heart of human beings of any age or in the heart of a fetus in utero.

The Tristan Technologies Model 621/624 Biomagnetometer (hereinafter referred to as the "Model 621/624") utilizes superconducting signal pickup coils and Superconducting Quantum Interference Devices (SQUIDs) to detect and amplify magnetic fields produced by electrical activity in the heart. The Model 621/624 consists of a sensor unit, an electronics subsystem for preliminary amplification, filtering, and analog to digital conversion of the signals from each SQUID, an electronics rack containing power supplies to power the electronics subsystem, a computer to control the operation of the electronic subsystem and the SQUIDs and to acquire and store the signal values collected by the system.

This document is a 510(k) premarket notification for the Tristan Technologies Model 621/624 Biomagnetometer. The notification primarily focuses on establishing substantial equivalence to a predicate device rather than detailing specific acceptance criteria and a comprehensive study against them. Therefore, many of the requested sections regarding the study and ground truth will not be directly derivable from this document.

Here's an analysis based on the provided text, highlighting what is available and what is not:

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

This document does not present a table of specific, quantifiable acceptance criteria or a direct comparison of the Model 621/624's performance against such criteria. The "performance" assessment is based on demonstrating technological equivalence to a predicate device.

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 explicitly stated in terms of patient numbers. The "non-clinical test measurements" were conducted at the University of Wisconsin Department of Medical Physics.
• Data Provenance: The tests were "non-clinical" and involved "test measurements" at a university in the US (University of Wisconsin). It is unclear if these were on human subjects or phantoms, but given the context of biomagnetic signals from the heart, it implies human data. The document does not specify if the data was retrospective or prospective.

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 "ground truth" here is the measurement of magnetic signals, not a diagnostic interpretation that would require expert consensus. The study focused on comparing the device's ability to measure these signals against a predicate device's published measurements.

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

Not applicable. There was no diagnostic adjudication process involved. The comparison was based on the output signals themselves.

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. This is not an AI-based device, and no MRMC study was conducted or mentioned. The device is a "Biomagnetometer" that measures and displays magnetic signals from the heart.

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

Essentially, yes, in the sense that the "non-clinical test measurements" focused on the device's ability to generate signals. However, this is not an "algorithm-only" device in the AI sense. It's a measurement device. Its performance is its standalone capability to measure. The study aimed to show its measurements were comparable to the predicate device.

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

The "ground truth" was the expected magnetic signals produced by electrical activity in the heart, as measured by a legally marketed predicate device. The comparison was made based on published data from the predicate device. In essence, the predicate device's measurements served as the reference for technological equivalence.

8. The sample size for the training set

Not applicable. This is not a machine learning or AI device that requires a training set.

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

Not applicable, as there is no training set.

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