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This software is intended for use by qualified research and clinical professionals with specialized training in the use of EEG and PSG recording instrumentation for the digital recording, playback, and analysis of physiological signals. Its specifications and features are especially well suited to electroencephilography, polysomnographic sleep recordings, and long term recordings used in epilepsy diagnosis.
This software is intended for use by qualified research and clinical professionals with specialized training in the use of EEG and PSG recording instrumentation for the digital recording, playback, and analysis of physiological signals. It is suitable for digital acquisition, display, comparison, analysis, and archiving of EEG potentials and other rapidly changing physiological parameters.

TWin PLUS will become the universal review package for all GRASS-Telefactor EEG systems. Further the TWin PLUS software package is intended for use with a number of hardware EEG inourterlices. It utilizes the windows operating system to manage four or more simultaneous ongoing tasks which include:

• Real-time collection and formating of real time EEG and other physiological parameters
• Real-time display of the foregoing parameters in waveform or numeric format as the data and user . may require.
• Real time analysis of EEG waveform data to identify events which may be epileptoform in nature and hence require special attention from professionals in attendance - SZAC analysis.
• Data collection and recording to disk of real time video and audio data, usually an image of the patient ● being monitored.
• Review and analysis of previously recorded data including the ability to look-back to old data while ● real-time recording of ongoing data is in progress.

The provided document describes the Grass-Telefactor TWin PLUS Software, an EEG Review Software. However, it does not contain explicit acceptance criteria or a detailed study proving the device meets specific performance metrics in the way modern medical device submissions typically do.

The document is a 510(k) premarket notification and focuses on establishing substantial equivalence to previously marketed devices rather than presenting a performance study against predefined acceptance criteria. Therefore, much of the requested information cannot be directly extracted from this document.

Here's an analysis based on the available information:

Key Takeaways from the Document:

• Substantial Equivalence: The primary argument for market clearance is that the TWin PLUS software is "functionally equivalent in design and function" to GRASS GAMMA, Telefactor BEEHIVE, and Telefactor SZAC Seizure Analysis Computer. It is described as a "composite of the three referenced substantially equivalent devices with enhanced capacity."
• No Diagnostic Claims: Crucially, the document explicitly states, "Grass-Telefactor makes no claims for diagnostic accuracy of this algorithm," and "None of the products claims to be in and of itself 'diagnostic'." Instead, it's intended to mark particular EEG passages for "diagnostic consideration" or for "professional review and classification." This significantly reduces the need for extensive diagnostic accuracy studies in the context of this 510(k) submission.
• Professional Review: The document consistently emphasizes that "All of the products are designed to be operated by trained medical professionals and the data collected is reviewed in its entirety by trained medical professionals."
• Algorithm Provenance: The "Flagging of Epileptoform EEG Data Sequences (SZAC)" module uses "the identical software algorithm developed and proven in the SZAC Computer previously marketed by Telefactor (K870450)." The "Semi Automatic Sleep Scoring System (SASSY K860219)" is also an "next iteration" of a previously cleared system. This suggests reliance on the prior clearance and established use of these specific algorithms.

Detailed Response to Requested Information (Based on Document and Inferences):

1. Table of Acceptance Criteria and Reported Device Performance

• Acceptance Criteria: The document does not define specific quantitative acceptance criteria (e.g., sensitivity, specificity, accuracy thresholds). The implicit acceptance criterion for this 510(k) is "substantial equivalence" to predicate devices and demonstrating that the device enhancements (e.g., increased channels, modern OS) do not raise new questions of safety or effectiveness.
• Reported Device Performance: The document does not report quantitative performance metrics (e.g., sensitivity, specificity, PPV, NPV) for the automated analysis features (SZAC, SASSY, apnea/desaturation/PLM marking).

2. Sample Sizes used for the Test Set and Data Provenance

• Test Set Sample Size: The document does not mention a specific test set sample size. Given the nature of a 510(k) focusing on substantial equivalence and the device's non-diagnostic claims, a formal clinical performance study with a dedicated test set exhibiting specific performance metrics would likely not have been required or performed.
• Data Provenance: Not applicable, as no specific performance study is detailed.

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

• Not applicable, as no specific performance study against a ground truth is detailed. The ground truth for the algorithms themselves (SZAC and SASSY) would have been established during their initial development and clearance (K870450, K860219), but this document doesn't provide those details. The current device's use relies on subsequent human expert review.

4. Adjudication method for the test set

• Not applicable, as no specific performance study against a ground truth is detailed.

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, a multi-reader multi-case (MRMC) comparative effectiveness study is not described or referenced in this document. The device's components (like SZAC and SASSY) are described as tools for expert review, not replacements for it.

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

• No, a standalone performance study is not described. In fact, the document disclaims standalone diagnostic performance: "Grass-Telefactor makes no claims for diagnostic accuracy of this algorithm," and "No diagnostic classification is made without a decision based on the review of a qualified professional." The device's function is explicitly described as "computer assisted" and for "professional review."

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

• The document does not describe the ground truth used for any performance evaluation of the TWin PLUS software itself. For the embedded algorithms (SZAC and SASSY), their "proven" status from prior 510(k) clearances implicitly means a ground truth was established, likely by expert interpretation of EEG/PSG data, but specifics are not in this document.

8. The sample size for the training set

• The document does not mention the sample size for a training set. The algorithms (SZAC, SASSY) were developed prior to this submission, and details about their training would be in the original K870450 and K860219 submissions if applicable.

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

• The document does not provide this information for TWin PLUS or its components. For the predicate algorithms (SZAC and SASSY), it would have been established during their original development, likely through expert annotation of physiological data.

In summary: K012976 is a 510(k) submission focused on demonstrating substantial equivalence and the integration of previously cleared functionalities into a new software platform with modern capabilities. It explicitly positions the software as a tool for trained professionals, not as an independent diagnostic device, thereby sidestepping the need for extensive de novo performance studies with defined acceptance criteria and ground truth validation for its automated features within this particular submission. The "proof" is largely based on the prior clearance and established use of the predicate devices and embedded algorithms.

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