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The Orion LifeSpan™ MEG non-invasively measures the magnetoencephalographic (MEG) (and, optionally, electroencephalographic (EEG) signals) produced by electrically active tissue of the brain. These signals are recorded by a computerized data acquisition system, displayed, and may then be interpreted by trained physicians to help localize these active areas. The locations may then be correlated with anatomical information of the brain. MEG is routinely used to identify the locations of visual, auditory, and somatosensory in the brain when used in conjunction with evoked response averaging devices. MEG is also used to non-invasively locate regions of epileptic activity within the brain. The localization information provided by the device may be used, in conjunction with other diagnostic data, as an aid in neurosurgical planning.

It is assumed that the users of the Orion LifeSpan™ MEG are physicians or neurology laboratory technicians who have received training in the following areas:

• Hospital procedures
• Physiological monitoring of patients
• Training relevant to the specific discipline or disorder under investigation

Note: This indication for use specifically excludes use of the Orion LifeSpan™ MEG as life support equipment, for example vital signs monitoring in intensive care units.

The Orion LifeSpan™ MEG is a magnetoencephalograph (MEG) which records magnetic signals from the human brain. The Orion LifeSpan™ MEG uses the CURRY software platform (K001781) to acquire, process and display these signals. Optionally EEG can be recorded simultaneously with the MEG using an integrated SynAmps2 (K023771) hardware system.

The Orion LifeSpan™ MEG can optionally be provided with adult, child or both sized helmets.

The Orion LifeSpan™ MEG is used by skilled operators or physicians trained in the acquisition and interpretation of such signals. It is used as an adjunct as part of a range of measurements performed, such as imaging and other studies, to form a more complete picture of the patient's pathology alongside a standard clinical workup.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

Device Name: Orion LifeSpan™ MEG
Predicate Device: Elekta Neuromag

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

The document does not explicitly state a "sample size" in terms of number of patients or cases for the non-clinical performance studies. Instead, it describes:

• Empty-room noise performance & Device compatibility tests: Performed using the Orion LifeSpan™ MEG and the predicate Elekta Neuromag devices themselves in an empty, magnetically shielded room. The data provenance includes a specific measurement of the Elekta Neuromag TRIUX MEG at Swinburne University of Technology, dated November 6th, 2019. This indicates retrospective use of the predicate device's data for comparison.
• Phantom Comparison Test: An "identical phantom signal" was recorded with both systems. This implies one or more phantom measurements. Data provenance again includes the Elekta Neuromag TRIUX MEG measured at Swinburne University of Technology.
• Limited Channel Operation Test: Phantom localization testing was performed. The number of phantom trials or repetitions for this test is not specified, but it was performed at three different degrees of channel operation (95%, 90%, and 80% of full channel capacity).

There is no mention of human subject data (clinical data) in these performance studies.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of those Experts

Not applicable. The ground truth for the non-clinical tests (empty-room noise, phantom localization, device compatibility, software verification) was established by physical measurements and engineering specifications, not by human expert interpretation of medical images or signals.

4. Adjudication Method for the Test Set

Not applicable, as no human expert review or adjudication process was described for establishing ground truth in these non-clinical tests.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

No, an MRMC comparative effectiveness study was not done. The document explicitly states: "Clinical testing was not performed." The studies presented are non-clinical, comparing device performance metrics directly to a predicate device or pre-defined engineering criteria, not evaluating human reader performance with or without AI assistance.

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

The device, the Orion LifeSpan™ MEG, is a medical instrument (magnetoencephalograph/electroencephalograph with associated software) for measuring brain signals, not an AI algorithm. Therefore, the concept of a "standalone algorithm performance" study as typically understood for AI/ML devices is not directly applicable. The non-clinical studies evaluate the standalone performance of the device itself (noise levels, localization accuracy) against established benchmarks. The CURRY software, while a component, is verified for its functionalities like processing, display, and localization, which are intrinsic to the device's operation.

7. The Type of Ground Truth Used

The ground truth for the non-clinical studies was based on:

• Physical measurements and established engineering benchmarks: For noise levels (fTrms/√Hz).
• Known phantom signal locations: For localization accuracy, where the true source location in the phantom is precisely known.
• Compliance with international standards: IEC 60601-1, IEC 60601-1-2, IEC 80601-2-26, IEC 62304, ISO 14971.

