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The SIGNA™ Champion is a whole body magnetic resonance scanner designed to support high resolution, 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 SIGNA™ Champion reflect the spatial distribution or molecular environment of nuclei exhibiting magnetic resonance. These images and/or spectra when interpreted by a trained physician yield information that may assist in diagnosis.

SIGNA™ Champion is a whole body magnetic resonance scanner designed to support high resolution, high signal-to-noise ratio, and short scan time. The system uses a combination of time-varying magnet 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, imaging techniques and reconstruction algorithms. The system features a 1.5T superconducting magnet with 70cm bore size. The system is designed to conform to NEMA DICOM standards (Digital Imaging and Communications in Medicine).

The provided text does not contain information about acceptance criteria and a study proving the device meets those criteria in the context of an AI/human reader performance study. The document is a 510(k) premarket notification for a Magnetic Resonance Diagnostic Device (SIGNA™ Champion).

The relevant sections state:

• "The subject of this premarket submission, the SIGNA™ Champion, did not require clinical studies to support substantial equivalence. Sample clinical images have been included in this submission."
• "The sample clinical images demonstrate acceptable diagnostic image performance of the SIGNA™ Champion in accordance with the FDA Guidance 'Submission of Premarket Notifications for Magnetic Resonance Diagnostic Devices' issued on October 10, 2023. The image quality of the SIGNA™ Champion is substantially equivalent to that of the predicate device."

This indicates that the FDA clearance for the SIGNA™ Champion MR system was based on demonstrating substantial equivalence to a previous predicate device (SIGNA™ Voyager) through non-clinical testing and the review of sample clinical images, rather than a prospective clinical study involving human readers and AI assistance for diagnostic tasks.

Therefore, I cannot provide the requested information regarding acceptance criteria and study details for an AI-assisted diagnostic device performance study because such a study was not conducted or reported in this 510(k) submission.

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The SIGNA Victor 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, neck, TMI, spine, breast, 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 SIGNA Victor reflect the spatial distribution or molecular environment of nuclei exhibiting magnetic resonance.

These images and/or spectra when interpreted by a trained physician yield information that may assist in diagnosis.

SIGNA™ Victor is a whole body magnetic resonance scanner designed to support high resolution, high signal-to-noise ratio, and short scan time. The system uses a combination of time-varying magnet 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, imaging techniques and reconstruction algorithms. The system features a 1.5T superconducting magnet with 60cm bore size. The system is designed to conform to NEMA DICOM standards (Digital Imaging and Communications in Medicine).

This document does not contain the information required to populate the requested table and answer the study-related questions. The provided text is a 510(k) summary for a Magnetic Resonance Diagnostic Device (SIGNA™ Victor).

Here's why and what information is available:

What the document does include:

• Device Name: SIGNA™ Victor
• Regulatory Status: 510(k) clearance, indicating substantial equivalence to predicate devices.
• Indications for Use: Broad diagnostic imaging of the entire body.
• Technology: 1.5T superconducting magnet, RF transmit/receive architecture, software application suite. Stated to employ the same fundamental scientific technology as predicate devices.
• Comparison to Predicates: Emphasizes that indications for use, technology, operating principles, and materials are similar to predicate devices (SIGNA™ Explorer (K143251), SIGNA™ Prime (K211980), SIGNA Voyager (K161567), SIGNA Artist Evo (K213603)).
• Non-Clinical Testing: Mentions compliance with various international standards (e.g., ANSI AAMI ES60601-1, IEC 60601-1-2, IEC 60601-2-33, IEC 62304, ISO 10993-1) and internal quality assurance measures (Risk Analysis, Requirements Reviews, Design Reviews, unit/integration/performance/simulated use testing). These tests demonstrate safety and performance but are usually technical safety and functional tests, not clinical performance metrics.
• Clinical Testing (or lack thereof): Crucially, the document states: "The subject of this premarket submission, the SIGNA™ Victor, did not require clinical studies to support substantial equivalence. Sample clinical images have been included in this submission. The sample clinical images demonstrate acceptable diagnostic image performance of the SIGNA™ Victor..."

Why the requested information is largely absent:

Because the device was cleared via the 510(k) pathway and "did not require clinical studies to support substantial equivalence," the detailed clinical performance data, acceptance criteria, sample sizes for test/training sets, ground truth establishment, expert qualifications, and MRMC study details that you're asking for are typically not part of such a submission.

The FDA's 510(k) pathway primarily focuses on demonstrating substantial equivalence to a legally marketed predicate device, rather than proving de novo safety and effectiveness through extensive clinical trials. The statement "The image quality of the SIGNA™ Victor is substantially equivalent to that of the predicate devices" is the core "acceptance criteria" and "proof" in this context, inferred from the non-clinical tests and sample images.

