Search Results

The SIGNA™ Artist Evo system is a whole body magnetic resonance scanner designed to support high signal-to-noise ratio, and short scan times. It is indicated for use as a diagnostic imaging device to produce axial, sagittal. coronal and oblique images, spectroscopic images, parametric maps, and/or spectra, dynamic images of the structures and/ or functions of the entire body, including, but not limited to, head, neart, abdomen, pelvis, joints, prostate, blood vessels, and musculoskeletal regions of the body. Depending on the region of interest being imaged, contrast agents may be used.

The images produced by the SIGNA™ Artist Evo system reflect the spatial distribution or molecular environment of nuclej exhibiting magnetic resonance. These images and/or spectra when interpreted by a trained physician vield information that may assist in diagnosis.

The SIGNA™ Artist Evo system is a whole body magnetic resonance scanner designed to support high resolution, high signal-to-noise ratio, and short scan times. The system features a superconducting magnet. The data acquisition system accommodates up to 128 independent receive channels in various increments and multiple independent coil elements per channel during a single acquisition series. The system uses a combination of time varying magnetic fields (gradients) and RF transmissions to obtain information regarding the density and position of elements exhibiting magnetic resonance. The system can image in the sagittal, coronal, axial, oblique, and double oblique planes, using various pulse sequences and reconstruction algorithms.

The provided text is a 510(k) summary for the SIGNA™ Artist Evo magnetic resonance diagnostic device. It details how the device is considered substantially equivalent to a predicate device (SIGNA™ Artist) rather than presenting a study for meeting specific acceptance criteria itself. The document focuses on non-clinical testing for safety and performance, and the claim of "acceptable diagnostic image performance" based on sample clinical images, but does not outline a formal study with acceptance criteria for a specific AI or image perception outcome.

Therefore, I cannot fulfill all parts of your request as the provided text does not contain the acceptance criteria and the comprehensive study details you've asked for related to device performance in an AI context.

However, based on the information provided, here's what can be extracted and inferred regarding the device's substantial equivalence claim:

1. Table of Acceptance Criteria and Reported Device Performance

As this is a 510(k) for a magnetic resonance diagnostic device (not an AI algorithm with specific performance metrics like sensitivity/specificity for a particular disease), the "acceptance criteria" are related to substantial equivalence to a predicate device in terms of safety, technological characteristics, and image quality.

Regarding specific study details for AI/perception performance:

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

• The document states: "Sample clinical images have been included in this submission." It does not specify a test set sample size, data provenance, or whether it was retrospective or prospective. This implies a qualitative assessment rather than a structured quantitative study.

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

• Not specified. The assessment of "acceptable diagnostic image performance" would inherently involve expert interpretation, but the number and qualifications of these experts are not detailed.

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

• Not specified.

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 comparative effectiveness study is described. The device is an MRI scanner, not an AI-assisted diagnostic tool discussed in this document.

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

• Not applicable as this is a medical imaging device, not an AI algorithm being evaluated for standalone performance. "The images produced...when interpreted by a trained physician yield information that may assist in diagnosis."

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

• For the "acceptable diagnostic image performance," the ground truth is implicitly expert interpretation of images for diagnostic quality, likely a qualitative assessment. There's no mention of pathology or outcomes data being used to establish ground truth for this aspect.

8. The sample size for the training set

• Not applicable. There is no mention of a "training set" as this device is not an AI/machine learning algorithm undergoing a training/validation process in the context of this 510(k) summary.

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

• Not applicable.

In summary: The provided 510(k) relates to the substantial equivalence of an MRI scanner (SIGNA™ Artist Evo) to its predicate device (SIGNA™ Artist) based on non-clinical engineering and systems testing, and a qualitative assessment of sample clinical images for diagnostic performance. It is not a submission detailing the performance of an AI diagnostic algorithm, and therefore the specific criteria you've asked for related to AI-driven studies are not present in this document.

Ask a specific question about this device

The SIGNA Artist system is a whole body magnetic resonance scanner designed to support high signalto-noise ratio, and short scan times. It is indicated for use as a diagnostic imaging device to produce axial, sagittal, coronal. and oblique images, spectroscopic images, parametric maps, and/or spectra, dynamic images of the structures and/or functions of the entire body, including, but not limited to, head, neck, heart, abdomen, pelvis, joints, prostate, blood vessels, and musculoskeletal regions of the body. Depending on the region of interest being imaged, contrast agents may be used.

The images produced by the SIGNA Artist system reflect the spatial distribution or molecular environment of nuclei exhibiting magnetic resonance. These images and/or spectra when interpreted by a trained that may assist in diagnosis.

The SIGNA Artist system is a whole body magnetic resonance scanner designed to support high resolution, high signal-to-noise ratio, and short scan times. The system features a superconducting magnet. The data acquisition system accommodates up to 128 independent receive channels in various increments and multiple independent coil elements per channel during a single acquisition series. The system uses a combination of time varying magnetic fields (gradients) and RF transmissions to obtain information regarding the density and position of elements exhibiting magnetic resonance. The system can image in the sagittal, coronal, axial, oblique, and double oblique planes, using various pulse sequences and reconstruction algorithms.
This 510(k) submission is for the SIGNA Artist 1.5T MR system, and has been triggered by the addition of the AIR Recon DL software feature.
The AIR Recon DL feature has been previously cleared for use on the SIGNA Premier 3T system through K193282, which is used as a reference device for this submission.

Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided text:

Acceptance Criteria and Device Performance for GE Healthcare SIGNA Artist with AIR Recon DL

The provided document describes the 510(k) submission for the GE Healthcare SIGNA Artist system with the added AIR Recon DL software feature. The study focuses on evaluating the impact of this new feature on image quality.

1. Table of Acceptance Criteria and Reported Device Performance

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

The document does not explicitly state the specific sample size for the test set used in the clinical evaluation. It mentions "sample images from clinically indicated scans."

The data provenance for the clinical evaluation is implied to be retrospective as it involves "sample images from clinically indicated scans" that were then evaluated with and without the AIR Recon DL feature. The country of origin of the data is not specified.

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

The document states that "Radiologists were asked to rate the images, and to comment on any notable aspects related to image quality." It does not specify the exact number of experts or their qualifications (e.g., years of experience, subspecialty).

4. Adjudication Method for the Test Set

The adjudication method is not explicitly stated. The text only mentions that "Radiologists were asked to rate the images, and to comment on any notable aspects related to image quality." This suggests an individual review process, but it doesn't detail how discrepancies or consensus building was handled if multiple radiologists reviewed the same case. It doesn't mention methods like 2+1, 3+1, or majority vote.

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

A multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly stated as the primary methodology. The clinical evaluation described involves radiologists rating images "both with and without the AIR Recon DL feature" and stating their preference. While this provides comparative feedback, it does not quantify human reader improvement in terms of diagnostic accuracy or a specific effect size. The study concludes that radiologists "preferred the AIR Recon DL images for clinical use" and that lesion conspicuity was maintained, indicating a subjective improvement, but not a measurable effect size of diagnostic performance.

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

Yes, a standalone performance evaluation was done as part of the "Nonclinical Tests." These tests were designed to evaluate the AIR Recon DL feature "and its impact on image quality, including SNR, sharpness, low contrast detectability, and noise spectral content. Analysis was performed to confirm that the feature does not introduce significant bias that might impact quantitative measurements based on signal intensity. The influence of motion during image acquisition on the performance of AIR Recon DL was also evaluated." These are objective, quantitative measurements of the algorithm's output without human interpretation being the primary endpoint.

7. The Type of Ground Truth Used

For the nonclinical tests, the ground truth appears to be based on objective image quality metrics, physical phantoms, and simulated conditions. For instance, evaluating SNR, sharpness, noise spectral content, a lack of signal intensity bias, and motion artifact influence against established benchmarks or predefined ideal conditions.

For the clinical tests, the ground truth for "equivalent or better image quality" and "maintained lesion conspicuity" was established by expert consensus/opinion from radiologists.

8. The Sample Size for the Training Set

The document does not specify the sample size for the training set used for the AIR Recon DL algorithm. It only mentions that the AIR Recon DL feature "has been previously cleared for use on the SIGNA Premier 3T system through K193282, which is used as a reference device for this submission." This implies the training was done prior to this specific submission for the SIGNA Artist.

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

The document does not provide details on how the ground truth for the training set was established for the AIR Recon DL algorithm. While it mentions the algorithm was previously cleared for another device, it does not elaborate on the specific data used for its initial training and ground truth annotation.

Ask a specific question about this device

The SIGNA Architect, SIGNA Artist, Discovery MR750 3.0T, Discovery MR450 1.5T, Discovery MR750w 3.0T and the Optima MR450w 1.5T systems are whole body magnetic resonance scanners designed to support high signal-to-noise ratio, and short scan times. It is indicated for use as a diagnostic imaging device to produce axial, sagittal. coronal, and oblique images, spectroscopic images, parametric maps, and/or spectra, dynamic images of the structures and/or functions of the entire body, including, but not limited to, head, neck, TMI, spine, breast, heart, abdomen, pelvis, joints, prostate, blood vessels, and musculoskeletal regions of the body. Depending on the region of interest being imaged, contrast agents may be used.

The images produced by the SIGNA Architect, SIGNA Artist, Discovery MR750 3.0T, Discovery MR450 1.5T, Discovery MR750w 3.0T and the Optima MR450w 1.5T systems reflect the spatial distribution or molecular environment of nuclei exhibiting magnetic resonance. These images and/or spectra when interpreted by a trained physician vield information that may assist in diagnosis.