8. The Sample Size for the Training Set

Not applicable. This device is a measurement instrument, not an AI/ML algorithm that requires a "training set" in the context of machine learning. The software component, CURRY, undergoes verification and validation but not "training" in the AI sense.

9. How the Ground Truth for the Training Set was Established

Not applicable, as there is no training set for an AI/ML algorithm described for this device.

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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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Use of the Artemis 123 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 location of the sources of those magnetic fields could contribute to diagnosis or therapy planning.

The Tristan Technologies Artemis 123 Biomagnetometer (hereinafter referred to as the "Artemis 123") utilizes superconducting signal pickup coils and Superconducting Quantum Interference Devices (SQUIDs) to detect and amplify magnetic fields produced by electrical activity in brain. The Artemis 123 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, and a patient table which accommodates and facilitates the optimal positioning of the head of a human being adjacent to the sensor unit.

The Tristan Technologies Artemis 123 Biomagnetometer is indicated for use for the patient whose physician believes that information about the magnetic fields produced by that patient's brain and information about the location of the sources of those magnetic fields could contribute to diagnosis or therapy planning.

The study presented focuses on demonstrating technological equivalence to a predicate device, the Magnes 2500 WH Biomagnetometer System, rather than establishing specific clinical acceptance criteria based on a disease diagnosis or therapy planning outcome. Therefore, the "acceptance criteria" and "device performance" in the context of this submission relate to these technological equivalence metrics.

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size for Test Set and Data Provenance

• Sample Size:
  • Sensitivity Test: The noise spectra of all channels (123 pickup coils) were recorded from the Artemis 123.
  • Source Localization Test: The magnetic field values were recorded for each of the Artemis 123 channels (123 pickup coils) from a phantom containing two dipolar sources.
• Data Provenance: Non-clinical tests conducted at the Children's Hospital of Philadelphia, USA. These tests were for "research use only" and the results were published in the peer-reviewed journal Frontiers Human Neuroscience on March 3, 2014. The data is retrospective in the context of this 510(k) submission, as it was published prior to the submission date.

3. Number of Experts and Qualifications for Ground Truth

This study did not involve human expert interpretation of brain magnetic field data for establishing ground truth. The "ground truth" for the non-clinical tests was established by:

• Sensitivity: The intrinsic noise characteristics of the device in an empty measurement environment, compared against the known specifications and characteristic noise form of the predicate device.
• Source Localization: The known actual locations of the two dipolar sources within the phantom. The "expertise" here lies in the precise engineering and construction of the phantom and the placement of the known sources.

4. Adjudication Method

Not applicable. This study did not involve human interpretation or subjective assessments that would require an adjudication method. The measurements were objective physical quantities (noise levels, localized source positions).

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No MRMC comparative effectiveness study was done. This submission focuses on the technological equivalence of the device's physical performance characteristics (sensitivity and source localization) compared to a predicate device, not on the impact of AI or the device on human reader performance in a clinical diagnostic setting.

6. Standalone Performance (Algorithm Only without Human-in-the-loop performance)

Yes, the performance evaluated was standalone performance of the device's physical sensing and localization capabilities.

• Sensitivity: The Artemis 123 was activated, and noise spectra were recorded and analyzed directly from its channels in an empty room.
• Source Localization: The Artemis 123 directly recorded magnetic fields from a phantom, and an algorithm fitted these values to a dipole source model to determine location, without human intervention in the interpretation process of the raw data for localization.

7. Type of Ground Truth Used

• Sensitivity: Based on the known specification (10 fT/√Hz) and characteristic noise profile of the predicate device (Magnes 2500 WH) as the comparative "ground truth."
• Source Localization: Physical ground truth established by the known and precise locations of dipolar sources within a specially constructed phantom.

8. Sample Size for the Training Set

No training set information is provided or relevant in this context. The study describes non-clinical performance evaluations of a physical device, not the training of an AI algorithm or machine learning model.

9. How Ground Truth for the Training Set was Established

Not applicable, as no training set was used.

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Use of the Magnes 2500 WH is indicated for the patient whose physician believes that information about the magnetic fields produced by that patient's brain and information about the location of the sources of those magnetic fields could contribute to diagnosis or therapy planning.