Therefore, I cannot generate the table or provide specific answers to most of the study-related questions based on the provided text. The document explicitly states no clinical studies were required, which means there was no formal "study" in the sense of a clinical trial to generate the kind of data you're requesting regarding accuracy, sensitivity, specificity, or specific performance metrics with defined acceptance criteria and comparator data.

Based on the available information, here's what could be inferred, though it doesn't directly meet all your criteria:

1. A table of acceptance criteria and the reported device performance

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

• The document states "did not require clinical studies" and only "sample clinical images have been included." It does not specify a sample size for a formal test set or its provenance (country of origin, retrospective/prospective). This implies a very limited, possibly anecdotal, set of images used for visual comparison rather than a statistically powered study.

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

• Not specified. Given the lack of a formal clinical study, the process for establishing ground truth (if any beyond visual review) for the "sample clinical images" is not detailed. The document mentions images are "interpreted by a trained physician," which is a general statement for MRI use, not specific to this submission.

4. Adjudication method for the test set

• Not specified, as a formal test set and adjudication process are not described.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done

• No, an MRMC study was not done. The document explicitly states "did not require clinical studies." Therefore, there is no effect size of human readers improving with AI vs. without AI assistance to report, as this is neither an AI device nor was such a study conducted.

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

• This device is an MRI machine, not an AI algorithm. Therefore, "standalone" algorithm performance is not applicable in the way it usually refers to AI/CADe devices. The MRI system itself has a "standalone" performance, which is assessed through technical and functional tests and comparison to its predicate devices.

7. The type of ground truth used

• For the "sample clinical images" (if any "ground truth" was formally established for them, which is unlikely given the context), it would implicitly be expert visual interpretation as determined by a trained physician for diagnostic purposes. It's not pathology or outcomes data from a formal study.

8. The sample size for the training set

• The device is an MRI scanner, not an AI algorithm trained on image data in the typical sense. Therefore, there is no "training set" of patient images as would be for a machine learning model. The system's "training" refers to its design, engineering, and testing against specifications and standards.

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

• Not applicable as there is no "training set" of patient images for an AI algorithm.

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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.

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The SIGNA Prime is a whole body magnetic resonance scanner designed to support high resolution, 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, 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 SIGNA Prime reflect the spatial distribution or molecular environment of nuclei exhibiting magnetic resonance.
These images and/or spectra when interpreted by a trained physician yield information that may assist in diagnosis.

SIGNA™ Prime is a whole body magnetic resonance scanner designed to support high resolution, high signal to-noise ratio, and short scan times. The systems use a combination of time-varying magnet 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 oblique planes, using various pulse sequences, imaging techniques and reconstruction algorithms. The system features a 1.5T superconducting magnet with 60cm bore size. The system is designed to conform to NEMA DICOM standards (Digital Imaging and Communications in Medicine).

The provided text is a 510(k) summary for the GE Healthcare SIGNA Prime magnetic resonance diagnostic device. The summary states that the device did not require clinical studies to support substantial equivalence and instead relied on non-clinical tests and sample clinical images to demonstrate acceptable diagnostic image performance.

Therefore, the study details requested cannot be fully provided as a formal comparative effectiveness study or standalone performance study as typically understood for AI/CADe devices was not conducted with predefined acceptance criteria for diagnostic metrics.

Here's a breakdown of the available information:

1. Table of Acceptance Criteria and Reported Device Performance:

No specific numerical acceptance criteria for diagnostic performance (e.g., sensitivity, specificity, AUC) are provided in the document. The general acceptance criterion was that the image quality of the SIGNA Prime is "substantially equivalent" to that of the predicate device (SIGNA Creator, K143251).

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

• Sample Size: The document mentions "Sample clinical images have been included in this submission" but does not specify the number of images or cases used as a "test set."
• Data Provenance: Not specified. It's unclear if these were retrospective or prospective, or the country of origin.

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

• The document states that "These images and/or spectra when interpreted by a trained physician yield information that may assist in diagnosis." However, it does not specify the number or qualifications of experts who interpreted the "sample clinical images" for the purpose of demonstrating substantial equivalence. The mechanism for establishing ground truth for these sample images is not detailed.

4. Adjudication method for the test set:

• Not specified. Given that a formal clinical study with a detailed ground truth process is not described, an adjudication method for a test set is not mentioned.

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. The document explicitly states: "The subject of this premarket submission, the SIGNA™ Prime, did not require clinical studies to support substantial equivalence." Therefore, an MRMC comparative effectiveness study involving human readers with and without AI assistance was not performed or reported.

