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The Resoundant Acoustic Driver System is intended for use with magnetic resonance diagnostic devices (MRDD) that include legally marketed MR elastography capabilities. It is indicated for generating acoustic vibrations in the body during an MRI exam, in order to assess tissue elasticity for diagnostic purposes as part of magnetic resonance elastography (MRE). When interpreted by a trained physician, this information can be useful in determining a diagnosis.

The Resoundant Acoustic Driver System includes an Active Driver, connective tubing and a Passive Driver. The system generates transverse acoustic waves in the low-audio frequency range in the body during an MRI exam. This allows assessment of elastic properties of soft tissue to aid in medical diagnosis as part of magnetic resonance elastography (MRE).

The Active Driver component is an electro-mechanical device that consists of a function generator, power-amplifier, linear motor and pump head, along with a microprocessor-based controller and power supply electronics, in an enclosure. The Passive Driver component is connected to the Active Driver through flexible tubing and is used to induce small vibrations in the scan subject. The Passive Driver is a lightweight enclosure containing no electrical components. It features a connection for flexible tubing and a diaphragm that is placed securely over patient clothing.

The Resoundant Acoustic Driver System is not an AI/ML device but an acoustic driver system used in Magnetic Resonance Elastography (MRE). Therefore, the typical acceptance criteria and study designs associated with AI/ML devices do not apply. This document primarily focuses on demonstrating substantial equivalence to a predicate device rather than providing extensive performance metrics against a clinical ground truth.

However, based on the provided text, we can extract the verification and validation information to describe how the device (not an AI algorithm) meets its acceptance criteria.

1. Table of Acceptance Criteria (from Verification and Validation) and Reported Device Performance

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

This information is not applicable and not provided in the document. The device is a hardware accessory, not a diagnostic algorithm that processes data to produce a result. Its "performance" is evaluated through engineering and safety tests rather than analysis of patient data.

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

This information is not applicable and not provided. Ground truth in the context of diagnostic algorithms refers to a confirmed diagnosis or condition. For a hardware device, ground truth would relate to measurable physical properties or compliance with engineering standards, which are typically assessed by engineers and testing personnel, not medical experts establishing diagnostic ground truth from patient data.

4. Adjudication Method for the Test Set

This information is not applicable and not provided. Adjudication methods are relevant for resolving discrepancies in expert interpretations of data, particularly in studies involving diagnostic accuracy algorithms.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

No, an MRMC comparative effectiveness study was not done. This type of study assesses the impact of an AI system on human reader performance. The Resoundant Acoustic Driver System is a hardware component for MRE, not an AI system.

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

No, a standalone performance study was not done because this is not an algorithm. The device's function is to generate acoustic vibrations, which is a physical process, not a computational one.

7. The Type of Ground Truth Used

The "ground truth" for this device's performance relies on engineering specifications, international safety standards (e.g., IEC 60601 series), and functional design requirements. For example, the "ground truth" for electrical safety is compliance with specific clauses of IEC 60601-1-2014. For mechanical performance, it would be adherence to defined dimensional tolerances or operational parameters. It does not involve expert consensus, pathology, or outcomes data in the way an AI diagnostic tool would.

8. The Sample Size for the Training Set

This information is not applicable. The Resoundant Acoustic Driver System is a hardware device; it does not utilize a training set in the context of machine learning.

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

This information is not applicable, as there is no training set for a hardware device.

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MREplus+ Software is an assisted ROI drawing tool for liver MRE and Fat/Water images and is used for receiving, display, ROI selection, and analysis generation. It displays to a trained reader MRE and Fat/Water images, preliminary ROIs that it calculates from these images, and statistical analysis calculated from the ROIs and images are presented in a way for review and, optionally, modification by the trained reader.

The MREplus+ software is a tool for assisted Magnetic Resonance Elastography (MRE) and multipoint Dixon Fat/Water (FW) image analysis which calculates preliminary automated regions of interest (ROIs) and provides the environment for the trained readers to review the relevant MRE and FW information and approve or modify the ROIs. MREplus+ is intended to be used only with liver MRE and FW data. The inputs for MREplus+ are the MRE and FW images. In the case of MRE, this includes magnitude images (showing anatomy), wave images (showing wave propagation) with multiple time points across the wave cycle, and the elasticity and confidence images calculated by MRE's on-scanner MMDI algorithm or an offline MMDI packaged with MREplus+. In the case of FW, images include in-phase, out-of-phase, fat, water, fat fraction, and R2*. MREplus+ includes a DICOM receiver which can recognize and accept these images when sent from the MRI scanner or workstation using standard protocols. From these images, MREplus+ calculates automated ROIs. ROIs can be reviewed by an authorized trained reader for review, modification and approval. MREplus+ performs statistical calculations from the ROIs. MREplus+ outputs images, ROIs, and calculated results in an archive compatible report.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) summary for the MREplus+ Software:

Acceptance Criteria and Reported Device Performance

The FDA 510(k) summary does not explicitly list numerical "acceptance criteria" but rather describes the testing performed to demonstrate substantial equivalence to a predicate device. The performance is described qualitatively and in terms of failure rates and modification rates.

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

   • The description of the MREplus+ as an "assisted ROI drawing tool" and the detail about "preliminary automated ROIs" followed by human review and optional modification suggest that the device is not intended for standalone (algorithm-only) use in a clinical setting for diagnosis.
   • The reported performance metrics, like "99% compared to the standard predicate methodology" likely refers to the accuracy of the MREplus+ suggested ROIs' derived values when compared to the predicate's values, rather than a standalone diagnostic performance metric.

6. The Type of Ground Truth Used:

   • The ground truth for the test sets was established by "standard expert reviewer readings using the predicate device." This is a form of expert consensus or expert-derived ground truth, where the "standard" implies an accepted clinical practice for deriving measurements from MRE and Fat/Water images. It's not pathology or outcomes data.

7. The Sample Size for the Training Set:

   • The document does not specify the sample size used for the training set for the MREplus+ software. It only provides details about the validation (test) sets.

8. How the Ground Truth for the Training Set Was Established:

   • The document does not provide information on how the ground truth for the training set was established. Since it is an AI-assisted tool, it would presumably require a large, annotated dataset for training, but these details are not present in the provided summary.

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