The Magnes 2500 WH biomagnetometer (hereafter "Magnes 2500 WH") comprises a magnetic sensor for detecting and measuring the magnetic fields produced by the human brain, along with the auxiliary equipment required to perform the measurements in a conventional medical facility environment and to display the results of the measurements to physicians in a variety of ways. The sensor utilizes an array of superconducting magnetic field pickup coils arranged in such a manner as to sense the magnetic fields over the portion of the skull enclosing the brain. For each such coil, a superconducting quantum interference device (SQUID) is used to detect the current induced in that coil by the brain magnetic field and produce a voltage proportional to the magnetic flux change. Conventional electronic and computer circuitry is used to amplify, filter, digitize, store and display the result of the measurement. The sensor includes an insulated reservoir of liquid helium as a refrigerant for cooling the superconducting components - pickup coils, SQUIDs, and interconnecting leads - to temperatures below their superconducting transition temperature. Heat is conducted from these superconducting elements along thermally conductive pathways into the helium reservoir. Provided as part of the Magnes 2500 WH biomagnetometer are the following ancillary items: Magnetically shielded room, comprised primarily of nickel-rich alloy and aluminum sheeting, to provide shielding from environmental sources of magnetic or ff noise. Manually operated non-magnetic gantry to place the sensor over the head of the patient in either a seated or supine position. Non-magnetic patient table with hydraulic elevation, to support the patient securely in either seated or supine position. Non-magnetic patient monitoring and communication devices, including video monitor, intercom, and head motion detector. Head shape and head position measurement system, to provide head shape and location relative to the sensor for data modeling and display. Computer workstation, operator console, and software to control system operation, data acquisition and storage, and data analysis and display. Sensory stimulus systems, to provide stimulation of the patient's somatosensory, auditory, y sumulus systems, to pro for magnetic measurement of evoked response.

The acceptance criteria and study proving the device meets them are described below. However, it's important to note that this 510(k) summary focuses on demonstrating substantial equivalence to a predicate device rather than presenting a performance study against predefined acceptance criteria for a novel device. The "acceptance criteria" here are implicitly linked to the performance of the predicate device.

1. Table of Acceptance Criteria and Reported Device Performance

performance in detecting brain magnetic fields with magnetometer coils as the predicate's gradiometer coils. | -   **Bench Test 1 (Dipolar Source in Head Phantom):** Waveforms measured with Magnes 2500 WH (magnetometer coils) and Magnes II (gradiometer coils) showed no significant difference for both normal alignment and presence of artificial magnetic noise conditions. 

• Bench Test 2 (Dipole Localization in Head Phantom): No significant difference in localized dipole parameters (physical location, strength, or orientation) between Magnes 2500 WH and Magnes II in multiple trials. |
  | - Refrigeration Method: No material difference in
  sensitivity. | - Fixed Source Sensitivity Test: Sensitivity of various channels in Magnes 2500 WH (solid thermal conduction) showed no material difference when the helium reservoir was full versus nearly empty. This was compared to a similar test on a Magnes II (direct immersion). |
  | - Head Position Measurement: No significant difference
  in relative location of reference points. | - Head Position Measurement Test: Repetitive measurements of reference points on a head phantom using the Magnes 2500 WH's new positioning system and the Magnes II's system showed no significant difference in the relative locations of the reference points. |

Study Details:

Since this is a 510(k) for substantial equivalence, the "study" described is a series of non-clinical comparative tests.

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size: Not explicitly stated in terms of number of patients or distinct head phantom configurations. The tests involved:
  • A head phantom with a dipolar source.
  • A fixed magnetic source.
  • A head phantom for position measurement.
• Data Provenance: The tests were non-clinical bench tests conducted by the manufacturer, Biomagnetic Technologies, Inc. This implies a controlled laboratory environment. The country of origin of the data is therefore the USA (where the company is based). The data is prospective in the sense that these tests were performed specifically for this 510(k) submission.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

• Not applicable (N/A). For these non-clinical bench tests demonstrating substantial equivalence, the "ground truth" was established by the physical setup of the experiments (e.g., a known dipolar source in a head phantom, a fixed magnetic source, reference points on a head phantom). The comparison was against the performance of a predicate device, not against expert interpretation of a complex clinical scenario.

