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The Rhythm View Workstation is a computerized system that assists in the diagnosis of complex cardiac arrhythmias. The Rhythm View Workstation is used to analyze electrogram and electrocardiogram signals and display them in a visual format.

RhythmView uses electrical signals collected from the electrodes of one or more multi-polar electrophysiology (EP) catheters. It provides a dynamic, simplified representation of wave propagation. The RhythmView computes and displays electrical rotors or focal beat sources that may sustain human heart rhythm disorders including focal AT, AFL, other SVT, AF, VT and VF in a given patient. The product takes as input electrical signals recorded during the heart rhythm disorder under consideration, typically from multiple specified locations within the heart during an electrophysiological study. The RhythmView then uses proprietary patented algorithms and methods to compute spatial organization during the heart rhythm disorder. These computed elements are displayed graphically in interactive form for review to aid diagnosis by the physician during an electrophysiology study.

Here's a breakdown of the acceptance criteria and study information for the RhythmView Workstation 6.1, derived from the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document primarily focuses on verifying the software updates for the RhythmView Workstation 6.1 and demonstrating that it performs at least as well as the prior versions, particularly concerning Rotational Activity Profile (RAP) detection. While specific numerical acceptance criteria (e.g., sensitivity, specificity thresholds) are not explicitly stated in the provided text, the performance is described in terms of validation and verification against its intended functionality.

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

• Test Set Sample Size: The document mentions "clinical data sets" for validating the Stability Map and default Stability Filter setting, but does not specify the exact number of cases or patients in these data sets.
• Data Provenance: The data used for validation are described as "clinical data sets." The document does not specify the country of origin of this data or if it was retrospective or prospective. It uses "clinical data sets," implying real patient data.

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

• Number of Experts: The document states that "Physician reads" were used to validate RAP functionality. It does not specify the number of physicians involved.
• Qualifications of Experts: The qualifications of the "Physicians" are not explicitly stated (e.g., years of experience, subspecialty).

4. Adjudication Method for the Test Set

The document does not describe a specific adjudication method (e.g., 2+1, 3+1). It only mentions "Physician reads" for validation.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Its Effect Size

The document does not indicate that a multi-reader multi-case (MRMC) comparative effectiveness study was performed to assess the improvement of human readers with AI vs. without AI assistance. The testing described focuses on validating the device's functionality and its comparison to prior versions.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

The document describes testing that evaluates the algorithm's performance (e.g., RAP detection, Stability Map validation) using "clinical data sets." While these tests indirectly assess the algorithm's capabilities, the validation of RAP functionality involved "Physician reads," suggesting a human-in-the-loop component for some aspects. Therefore, it's not strictly a standalone performance study without any human involvement for interpretation.

7. The Type of Ground Truth Used

The ground truth for the test sets (or validation sets) appears to be based on:

• Known rotational targets: For validating RAP detection, the system was validated against "known rotational targets," implying a pre-established understanding of these targets. This could be based on prior clinical assessment, expert consensus, or established electrophysiology principles.
• Physician reads: For RAP functionality validation, physician interpretations served as part of the ground truth or a reference point.
• Clinical data sets: The term "clinical data sets" implies data derived from patient studies, where the outcomes or characteristics might be considered true based on clinical assessment, although explicit details are lacking.

8. The Sample Size for the Training Set

The document does not provide any information regarding the sample size used for the training set.

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

The document does not provide any information on how the ground truth for the training set was established. In fact, training data details are entirely absent from the provided text, which focuses on validation and verification of software updates.

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For use in cardiac electrophysiology procedures to assist in the diagnosis of arrhythmias that may be difficult to identify using conventional mapping systems alone (i.e., linear mapping catheters). The FIRMap Catheter may also be used for delivery of externally generated pacing stimuli.

The FIRMap Catheter is a sterile, single use device used to detect and record intracardiac electrical potentials and to deliver externally generated pacing stimuli. The FIRMap Multiple Electrode Recording and Pacing Catheter may also be used for delivery of externally generated pacing stimuli. The FIRMap catheter is delivered to the heart chamber via an intravascular sheath. After the catheter is positioned the sheath is withdrawn enough to allow the basket to expand and the electrodes to contact the heart wall.

The furnished document is a 510(k) summary for the FIRMap Catheter, which is an electrode recording catheter used in cardiac electrophysiology procedures. It states that "No animal or clinical testing was conducted." Therefore, there is no study described that proves the device meets specific acceptance criteria based on performance with human or animal subjects.

However, the document lists several performance data tests and concludes that these tests demonstrate substantial equivalence to the predicate device and assure conformity to appropriate requirements. This represents the "study" that was performed and its findings regarding acceptance.

