(110 days)
PreOp is intended for use by a trained/qualified EEG technologist or physician on both adult and pediativ subjects at least 3 years of age for the visualization of human brain function by fusing a variety of EEG information with rendered images of an individualized head model and an individualized MRI image.
PreOp is medical device software that combines EEG data and MR images to visualize recorded EEG activity in 3D in the brain. PreOp can be subdivided in 3 main modules: 3D Electrical Source Imaging (i.e. 3D ESI), Report generation and Viewer generation. The device's input is the MRI and EEG data that are uploaded by the user to the PreOp cloud environment. The output of the device is a report containing the results of the visualization and the ability to evaluate the results in 3D using the 3D viewer. The user can access the output through the PreOp cloud environment.
Here's a breakdown of the acceptance criteria and the study details for the PreOp device, based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
The document does not explicitly state quantitative acceptance criteria in a dedicated section. Instead, the acceptance is based on demonstrating "substantial equivalence" to a predicate device, particularly in source localization performance.
| Acceptance Criterion (Implicit) | Reported Device Performance (PreOp vs. Predicate) |
|---|---|
| Source Localization Equivalence (Study 1): The PreOp algorithms should be substantially equivalent to the predicate device algorithm in terms of source localization accuracy for epileptic spikes. | "The results demonstrated that the proposed PreOp algorithms were substantially equivalent to the predicate device algorithm" based on concordance ratings by three experienced epileptologists on a sublobular level. This comparison was between sLORETA with FDM using individualized anatomical MRI (PreOp) and sLORETA with FDM using idealized anatomical MRI (predicate). |
| Source Localization Consistency (Study 2): Performance of spike source localization should be consistent between HD-EEG and LD-EEG recordings within PreOp. | In 13 epileptic spikes across 8 patients, both algorithms (HD-EEG vs. LD-EEG within PreOp) provided identical source locations. In only 3 spikes, the localization was not 100% equivalent but "very close to each other." |
| Clinical Usability: The device should meet usability requirements. | "Usability validation is part of the Clinical Performance data and PreOp was tested and meets the requirements of following standard: AAMI/ANSI/IEC 62366:2007, Medical devices - Application of usability engineering to medical devices." |
| Software Verification and Validation: The software should be fit for clinical use and meet relevant standards. | "Validation testing involved algorithm testing which validated the accuracy of PreOp. The product was deemed fit for clinical use." "PreOp was designed and developed as recommended by FDA’s Guidance, 'Guidance for the Content of Premarket Submissions for Software Contained in Medical Device'." "According to AAMI/ANSI/IEC 62304 Standard, PreOp safety classification has been set to Class B." |
2. Sample Size Used for the Test Set and Data Provenance
- Study 1 (Source Localization Equivalence):
- Sample Size: 18 epilepsy subjects.
- Data Provenance: Retrospective data analysis. Country of origin is not explicitly stated, but given Epilog is based in Belgium, it's likely European or a mix.
- Study 2 (Spike Source Localization Consistency):
- Sample Size: Data from 8 patients, evaluating 16 epileptic spikes (13 identical + 3 very close).
- Data Provenance: Not explicitly stated as retrospective or prospective, but likely retrospective. Country of origin is not specified.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications
- Study 1:
- Number of Experts: Three experienced epileptologists.
- Qualifications: "Experienced epileptologists." Specific years of experience are not provided.
- Study 2: Not applicable in the same way, as this study was comparing internal algorithm performance rather than having experts establish a new ground truth based on device output.
4. Adjudication Method for the Test Set
- Study 1: The document states that the three experienced epileptologists "were asked to rate whether each of the algorithm solutions (sLORETA with the finite difference model [FDM] using an idealized or individualized anatomical MRI) were concordant on a sublobular level." The specific method for consolidating these ratings (e.g., 2 out of 3 agreement) is not detailed, but it implies a consensus-based approach for determining "substantial equivalence."
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done, What was the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance
No, a traditional MRMC comparative effectiveness study evaluating human reader improvement with AI assistance was not performed. The study focused on comparing the performance of the device's algorithms against a predicate device's algorithms (Study 1) or comparing different internal algorithm configurations (Study 2), with human experts acting as adjudicators for the output in Study 1.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was Done
Yes, the studies primarily assess the standalone performance of the algorithms.
- Study 1 directly compares "the source localization accuracy of the PreOp software algorithms" to "the predicate algorithm." Human experts then rated the concordance of these algorithm outputs with post-operative reports. This is a standalone comparison.
- Study 2 compares "the performance of spike source localization using HD-EEG recordings... and Low Density LD-EEG recordings" within the PreOp algorithms themselves. This is also a standalone assessment.
7. The Type of Ground Truth Used
- Study 1 (Source Localization Equivalence):
- Ground Truth: Clinical outcomes data combined with expert consensus. Specifically, the resected zone (operative data) for subjects who were Engel I postoperatively (favorable outcome) was used as the reference. The "summaries of the postoperative reports" were provided to the epileptologists, who then rated the concordance of the algorithm solutions with this clinical ground truth.
- Study 2 (Spike Source Localization Consistency):
- Ground Truth: The "ground truth" here is internal consistency of the PreOp algorithm itself under different input conditions (HD-EEG vs. LD-EEG). There isn't an external ground truth like pathology for this specific study; instead, it verifies the consistency of the algorithm's output.
8. The Sample Size for the Training Set
The document does not provide information on the sample size for the training set used to develop the PreOp algorithms. The studies described are validation studies (test sets) for the already developed software.
9. How the Ground Truth for the Training Set Was Established
Since the document does not mention the training set size, it also does not detail how the ground truth for any training set was established.
§ 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).