4. Adjudication Method for the Test Set

• Not applicable (N/A). There was no human interpretation or adjudication required for the output of these technical performance tests. The comparison was based on quantitative measurements and waveform analysis.

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 510(k) pertains to a non-AI medical device (a biomagnetometer) and focuses on substantial equivalence based on technological characteristics, not on the impact of AI assistance on human reader performance.

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

• Yes, in essence, standalone performance was assessed. The tests directly evaluated the physical performance of the Magnes 2500 WH hardware and its signal processing against the Magnes II. This is a "device-only" evaluation in a controlled setting, which can be considered analogous to standalone performance for a software algorithm, though it's for hardware.

7. The Type of Ground Truth Used

• The "ground truth" in these comparative non-clinical tests was established by:
  • Known physical parameters: The characteristics of the dipolar source in the head phantom (strength, location, orientation).
  • Known physical configuration: The fixed magnetic source and reference points on the head phantom.
  • Comparative measurement: The performance of the predicate device (Magnes II) served as the benchmark for "truthfulness" in some comparisons.

8. The Sample Size for the Training Set

• Not applicable (N/A). This device is not an AI/ML algorithm that requires a training set. It is a measurement device where its operation is based on fundamental physics and engineering principles, not learned patterns from data.

9. How the Ground Truth for the Training Set was Established

• Not applicable (N/A). As stated above, there is no training set for this type of device.

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The Neuromag-122 system is intended for use as a magneto encephalographic (MEG) device which non-invasively detects and displays biomagnetic signals produced by electrically active nerve tissue in the brain. When interpreted by a trained clinician, the data enhances the diagnostic capability by providing useful information about the location relative to brain anatomy of active nerve tissue responsible for critical brain functions.

This device integrates 122 SQUID planar gradiometers with medical super-computers and data intracelly long in order to measure the differences in the magnetic signals generated by the intracellular dendritic currents. These detectors are positioned in a helmet shaped array which gives the user the ability to record the electrical activity of the entire surface of the brain simultaneously without having to move the position of the probe.

The provided document is a 510(k) summary for the Neuromag-122 device, establishing its substantial equivalence to a predicate device (Biomagnetic Technologies Magnes Single, K901215A), rather than demonstrating direct evidence of meeting specific acceptance criteria through a clinical study. Therefore, a direct response to some of the questions, such as detailed acceptance criteria and a study proving they are met, cannot be fully provided from the given text.

However, based on the information provided, here's what can be extracted and inferred:

1. Table of acceptance criteria and the reported device performance

The document does not define explicit "acceptance criteria" in the format of specific thresholds for performance metrics. Instead, it argues for "substantial equivalence" based on comparative parameters between the Neuromag-122 and the predicate device. The implicit acceptance criterion is that the Neuromag-122 performs at least as well as, or is sufficiently similar in its technical characteristics and intended use to, the predicate device to be considered safe and effective.

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

The document does not describe a test set or clinical study with a specific sample size. The substantial equivalence argument relies on comparing the technical specifications and intended use of the Neuromag-122 to a legally marketed predicate device. No patient data, therefore, is mentioned for any "test set".

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. As noted above, no clinical "test set" and corresponding ground truth establishment are described in this document. The "ground truth" for the device's function is the established performance and safety of the predicate device.

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

Not applicable. No clinical test set or adjudication method is described.

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

Not applicable. This document is for a medical device (MEG system) and not an AI or imaging diagnostic software that would typically undergo an MRMC study.

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

Not applicable. The Neuromag-122 is a hardware system for detecting biomagnetic signals, not a standalone algorithm. Its intended use explicitly states that the data needs to be "interpreted by a trained clinician."

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

The document implicitly uses the established safety and effectiveness of the predicate device (Biomagnetic Technologies Magnes Single, K901215A) as its "ground truth" or standard for comparison. The regulatory review for substantial equivalence primarily relies on a comparison of technological characteristics, intended use, and existing performance data of the predicate device.

8. The sample size for the training set

Not applicable. This document does not describe a machine learning algorithm that would require a training set.

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

Not applicable. As above, no training set or its ground truth establishment is mentioned.

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