Here is an attempt to structure the requested information based on the provided text, while noting the absence of clinical performance data in the traditional sense:

1. Table of Acceptance Criteria and Reported Device Performance

Since no specific acceptance criteria values were explicitly stated for the listed tests, and the "reported device performance" is a general statement of success rather than quantitative results, I will infer the acceptance criterion as "conforms to requirements" and the performance as "met this standard."

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

• Sample Size: Not specified for the listed tests (Radial Strength, Lateral Strength, etc.). These appear to be bench or engineering tests where "sample size" might refer to the number of catheters tested, which is not provided.
• Data Provenance: The tests are likely retrospective bench/engineering tests conducted in a lab setting by the manufacturer (Abbott Electrophysiology) to demonstrate the physical properties and functional aspects of the device, rather than involving patient data. Country of origin not specified, but likely where Abbott Electrophysiology's R&D is located.

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

Not applicable. For bench/engineering tests like Radial Strength or Torque, "ground truth" is typically established by engineering specifications, material properties, and relevant industry standards, not by human expert consensus on clinical findings.

4. Adjudication Method for the Test Set

Not applicable. As these were non-clinical bench tests, adjudication methods like 2+1 or 3+1 (common in image interpretation or clinical trials) are not relevant. Test results would be compared against predefined engineering specifications or performance limits.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No. The document explicitly states: "No animal or clinical testing was conducted." Therefore, no MRMC study involving human readers or AI assistance was performed or described.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

No. The FIRMap Catheter is a physical medical device, not a software algorithm or AI. Performance is related to its physical and functional characteristics, not algorithm capability.

7. Type of Ground Truth Used

The ground truth for the performance data (Radial Strength, Lateral Strength, etc.) would be established by engineering specifications and recognized industry standards (e.g., ISO and IEC standards listed in the document), rather than expert consensus, pathology, or outcomes data, as no clinical testing was performed.

8. Sample Size for the Training Set

Not applicable. As a physical medical device, there is no "training set" in the context of machine learning or AI algorithms. The design and manufacturing process would involve engineering and material testing, but not a data-driven training set.

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

Not applicable, as there is no training set for this type of device.

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The Rhythm View Workstation is a computerized system that assists in the diagnosis of complex cardiac arrhythmias. The Rhythm View Workstation is used to analyze electrogram and electrocardiogram signals and display them in a visual format.

The RhythmView Workstation is comprised of the following components: Cart, Monitor/Display, Computer, Radio-Frequency Identification (RFID) Reader/Writer, Software, Keyboard, Mouse, Two Port USB Switch, Solid State Hard Drive (optional component).

RhythmView takes electrical signals collected from multi-polar electrophysiology catheters and outputs a graphic display that assists in the diagnosis of cardiac arrhythmias.

The RhythmView computes and displays electrical rotors or focal beat sources that may sustain human heart rhythm disorders including focal AT, AFL, other SVT, AF, VT and VF in a given patient. The product takes as input electrical signals recorded during the heart rhythm disorder under consideration, typically from multiple specified locations within the heart during an electrophysiological study. The RhythmView then uses proprietary patented algorithms and methods to compute spatial organization during the heart rhythm disorder. These computed elements are displayed graphically in interactive form for review to aid diagnosis by the physician during an electrophysiology study.

Here's an analysis of the provided text to extract information about the device's acceptance criteria and the study proving it meets those criteria.

Note: The provided document is a 510(k) summary for a software update to an existing device, the RhythmView Workstation. As such, the "study" described focuses on verification and validation of the new software features, rather than a full clinical trial to establish initial safety and effectiveness, which would have been done for the original device. This means some of the requested information (like MRMC studies for AI performance) might not be directly applicable or explicitly detailed in this type of submission.

Acceptance Criteria and Device Performance Study for RhythmView Workstation SW V6.0.3

The RhythmView Workstation is a computerized system that assists in the diagnosis of complex cardiac arrhythmias by analyzing electrogram and electrocardiogram signals and displaying them in a visual format. The current 510(k) (K161240) pertains to software version 6.0.3, which introduces new features like "Stability Map" and "Epoch Timeline."

The performance testing primarily focuses on validating these new software features and confirming that the device continues to meet its intended use.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly list quantitative "acceptance criteria" for the new software features (e.g., a specific sensitivity or specificity threshold). Instead, it describes types of validation performed to ensure the new features function as intended and do not negatively impact the existing functionality.