The Discovery MR750 3.0T, Discovery MR450 1.5T, Discovery MR750w 3.0T, Optima MR450w 1.5T, SIGNA Architect and SIGNA Artist systems are whole body magnetic resonance scanners designed to support high resolution, high signal-to-noise ratio, and short scan times. The systems each feature a superconducting magnet. The data acquisition system accommodates up to 128 independent receive channels in various increments and multiple independent coil elements per channel during a single acquisition series. Each system uses a combination of time varying magnetic fields (gradients) and RF transmissions to obtain information regarding the density and position of elements exhibiting magnetic resonance. Each system can image in the sagittal, coronal, axial, oblique, and double oblique planes, using various pulse sequences and reconstruction algorithms. The Discovery MR750 3.0T, Discovery MR450 1.5T, Discovery MR750w 3.0T, Optima MR450w 1.5T, SIGNA Architect. SIGNA Artist systems are designed to conform to NEMA DICOM standards (Digital Imaging and Communications in Medicine).

The original description hasn't changed from predicate devices (K160618), other than reflecting the additional receive channels available.

The modifications to these systems include the MAGIC DWI and CardioMaps software features, delivered via the DV26 program. The proposed software features will be ported over to other GE Healthcare MR systems based on appropriate design controls and evaluation of the change in accordance with FDA's Guidance—Deciding When to Submit a 510(k) for a Change to an Existing Device.

This document describes the premarket notification (510(k)) for GE Medical Systems' SIGNA Architect, SIGNA Artist, Discovery MR750 3.0T, Discovery MR450 1.5T, Discovery MR750w 3.0T and the Optima MR450w 1.5T Magnetic Resonance (MR) diagnostic devices. The submission focuses on the addition of MAGIC DWI (Diffusion-Weighted Imaging) and CardioMaps software features.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state specific quantitative acceptance criteria or performance metrics for the MAGIC DWI and CardioMaps software features in a table format. Instead, it indicates that testing was completed with "passing results per the pass/fail criteria defined in the test cases."

Implicit Acceptance Criteria (inferred from the document):

• Safety and Effectiveness: The primary acceptance criterion is that the modified software features (MAGIC DWI and CardioMaps) are "as safe and effective as the predicate" devices and do "not raise different questions of safety and effectiveness."
• Compliance with Standards: The software features must comply with voluntary standards: AAMI/ANSI 62304, AAMI/ANSI ES60601-1, and IEC 60601-2-33.
• Acceptable Performance: Phantom testing for both software features must demonstrate "acceptable performance."

Reported Device Performance:

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

• Test Set Sample Size: The document does not specify a numerical sample size for either the phantom testing or the clinical images. It generically refers to "phantom testing" and "sample clinical images."
• Data Provenance: Not explicitly stated. For phantom testing, it's typically controlled laboratory conditions. For clinical images, it's not mentioned whether they are retrospective or prospective, nor their country of origin.

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

The document does not provide this information.
The summary states that images and/or spectra are interpreted by a "trained physician," but it doesn't detail the number or qualifications of experts involved in establishing ground truth for the specific performance evaluation of the new software features.

4. Adjudication Method for the Test Set

The document does not specify an adjudication method.
It states that "passing results per the pass/fail criteria defined in the test cases" were achieved for phantom testing. For clinical images, it mentions they are "interpreted by a trained physician," implying clinical judgment, but no formal adjudication process (like 2+1 or 3+1) is described for the evaluation presented in this summary.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

The document does not indicate that an MRMC comparative effectiveness study was performed.
The evaluation relies on compliance with standards, phantom testing, and presentation of sample clinical images to demonstrate "substantial equivalence" rather than a comparative effectiveness study measuring human reader improvement with AI assistance. The software features are enhancements to image acquisition and processing, not explicitly AI-assisted diagnostic tools in the context of comparative reading studies.

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

While the software features (MAGIC DWI and CardioMaps) represent algorithm-only additions, the document emphasizes that the "images and/or spectra when interpreted by a trained physician yield information that may assist in diagnosis."
The "phantom testing" and quality assurance measures (e.g., unit-level, integration, performance, safety testing) can be considered standalone evaluations of the algorithms' output quality and adherence to specifications. However, the ultimate "performance" in the diagnostic context is tied to physician interpretation. The regulatory focus here is on the system producing diagnostically useful images, not on an algorithm making a standalone diagnosis.

7. The Type of Ground Truth Used

• For Phantom Testing: The ground truth would typically be established by known physical properties or measurements of the phantom itself. The "pass/fail criteria" would be based on expected quantitative accuracy, image quality, or signal properties against these known values.
• For Clinical Images: The document mentions "images and/or spectra when interpreted by a trained physician yield information that may assist in diagnosis." This implies that the effectiveness in a clinical setting is ultimately judged by expert clinical interpretation, but it does not specify a formal "ground truth" (e.g., pathology, surgical findings, long-term outcomes) used to validate the clinical utility of the specific new software features. It's more about demonstrating that the images produced can be interpreted by a physician to assist diagnosis.

8. The Sample Size for the Training Set

The document does not provide any information regarding a training set sample size. This is likely because the referenced software features are defined as modifications to existing MR systems, and while they involve algorithms, the summary doesn't describe them as machine learning models that require distinct "training sets" in the typical sense. The development process described (risk analysis, requirements reviews, design reviews, various levels of testing) is a standard software engineering approach.

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

As no training set is mentioned for machine learning, information on how its ground truth was established is not applicable or provided in this document.

Ask a specific question about this device