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The MUSE Cardiology Information System is intended to store, access and manage cardiovascular information on adult and pediatric patients. The information consists of measurements, text, and digitized waveforms. The MUSE Cardiology Information System provides the ability to review and edit electrocardiographic procedures on screen, through the use of reviewing, measuring, and editing tools including ECG serial comparison. The MUSE Cardiology Information System is intended to be used under the direct supervision of a licensed healthcare practitioner, by trained operators in a hospital or facility providing patient care. The MUSE Cardiology Information System is not intended for primary monitoring. The MUSE Cardiology Information System is not intended for pediatric serial comparison.

The MUSE Cardiology Information System is a network PC-based system comprised of a client workstation/server configuration that manages adult and pediatric diagnostic cardiology data by providing centralized storage and ready access to a wide range of data/reports (e.g. Resting ECG, Stress, Holter, HiRes) from GE and non-GE diagnostic and monitoring equipment.
The device provides the ability
To review and edit stored data consisting of measurements, text, and digitized waveforms on screen, through the use of reviewing, measuring, and editing tools including ECG serial comparison.
To generate formatted management reports, ad-hoc database search reports and clinical patient reports on selected stored data.
This modification will provide the capability to generate median waveforms and ECG measurements from 12-lead ECG data received in a GE specified XML format. Other added functionality includes an additional QTc calculation method, a refined tool (Interval Editor) to manually measure, review, and document ECG waveform parameters, and workflow enhancements.

The provided document, K072502, a Special 510(k) Premarket Notification for the GE Healthcare MUSE Cardiology Information System, focuses on demonstrating substantial equivalence to a predicate device (K050614) after a modification. This type of submission typically emphasizes non-clinical testing and conformity to standards rather than detailed clinical study data with specific acceptance criteria for performance metrics.

Based on the provided text, the document states: "Based on the results of the performed testing it is concluded that the MUSE Cardiology Information System with the modifications subject to this submission is substantially equivalent to and is as safe, as effective, and performs as well as the currently marketed predicate device cleared in K050614."

This indicates that the "acceptance criteria" for this specific submission is primarily demonstration of substantial equivalence through non-clinical testing and adherence to quality assurance measures, rather than meeting specific quantifiable performance thresholds for clinical accuracy or efficacy.

Therefore, a table of acceptance criteria and reported device performance in the clinical sense is not explicitly provided in this document as it would be in an original 510(k) or PMA that introduces a novel analytical capability. The focus is on the impact of the modification to an already cleared device.

However, I can extract information related to the "study" (testing) that proves the device meets the implicit criterion of substantial equivalence.

1. A table of acceptance criteria and the reported device performance

As noted above, explicit quantitative acceptance criteria for clinical performance (e.g., sensitivity, specificity, accuracy for a specific disease detection) and corresponding reported performance are not provided in this 510(k) summary. The acceptance criterion for this modification is largely qualitative: proving the modified device is as safe, effective, and performs as well as the predicate.

The document lists the following quality assurance measures and performance testing as the "study" demonstrating this:

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 primarily describes non-clinical testing (risk analysis, requirements reviews, design reviews, integration testing, final acceptance testing, performance testing). It does not mention a clinical test set or sample size in the context of clinical performance evaluation. Therefore, information regarding data provenance (country of origin, retrospective/prospective) for a clinical test set is also not provided.

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)

Given that no clinical test set is described, there is no mention of experts used to establish ground truth for such a set in this document.

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

Since no clinical test set is described, no adjudication method for a clinical test set is mentioned.

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 MRMC comparative effectiveness study was mentioned or performed according to this document. The submission is for a modification of an existing system, not the introduction of AI assistance.

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

The document does not describe the evaluation of a standalone algorithm's performance in the context of clinical diagnostic accuracy. The testing mentioned (integration, acceptance, performance testing) refers to the functionality of the system modifications, not a new diagnostic algorithm. The device itself is described as an "ECG Analysis Computer," implying an algorithm component, but its standalone diagnostic performance is not the subject of the testing reported here.

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

Given the focus on non-clinical testing for system modifications, no specific type of clinical ground truth (like expert consensus, pathology, or outcomes data) is mentioned as being used for performance evaluation in this document.

8. The sample size for the training set

The document describes non-clinical testing for a system modification. There is no mention of a "training set" in the context of machine learning or algorithm development.

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

As no training set is mentioned (see point 8), there is no information on how its ground truth would have been established.

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