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

• The SIGNA Prime is a magnetic resonance scanner, not an AI algorithm or CADe device. Therefore, the concept of "standalone (algorithm only without human-in-the-loop performance)" is not applicable in this context. The device itself produces images for human interpretation.

7. The type of ground truth used:

• For the "sample clinical images" used to demonstrate acceptable diagnostic image performance, the ground truth is implicitly expert interpretation by a "trained physician" as stated in the Indications for Use. However, the exact methodology for establishing this ground truth for the purpose of the submission is not detailed (e.g., expert consensus vs. pathology vs. outcomes data).

8. The sample size for the training set:

• Not applicable. The SIGNA Prime is a hardware device (MRI scanner) with associated software, not an AI/ML algorithm that is "trained" on a dataset in the typical sense. While the software platform and reconstruction algorithms were likely developed and refined, the document does not describe a "training set" in the context of an AI algorithm evaluation.

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

• Not applicable. (See point 8).

Summary of the Study:

The K211980 submission for the SIGNA Prime focused on demonstrating substantial equivalence to a predicate device (SIGNA Creator, K143251). This was primarily achieved through:

• Non-clinical tests: Compliance with various electrical, safety, software, and biocompatibility standards (e.g., ANSI/AAMI ES60601-1, IEC 60601-1-2, IEC 60601-2-33, IEC 62304, IEC 60601-1-6, IEC 62366-1, ISO 10993-1, NEMA MS, NEMA PS3 DICOM).
• Risk management activities: Including risk analysis, design reviews, and various levels of testing (unit, integration, performance, simulated use).
• Sample clinical images: These images were provided to demonstrate acceptable diagnostic performance and visual equivalence to the predicate device. However, the specific methodology for selecting, evaluating, or establishing ground truth for these sample images is not detailed, nor are any quantitative metrics provided for their performance.

The submission explicitly states that clinical studies were not required to support substantial equivalence. Therefore, the detailed information typically sought for the evaluation of AI/CADe devices (such as sample sizes for test/training, expert qualifications, adjudication methods, or MRMC study results) is not present in this document.

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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.

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The SIGNA Voyager is a whole body magnetic resonance scanner designed to support high resolution, high signal-to-noise ratio, and short scan time imaging. The SIGNA Voyager is indicated for use as a diagnostic imaging device to produce axial, sagittal, coronal, and oblique images, spectroscopic images and/or spectra, dynamic images, and parametric maps of the internal structures and organs of the entire body. Body structures for evaluation include, but are not limited to: head, neck, TMJ, 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 Voyager reflect the spatial distribution and/or molecular environment of nuclei exhibiting magnetic resonance. These images and/or spectra when interpreted by a trained physician yield information that may assist in diagnosis.

The SIGNA Voyager system is a whole body magnetic resonance scanner designed to support high resolution, high signal-to-noise ratio, and short scan times. 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 and oblique planes, using various pulse sequences and reconstruction algorithms. The system is offered as a new system installation, in either a fixed or a mobile configuration. The system features a 1.5T superconducting magnet with a 70cm bore size.

This document, K192426, is a 510(k) premarket notification for a magnetic resonance diagnostic device (MRI scanner), the SIGNA Voyager / SIGNA Voyager Quantum, submitted by GE Healthcare (Tianjin) Company Limited.

Based on the provided text, there is no specific information regarding acceptance criteria and a study that proves the device meets those criteria in the context of an AI/algorithm performance study. The document is a 510(k) summary focusing on demonstrating substantial equivalence to a predicate device (SIGNA Voyager K161567) and does not detail the evaluation of an AI algorithm's performance with respect to specific diagnostic criteria.

Instead, the summary emphasizes:

• Compliance with voluntary standards: ANSI/AAMI ES60601-1, IEC 60601-1-2, IEC 60601-2-33, and applicable NEMA MS standards for MRI.
• Quality assurance measures: Risk Analysis, Requirements Reviews, Design Reviews, Unit-level testing, Integration testing, Performance testing, Safety testing, and Simulated use testing.
• Lack of clinical studies required for substantial equivalence: "The subject of this premarket submission, SIGNA Voyager, did not require clinical studies to support substantial equivalence."
• Human subject scanning for design validation: "Scanning of human subjects on the SIGNA Voyager system has been conducted at GE Healthcare facilities as part of design validation activities in order to ensure that the modified device meets user requirements." This is for validating the device's performance (e.g., image quality, signal-to-noise ratio, scan times), not for evaluating an AI's diagnostic accuracy or impact on human reader performance.

Therefore, I cannot populate the requested tables and information about AI algorithm performance, as the provided document does not describe such a study or related acceptance criteria. The 510(k) is for the MRI hardware itself and its modifications, not an AI diagnostic adjunct.

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