(37 days)
The microTargeting™ Guideline 4000 5.0 System is intended to record and stimulate electrophysiological activity, as well as aid in the accurate placement of electrodes and other instruments.
The microTargeting™ Guideline 4000™ 5.0 System is an electrophysiological recording and stimulation system designed primarily for functional neurosurgery procedures. The system provides high quality and research-grade recordings on both microelectrodes and macroelectrodes, including low-frequency signals on externalized DBS leads, improved signal quality, decreased noise susceptibility, improved user experience with redesigned software, new intraoperative data analysis options, and an updated user interface to support touch screens and multiple high-resolution monitors. Additionally, it provides simultaneous constant-current and constant-voltage stimulation capabilities that include multiple weighted sources and return paths as well as complex and arbitrary stimulation waveforms. The software provides an intuitive patient management system, as well as advanced visualization and analysis methods for both single-unit recording and low-frequency signals (LFP, SEEG etc.). A fully configured system consists of a Core Module, a notebook PC, an 8-channel UE interface unit, a remote, an optional second 8-channel LF interface and an optional synchronization unit.
The provided document is a 510(k) Premarket Notification for the "microTargeting™ Guideline 4000 5.0 System." This document focuses on demonstrating substantial equivalence to a predicate device, as required for certain medical devices by the FDA. It does not include the information typically found in a study proving a device meets acceptance criteria for an AI/ML-driven diagnostic or assistive device (e.g., performance metrics like sensitivity, specificity, AUC, human-in-the-loop studies, ground truth establishment, or sample sizes for deep learning models).
The document is for an electrophysiological recording and stimulation system for neurosurgical procedures, not an AI/ML-driven device or an imaging diagnostic device. Therefore, the details requested (e.g., acceptance criteria for AI diagnostic performance, sample size for test sets, number of experts for ground truth, MRMC studies) are not applicable to the content of this specific submission.
The "acceptance criteria" discussed in the document are related to electrical safety, mechanical integrity, macro/micro stimulation accuracy, and software regression testing for an electrophysiological device, comparing its performance to that of a previous version of the device (the predicate device).
However, I will extract and present the information available that is conceptually similar to what your questions are asking for, but with the understanding that it pertains to a different type of device and different evaluation methodology than what your questions imply.
Here's an attempt to answer your questions based on the provided text, while making it clear where the information is not relevant or not present due to the nature of the device and submission:
Device: microTargeting™ Guideline 4000™ 5.0 System
Intended Use: To record and stimulate electrophysiological activity, as well as aid in the accurate placement of electrodes and other instruments during functional neurosurgical procedures.
1. A table of acceptance criteria and the reported device performance:
The document describes nonclinical performance data for the device, and the "acceptance criteria" are implied by the "Results" section for each test, indicating that the device "met the acceptance criteria."
