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The AcQMap 3D Imaging and Mapping Catheter is intended to be used in the right and left atrial chambers to collect ultrasound data for visualizing the selected chamber and recording electrical impulses in patients with complex arrhythmias that may be difficult to identify using conventional mapping systems alone.

Electrode Recording Catheter/Transducer Ultrasound

The provided text describes the AcQMap 3D Imaging and Mapping Catheter, but it primarily focuses on establishing substantial equivalence to a predicate device rather than presenting a detailed study proving the device meets specific acceptance criteria in the manner an AI/ML device would typically be evaluated (e.g., accuracy, sensitivity, specificity for classification tasks).

However, I can extract the information relevant to a "study" that assessed the device's performance, even if it's not a typical AI/ML validation study. The closest an AI/ML validation study with the criteria you listed comes is the "DDRAMATIC-SVT" clinical study.

Here's the breakdown based on the provided text:

Acceptance Criteria and Reported Device Performance:

The document doesn't explicitly state quantitative acceptance criteria for parameters like sensitivity, specificity, or image quality, as it's a 510(k) submission focused on substantial equivalence for a medical device that collects data for imaging and mapping, not an AI/ML diagnostic output. The "performance" is primarily described in terms of the device's ability to safely and effectively perform its intended function.

Study Details (Based on the DDRAMATIC-SVT Clinical Study):

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

   • Sample Size: 84 patients.
   • Data Provenance: Prospective, non-randomized, open-label study conducted at eight clinical sites outside the U.S.

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

   • This information is not provided in the text. The study describes "gathering data to create... dipole density activation maps" and "recording electrical impulses," implying physician interpretation of the generated maps and electrical data, but the number and qualifications of experts for ground truth establishment are not detailed.

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

   • This information is not provided in the text.

4. 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:

   • This was not a MRMC comparative effectiveness study and does not involve AI assistance for human readers in the context described. The device itself is a data collection and imaging tool, not an AI diagnostic algorithm.

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

   • This was not a standalone algorithm performance study. The device's function is to collect data for physicians to interpret. The text states it helps in "identifying complex arrhythmias that may be difficult to identify using conventional mapping systems alone," implying human interpretation of the device's output.

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

   • The text describes the study as demonstrating "safety and performance in gathering data to create right and/or left atrial dipole density activation maps." The "ground truth" for the effectiveness of the device as a data collection tool would implicitly be its ability to generate these maps and record electrical impulses as intended, likely verified by the practicing cardiologists using the system during the study. It's not explicitly tied to a definitive "ground truth" like pathology or a hard clinical outcome in the same way an AI diagnostic would be.

7. The sample size for the training set:

   • This information is not applicable/not provided. The device is a hardware system for data collection and imaging, not an AI/ML model that requires a training set.

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

   • This information is not applicable/not provided as there is no training set for an AI/ML model.

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