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

• Test Set Sample Size: The document mentions "clinical data sets" for validation of the Stability Map, but does not specify the sample size (i.e., number of patients or cases) used for these tests.
• Data Provenance: The document states "clinical data sets" were used. No specific country of origin is mentioned. The testing methods described imply a retrospective analysis of existing clinical data, particularly for validating the Stability Map's compilation and consistency of diagnosis.

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

• Number of Experts: The document refers to "Physician validation," implying that multiple physicians were involved in confirming the consistency of diagnoses. However, the exact number of experts is not specified.
• Qualifications of Experts: The experts are identified as "Physician(s)." No specific qualifications (e.g., years of experience, sub-specialty) are provided in this summary.

4. Adjudication Method for the Test Set

The document does not describe a formal adjudication method (like 2+1 or 3+1 consensus) for establishing ground truth or resolving discrepancies among readers. The "Physician validation" suggests that physicians reviewed the outputs, but the process for achieving a single "ground truth" or resolving disagreements is not detailed.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No. The document does not describe a multi-reader multi-case (MRMC) comparative effectiveness study to assess how human readers improve with AI vs. without AI assistance. This submission is for a software update that provides new display and analysis tools, rather than an AI-driven diagnostic aid that would directly assist human interpretation in a comparative manner. The "Physician validation" described is more akin to a usability/consistency check of the new features.

6. Standalone Performance (Algorithm Only)

The device is a "computerized system that assists in the diagnosis" and displays information for physician interpretation. It's not a standalone AI algorithm designed to provide a diagnosis without human interaction. The validation focuses on the accuracy of the output of the system (e.g., Stability Maps as correct compilations) and its utility for physicians, rather than an independent diagnostic performance of the algorithm itself. Therefore, a standalone (algorithm only without human-in-the-loop performance) study, in the sense of an AI model making diagnostic predictions, was not performed or detailed for this type of device and software update.

7. Type of Ground Truth Used

The ground truth for the "Physician validation" appears to be expert consensus on diagnosis. The validation checks if "activation maps with and without RAP lead to a consistent diagnosis," implying that the physicians' established diagnosis (which forms the clinical ground truth for the case) should remain consistent regardless of the presence of the new RAP display features. For the "Stability Map Accuracy," the ground truth seems to be computational correctness (i.e., checking if the map is a "correct compilation" of other data).

8. Sample Size for the Training Set

Not applicable/Not provided. This document describes the validation of a software update for an existing medical device, not the development of a new machine learning model requiring a training set. The algorithms are proprietary, patented, and seem to be based on established computational methods for signal processing and display, rather than data-driven learning from a large training dataset.

9. How Ground Truth for the Training Set Was Established

Not applicable. As no training set is mentioned (see point 8), there is no information on how a ground truth for a training set would have been established.

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For use in cardiac electrophysiology procedures to assist in the diagnosis of complex arrhythmias that may be difficult to identify using conventional mapping systems alone (i.e., linear mapping catheters). The FIRMap Catheter may also be used for delivery of externally generated pacing stimuli.

The catheter is used in cardiac electrophysiology procedures to assist in the diagnosis of complex arrhythmias that may be difficult to identify using conventional mapping systems alone (i.e., linear mapping catheters). The FIRMap Multiple Electrode Recording and Pacing Catheter may also be used for delivery of externally generated pacing stimuli.

The FIRMap catheter is delivered to the heart chamber via an intravascular sheath. After the catheter is positioned the sheath is withdrawn enough to allow the basket to expand and the electrodes to contact the heart wall.

The provided text is a 510(k) Summary for the FIRMap Catheter, which outlines the device's technical characteristics and the performance testing conducted to demonstrate its substantial equivalence to a predicate device. This document describes a medical device, not an AI/ML powered device, and therefore the relevant information for an AI/ML acceptance criteria and study cannot be extracted.

Reasoning for inability to extract AI/ML specific information:

The document describes the FIRMap Catheter, an electrode recording catheter used in cardiac electrophysiology procedures. The testing described is focused on the mechanical integrity and functionality of the catheter's basket subassembly, which are hardware-related aspects (e.g., radial force, spline stiffness, introduction/withdrawal cycling, and in-vivo testing in a "Pancake Atrium").

There is no mention of:

• An algorithm or AI/ML model.
• Data provenance, test sets, or training sets in the context of an AI/ML model.
• Expert interpretation of AI/ML outputs or ground truth establishment for AI/ML.
• Multi-reader multi-case studies related to AI/ML assistance.
• Standalone performance of an algorithm.

The document entirely focuses on the physical device and its mechanical/electrical performance, not on software or AI capabilities.

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