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The CardioInsight Cardiac Mapping System is intended for acquisition, analysis, display and storage of cardiac electrophysiological data and maps for analysis by a physician.

The CardioInsight Cardiac Mapping System is a non-invasive mapping system for beat-by-beat, multi-chamber, 3D mapping of the heart. The CardioInsight Cardiac Mapping System displays cardiac maps and virtual electrograms from real-time chest ECG signals (measured by a Sensor Array placed on the torso) and CT scan data. The ECG signals in concert with the CT scan information (geometrical information) are used in mathematical algorithms to transform the measured body surface signals into epicardial signals via solving the cardiac inverse problem. The CardioInsight Cardiac Mapping System software uses this data to provide various cardiac signal analyses and displays interactive 3D color maps including potential, activation, voltage, propagation, and phase maps. The CardioInsight Cardiac Mapping System is mobile and can be used for mapping at the patient's bedside or in the EP lab.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The provided 510(k) summary does not explicitly state quantitative acceptance criteria or a direct comparison of the proposed device's performance against such criteria. Instead, it details that the device underwent various verification and validation tests to ensure it meets design inputs and performs as intended, and that it is "substantially equivalent" to its predicate device. The performance data section describes the types of testing performed rather than specific numerical results or acceptance thresholds.

2. Sample Size Used for the Test Set and Data Provenance

The document does not explicitly state the sample size used for the test set or the data provenance (e.g., country of origin, retrospective/prospective). The testing described is primarily focused on software and system verification and validation in simulated environments, bench testing, and usability testing. There is no mention of clinical data or patient-specific test sets in this summary.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

This information is not provided. Given that the testing described is primarily technical and simulated, the concept of "ground truth established by experts" in a clinical context isn't directly applicable for all tests mentioned. Usability testing would involve users, but their qualifications are not detailed.

4. Adjudication Method for the Test Set

This information is not provided. As mentioned above, the testing appears to be primarily technical validation rather than clinical adjudication of results against a gold standard.

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 study is mentioned in the provided text. The device is a cardiac mapping system, and the changes are related to software enhancements, algorithm improvements, and usability, rather than an AI-assisted diagnostic aid for human readers.

6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) Was Done

The document describes "Algorithm Testing and Integration" and "Bench testing" which would fall under standalone performance evaluation of the algorithms. However, explicit metrics or results from such standalone performance are not provided, only that the algorithms "met requirements and functioned as intended." The system itself is explicitly designed for "analysis by a physician," indicating a human-in-the-loop design.

7. The Type of Ground Truth Used

For the algorithm testing, the ground truth would likely be defined by the requirements themselves and the expected output of the algorithms given specific simulated inputs. For usability testing, the 'ground truth' would be user feedback and whether the system functions as expected from a user perspective. There is no mention of pathology, outcomes data, or expert consensus from clinical cases as a ground truth in this summary.

8. The Sample Size for the Training Set

This information is not provided. The document focuses on verification and validation of the updated system against a predicate device and engineering requirements, not on the development or training of new machine learning models.

9. How the Ground Truth for the Training Set Was Established

This information is not provided, as the submission does not detail the training of a new AI/ML model.

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The Medtronic CardioInsight® Cardiac Mapping System is intended for acquisition, analysis, display and storage of cardiac electrophysiological data and maps for analysis by a physician.

The Medtronic CardioInsight® Cardiac Mapping System is a non-invasive mapping system for beat-by-beat, multichamber, 3D mapping of the heart. The CardioInsight Cardiac Mapping System displays cardiac maps and virtual electrograms from real-time chest ECG signals (measured by a Sensor Array placed on the torso) and CT scan data. The ECG signals in concert with the CT scan information (geometrical information) are used in mathematical algorithms to transform the measured body surface signals into epicardial signals via solving the cardiac inverse problem. The CardioInsight Cardiac Mapping System software uses this data to provide various cardiac signal analyses and displays interactive 3D color maps including potential, activation, voltage, propagation, and phase maps. The CardioInsight Cardiac Mapping System system is mobile and can be used for mapping at the patient's bedside or in the EP lab.

This looks like a 510(k) summary for the Medtronic CardioInsight® Cardiac Mapping System.

Based on the provided document, the device described is a cardiac mapping system, not an AI/ML algorithm that predicts or diagnoses. Therefore, much of the requested information (like acceptance criteria for AI performance, sample sizes for training/test sets, expert adjudication, MRMC studies, or standalone algorithm performance) is not applicable to this device submission.

The document focuses on demonstrating substantial equivalence to a predicate device (ECVUE MAPPING SYSTEM) based on shared intended use, technology, and performance, rather than providing specific performance metrics against clinical acceptance criteria in the context of an AI model's output.

Here's what can be extracted and what is explicitly stated as not applicable:

1. Table of Acceptance Criteria and Reported Device Performance:

The document describes various performance tests conducted to verify the device's compliance with safety and specifications, and that it "performs as designed." However, it does not present a table of specific clinical acceptance criteria with corresponding performance metrics in the way one would for a diagnostic or predictive AI device. The performance data highlighted are primarily related to electrical safety, mechanical safety, software/firmware verification, algorithm testing (to ensure it met requirements and functioned as intended), packaging validation, and usability testing.

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

• Not Applicable in the traditional AI sense. The document states: "Preclinical and clinical testing were not required for the CardioInsight Cardiac Mapping System." This implies there wasn't a specific "test set" of clinical cases used to evaluate an algorithm's performance against ground truth in the way an AI model would be tested. The validation was primarily system functionality and safety.

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

• Not Applicable. As no clinical "test set" was described for an AI algorithm's output, there's no mention of experts establishing ground truth in this context.

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

• Not Applicable.

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:

• Not Applicable. This is not an AI-assisted diagnostic device where human reader improvement would be measured. The device provides data and maps for analysis by a physician.

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

• Not Applicable. The device is a system for "acquisition, analysis, display and storage of cardiac electrophysiological data and maps for analysis by a physician." It's an instrument providing information to a human. There is no mention of a standalone diagnostic algorithm being evaluated.

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

• Not Applicable in the context of AI performance. The system's "truth" is its ability to accurately acquire and transform ECG and CT data into epicardial signals and maps, which is verified through engineering and functional testing rather than clinical expert ground truth for interpretation.

8. The sample size for the training set:

• Not Applicable. The device is not an AI/ML model that undergoes a "training phase" with a specific dataset. Its algorithms are based on mathematical principles (solving the cardiac inverse problem) and are validated through standard software and system verification.

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

• Not Applicable. See point 8.

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