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MUSE CV is a large capacity client server based computer system that accesses, stores, and manages cardiovascular information. The information can consist of measurements, text, digitized waveforms and angiographic images.

MUSE CV is intended to be used in a hospital environment by trained operators.

MUSE CV is designed for network compatibility to facilitate retrieval and distribution of cardiovascular information.

MUSE CV is designed to interface with other hospital information systems through various communication protocols to support information continuity and results reporting.

MUSE CV can provide serial comparison of cardiovascular information to facilitate review of current and previous records.

MUSE CV can provide serial trending of cardiovascular information to facilitate review of current and previous records.

Through integration with Accusketch (CardioTrace), MUSE aids the physician or trained technologist in providing and documenting an objective quantification of coronary artery stenosis and measurement and quantification of left ventricular function. Also provided is the ability to digitize and store video images and the ability to interactively annotate and report current and post procedural patient cardiac status.

Use of MUSE CV is intended for accessing, storage and management of both adult and pediatric cardiovascular information.

MUSE CV is a large capacity client server based computer system that accesses, stores, and manages cardiovascular information. The information can consist of measurements, text, digitized waveforms and angiographic images.

The provided text does not contain specific acceptance criteria or a detailed study proving device performance against such criteria for the MUSE Cardiovascular Information System. It focuses on the regulatory submission, intended use, and general safety and effectiveness statement, rather than quantifiable performance metrics.

However, based on the information provided, here's what can be inferred or stated about the study and performance:

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not explicitly state quantitative acceptance criteria for the device's performance (e.g., specific accuracy thresholds for measurements, speed metrics, etc.). Instead, it focuses on qualitative measures and equivalence to predicate devices.

2. Sample size used for the test set and the data provenance:

• The document does not specify a sample size for any test set (e.g., number of patient cases, ECGs, angiograms).
• The document does not specify the country of origin of the data or whether it was retrospective or prospective.

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

• The document does not specify the number of experts or their qualifications used to establish ground truth for any test set.

4. Adjudication method for the test set:

• The document does not mention any specific adjudication method for a test set.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and what was the effect size of how much human readers improve with AI vs without AI assistance:

• The document describes the device as a "Cardiovascular Information System" that "aids the physician or trained technologist in providing and documenting an objective quantification of coronary artery stenosis and measurement and quantification of left ventricular function." It explicitly mentions that it "employs the same functional technology as the predicate devices" with "technological improvements made by manufacturers with respect to speed, performance and reliability."
• There is no mention of a multi-reader multi-case (MRMC) comparative effectiveness study.
• There is no mention of AI or an effect size of how much human readers improve with AI vs without AI assistance. This device predates widespread AI integration in medical devices.

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

• The document describes the device as a system that "aids the physician or trained technologist," implying a human-in-the-loop design.
• There is no mention of a standalone (algorithm only) performance study.

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

• The document does not explicitly state the type of ground truth used for any evaluations. Given the nature of the system (information management and quantification aid), it's likely that the "objective quantification" refers to established clinical methods and expert interpretation, but this is not detailed.

8. The sample size for the training set:

• The document does not mention a training set or its sample size. This is consistent with the device being an "information system" and not an AI/ML-based diagnostic device in the modern sense. The "testing on unit level," "software and hardware testing," and "system integration testing" suggest traditional software development and validation rather than machine learning model training.

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 ground truth for a training set was established.

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The MUSE CV is a large capacity client server based computer system that accesses, stores and manages cardiovascular information. The information can consist of measurements, text, and digitized waveforms. MUSE CV is intended to be used in a hospital environment by trained operators. MUSE CV is designed for network compatibility and interfaces with other hospital information systems through various communication protocols. MUSE CV provides the ability to serially compare/trend cardiovascular information. Use of MUSE CV is intended for accessing, storage and management of both adult and pediatric cardiovascular information.

MUSE CV is a large capacity client server based computer system that accesses, stores and manages cardiovascular information. The information can consist of measurements, text, and digitized waveforms.

This document describes a 510(k) premarket notification for the "MUSE Cardiovascular Information System". It's important to note that this submission does not contain acceptance criteria or performance study results in the typical sense of evaluating an AI/ML device.

The document states that the MUSE CV system employs the same functional technology as predicate devices, with improvements in "speed, performance and reliability." It also claims compliance with "voluntary standards as detailed in Section 9 of this submission," but Section 9 is not provided in the given text.

The "performance" section mentions quality assurance measures applied during development, but these are general development practices and not specific study results or acceptance criteria for a device performance claim like accuracy, sensitivity, or specificity.

Therefore, many of the requested fields cannot be filled from the provided text because the submission focuses on substantial equivalence to predicate devices based on functional technology and general quality assurance, rather than detailed performance metrics of a novel AI/ML algorithm.

Here's a breakdown based on the information available:

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

   • Acceptance Criteria: Not explicitly stated as quantifiable performance metrics (e.g., accuracy, sensitivity, specificity). The criteria appear to be compliance with voluntary standards and demonstrating that the device is "as safe, as effective, and performs as well as the predicate devices."
   • Reported Device Performance: The document only states that "The results of these measurements demonstrated that MUSE CV is as safe, as effective, and performs as well as the predicate devices." No specific quantitative performance values are provided.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):

   • Not specified. There is no mention of a traditional "test set" or clinical study data.

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):

   • Not applicable. No ground truth establishment process is described as there's no clinical performance study detailed.

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

   • Not applicable. No test set or expert adjudication is described.

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 type of study is not mentioned. The device's primary function is described as storing, managing, and facilitating serial comparison/trending of cardiovascular information, not as an AI-assisted diagnostic tool for human readers.

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

   • Not applicable in the typical AI sense. The device is a "Cardiovascular Information System" for data management, not a standalone diagnostic algorithm. While it performs "serial comparison" and "serial trending," its performance is evaluated against predicate devices based on a broader system functionality, not specific diagnostic accuracy.

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

   • Not applicable. No ground truth for performance evaluation is described.

8. The sample size for the training set:

   • Not applicable. This is not an AI/ML device that requires a training set in the typical sense.

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

   • Not applicable. As above, no training set is mentioned.

Summary based on available information:

The provided document is a 510(k) summary focused on demonstrating substantial equivalence of the "MUSE Cardiovascular Information System" to existing predicate devices. It emphasizes functional and technological similarity, as well as adherence to general quality assurance and voluntary standards. It does not detail specific performance studies with quantitative metrics, test sets, or ground truth establishment typically associated with the evaluation of AI/ML diagnostic or assistive devices.

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