| Test | Test Method Summary | Acceptance Criteria (Implied by Results) & Reported Device Performance |
|---|---|---|
| Electrical Safety | Electrical Safety consistent with IEC 60601 (Class 1 ME Equipment). Internal Testing based on IEC 60601 protocols. 60601-1 Electrical Safety Testing Performed Externally by 3rd Party entity. | Acceptance Criteria: Passing the acceptance criteria necessary for establishing basic safety per IEC 60601. |
| Reported Performance: Both internally and externally performed electrical safety testing passed the acceptance criteria. Substantive equivalence to predicate established. | ||
| Mechanical Integrity | Mechanical Strength and Integrity testing consistent with standard IEC 60601. Internal Testing based on IEC 60601 protocols. 60601-1 Mechanical Strength Testing Performed Externally by 3rd Party entity. | Acceptance Criteria: Passing the acceptance criteria necessary for establishing basic safety per IEC 60601. |
| Reported Performance: Both internally and externally performed mechanical strength testing passed the acceptance criteria. Substantive equivalence to predicate established. | ||
| Macro Stimulation | Internally generated protocol assessing three key parameters: accurate output frequency, accurate pulse duration, and accurate stimulus amplitude. Measured using a NIST traceable oscilloscope with precision resistive loads as microelectrode analogs. Each parameter adjusted via software user interface. | Acceptance Criteria: Accuracy of stimulator outputs to within ±10% of the User Interface set point for frequency, pulse duration, and amplitude. |
| Reported Performance: The subject devices met the acceptance criteria. Substantive equivalence to predicate established. | ||
| Micro Stimulation | Same test protocol as Macro Stimulation, but for lower stimulus amplitude range. | Acceptance Criteria: Accuracy of stimulator outputs to within ±10% of the User Interface set point for frequency, pulse duration, and amplitude. |
| Reported Performance: The subject devices met the acceptance criteria. Substantive equivalence to predicate established. | ||
| Software Regression Testing | Internally created protocol based on workflow established in the Usability specification of the predicate device. Performed iteratively at each software release per IEC 62304. All major and minor software functions tested. Bugs fixed since previous round assessed for effectiveness and risk. | Acceptance Criteria: All major areas of software functionality confirmed, and no remaining bugs with a risk level greater than "Acceptable" as defined by risk management plan. |
| Reported Performance: All major areas of software functionality were confirmed for the subject device. No remaining bugs had a risk level of greater than Acceptable. Substantive equivalence to predicate established. |
2. Sample size used for the test set and the data provenance:
- Sample Size: Not specified in terms of "test set" samples (e.g., patient cases) as this is not a diagnostic AI/ML device. The tests described are device-level verification and validation (e.g., electrical safety, mechanical, stimulation accuracy) conducted on the physical device and its software. The number of units tested is not stated but standard V&V would involve a representative sample of manufactured units.
- Data Provenance: Not applicable in the context of patient data sets. The testing is based on internal protocols, external third-party testing (for electrical safety and mechanical strength), and comparison to a legally marketed predicate device (FHC, Inc.'s microTargeting™ Guideline 4000).
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Not Applicable. This is not a device that relies on expert interpretation of data (e.g., images, waveforms) to establish a "ground truth" for diagnostic accuracy in the way an AI/ML-driven device would. The "ground truth" for this device's performance is objectively measured against established engineering and safety standards (e.g., IEC 60601) and specifications for electrical outputs.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:
- Not Applicable. As there are no human interpretations of patient data involved in establishing "ground truth" for the tests described, no adjudication method is relevant.
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 device is an electrophysiological recording and stimulation system, not an AI-assisted diagnostic tool. Therefore, MRMC studies involving human readers are not applicable to its evaluation.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
- Not Applicable / Interpreted Differently. While the device itself operates "stand-alone" in its core functions (e.g., generating stimuli, recording signals), there is no 'algorithm-only' performance test in the diagnostic sense. The software functions were tested through "Software regression testing," which assesses the functionality and correctness of the software components. The device is intended for use by a neurosurgeon, neurologist, or clinical neurophysiologist, meaning there is always a human-in-the-loop controlling its operation and interpreting its outputs in a clinical context.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc):
- The "ground truth" for evaluating this device's performance would be the expected engineering specifications and international safety standards. For example:
- For electrical safety: Compliance with IEC 60601-1.
- For mechanical integrity: Compliance with IEC 60601-1.
- For stimulation accuracy: Output frequency, pulse duration, and amplitude within ±10% of the user interface set point, as measured by calibrated equipment (NIST traceable oscilloscope).
- For software: Functionality confirmed against use-case specifications and risk assessment ensuring no unacceptable risks.
8. The sample size for the training set:
- Not Applicable. This is not an AI/ML device that requires a "training set" of data for learning.
9. How the ground truth for the training set was established:
- Not Applicable. As there is no training set for an AI/ML model, the concept of establishing ground truth for it does not apply.
§ 882.1330 Depth electrode.
(a)
Identification. A depth electrode is an electrode used for temporary stimulation of, or recording electrical signals at, subsurface levels of the brain.(b)
Classification. Class II (performance standards).