(121 days)
The software is intended for use by a trained/qualified EEG technologist or physician on both adult and pediatric subjects at least 16 years of age for the visualization of human brain function by fusing a variety of EEG information with rendered images of an idealized head model and an idealized MRI image.
Sourcerer is an EEG source localization software that uses EEG and MRI-derived information to estimate and visualize cortex projections of human brain activity. Sourcerer is designed in a client-server model wherein the server components integrate directly with FLOW - BEL's software. Inverse source projections are computed on the server using EEG and MRI data from FLOW using the Electro-magnetic Inverse Module (EMIM API). The inverse results are interactively visualized in the Chrome browser running on the client computer using the Electro-magnetic Functional Anatomy Viewer (EMFAV).
Here's an analysis of the provided text to extract the acceptance criteria and study details:
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Algorithmic Testing (HexaFEM) | |
| Consistency with analytical solutions for three-layer spherical model | HexaFEM solutions are consistent with the analytical solutions for the three-layer spherical model. |
| Consistency with FDM solutions for a realistic head model using the same conductivity values | HexaFEM and FDM solutions are the same for one realistic head model using the same conductivity values. |
| Algorithmic Testing (Inverse Model - EMIM Module) | |
| LORETA: Localization error distance similar to reported values by its creator. | Average localization error is about 7 mm, similar to what is reported for LORETA from its creator. |
| sLORETA: Exact source estimation results for simulated signal sources, replicating creator's reported results. | Source estimation results are exact for the simulated signal sources, fully replicating simulated results reported by sLORETA's creator. |
| MSP: Zero localization error for simulated signal sources. | Shows 100% (zero localization error), as expected. |
| Clinical Performance Testing | |
| Performance of Sourcerer to be equivalent to GeoSource (Predicate Device). | Performance of Sourcerer was shown to be equivalent to GeoSource (comparison based on Euclidian distance between maximal amplitude location and resected boundary in epileptic patients). |
| Software Verification and Validation Testing | |
| Accuracy of Sourcerer validated through algorithm testing. | Algorithm testing validated the accuracy of Sourcerer. Product deemed fit for clinical use. |
| Developed according to FDA's "Guidance for the Content of Premarket Submissions for Software Contained in Medical Device". | Sourcerer was designed and developed as recommended by the FDA guidance. |
| Safety classification set to Class B according to AAMI/ANSI/IEC 62304 Standard. | Sourcerer safety classification set to Class B. |
| "Basic Documentation Level" applied. | "Basic Documentation Level" applied to this device. |
2. Sample size used for the test set and the data provenance
The text explicitly mentions:
- Clinical Performance Testing: "The clinical data used in the evaluation is obtained from epileptic patients during standard presurgical evaluation." The sample size for the clinical test set is not explicitly stated as a number, but rather as "each patient's pre-operative hdEEG recording." It's implied there were multiple patients, but the exact count is missing.
- Data Provenance: The clinical data is retrospective ("obtained from epileptic patients during standard presurgical evaluation") and appears to be from a clinical setting, presumably within the country of origin of the device manufacturer (USA, as indicated by the FDA submission).
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
- Clinical Performance Testing Ground Truth: The ground truth for the clinical test set was established by:
- Resected region (from MRI): This implies surgical and pathological confirmation of the epileptic zone, which would typically involve neurosurgeons and neuropathologists.
- Clinical outcome: This refers to the patient's post-surgical seizure control, indicating the success of the resection.
No specific number of experts or their qualifications (e.g., number of years of experience) are provided in the document.
4. Adjudication method for the test set
The document does not explicitly describe an adjudication method for establishing ground truth, such as 2+1 or 3+1. The ground truth for the clinical performance testing relied on the "resected region (from MRI)" and "clinical outcome," which are objective clinical findings rather than subjective expert interpretations requiring adjudication.
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
There is no mention of a multi-reader multi-case (MRMC) comparative effectiveness study. The clinical performance testing compared the device's output (Electrical Source Imaging - ESI) to the predicate device (GeoSource) and the ground truth (resected region, clinical outcome), not improved human reader performance with AI assistance.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Yes, extensive standalone (algorithm only) performance testing was done:
- Algorithmic Testing of HexaFEM: Compared HexaFEM solutions to analytical solutions and FDM solutions.
- Algorithmic Testing of Inverse Model (EMIM Module): Tested LORETA, sLORETA, and MSP solvers using "test files with known signal sources." This involved comparing the algorithm's estimated source generator to the known (simulated) source.
7. The type of ground truth used
- Algorithmic Testing (HexaFEM):
- Mathematical/Analytical Ground Truth: Comparison with "analytical solutions for the three-layer spherical model."
- Comparative Ground Truth: Comparison with "FDM solutions for one realistic head model."
- Algorithmic Testing (Inverse Model - EMIM Module):
- Simulated/Known Ground Truth: "known signal sources" from forward projections were used as ground truth for "recovering the source generator (known)."
- Clinical Performance Testing:
- Outcomes Data/Pathology/Clinical Consensus: "resected region (from MRI)" and "clinical outcome" were used to establish the ground truth for epileptic focus localization.
8. The sample size for the training set
The document does not specify the sample size for the training set. It focuses on verification and validation, but not the training of the underlying algorithms.
9. How the ground truth for the training set was established
Since the document does not specify the training set, it does not describe how its ground truth was established. The ground truth description is primarily for the test/validation sets.
§ 882.1400 Electroencephalograph.
(a)
Identification. An electroencephalograph is a device used to measure and record the electrical activity of the patient's brain obtained by placing two or more electrodes on the head.(b)
Classification. Class II (performance standards).