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The SIGNA Premier 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, 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 Premier system 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.

SIGNA Premier is a whole-body magnetic resonance scanner featuring a 3.0T superconducting magnet with a 70cm bore size. Major elements of the system include the magnet, gradient coils, body RF transmit coil, RF receive subsystem, patient support system (table), host computer, and system software. The system is compatible with a suite of RF receive coils, and is capable of using various pulse sequences, imaging techniques and reconstruction algorithms.

This submission is prompted by the introduction of a new software feature called AIR Recon DL onto the SIGNA Premier system. AIR Recon DL is a deep-learning based reconstruction technique designed to improve signal-to-noise ratio (SNR) and image sharpness. The feature also enables shorter scan times while preserving SNR and image sharpness.

The addition of the AIR Recon DL feature involved modifications to the SIGNA Premier system software. There were no changes related to AIR Recon DL to the system's hardware components.

The provided text describes the acceptance criteria and supporting studies for the SIGNA Premier system with the AIR Recon DL feature. However, it does not explicitly provide a table of acceptance criteria with reported device performance or all the specific details requested in question points 2 through 9.

Based on the available information, here's what can be extracted:

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Test Set Sample Size: The document mentions "objective measures of in vivo images" and a "reader evaluation study" on "images acquired across a variety of pulse sequences and anatomies," and "sample images from clinically indicated scans." However, the specific number of images or patient cases used for these test sets is not provided.
• Data Provenance: The document states "in vivo images" and "clinically indicated scans." This implies retrospective clinical data, but the country of origin is not specified.

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

• Number of Experts: The document states "Radiologists were asked to perform blinded reads" and "Radiologists were asked to rate the images." The specific number of radiologists/experts involved is not provided.
• Qualifications of Experts: The experts are identified as "Radiologists," but their specific qualifications (e.g., years of experience, subspecialty) are not provided.

4. Adjudication method for the test set:

• The document implies that radiologists provided ratings and comments, and the results were aggregated to conclude on preference and image quality. However, a formal adjudication method like "2+1" or "3+1" to establish a consensus ground truth among multiple readers is not explicitly stated or described. The reads were "blinded," but it doesn't detail how discrepancies were resolved or if there was a consensus process.

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:

• MRMC Study: A "reader evaluation study" was performed where "Radiologists were asked to perform blinded reads of both AIR Recon DL images and images without AIR Recon DL." This indicates a comparative reading study was conducted. Also, "Comparisons were also made between AIR Recon DL images from shorter scan time acquisitions and images without AIR Recon DL taken with longer scan times."
• Effect Size: The document states that the results "confirmed that the AIR Recon DL feature provides images with equivalent or better image quality in terms of the legibility of clinically relevant structures." It also notes "the radiologists reading the images also indicated a preference for the AIR Recon DL images." However, a specific quantifiable effect size measuring how much human readers improve (e.g., in terms of diagnostic accuracy, confidence, or reading time) with AI assistance compared to without it is not provided. The improvement is described qualitatively (equivalent or better image quality, preference).

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

• The nonclinical testing on a "digital reference object and phantom imaging" evaluated the algorithm's impact on image quality metrics (SNR, sharpness, low contrast detectability, noise spectral content, bias, motion artifacts). This constitutes a standalone performance assessment of the algorithm's effects on image characteristics.

7. The type of ground truth used:

• For nonclinical testing (phantoms), the ground truth is known physical properties or measurements of the reference object/phantom.
• For clinical testing, the ground truth is established through expert consensus/ratings by radiologists. The document refers to "legibility of clinically relevant structures" and "lesion conspicuity" being maintained, relying on expert interpretation rather than pathology or long-term outcomes data.

8. The sample size for the training set:

• The document describes the AIR Recon DL feature as "a deep-learning based reconstruction technique." However, it does not provide any information regarding the sample size of the training set used to develop this deep learning model.

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

• As the document does not provide information about the training set (sample size or data), it does not describe how the ground truth for the training set was established.

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The SIGNA Premier system 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, TMJ, spine, breast, heart, abdomen, pelvis, joints, prostate, blood vessels, and musculoskeletal regions of the region of interest being imaged, contrast agents may be used.

The images produced by the SIGNA Premier system 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 Premier is a whole body magnetic resonance scanner designed to support high resolution, high signal-to-noise ratio, and short scan times, and is designed for improved patient comfort and workflow. The system features a 3.0T superconducting magnet with a 70cm bore size and can image in the sagittal, coronal, axial, oblique, and double oblique planes, using various pulse sequences, imaging techniques and reconstruction algorithms. The system is designed to conform to NEMA DICOM standards (Digital Imaging and Communications in Medicine). The modifications to this system include the AIRx software features, which allows users the flexibility to automate and standardize a number of connected steps required for an MRI examination of the brain.

This document describes a 510(k) premarket notification for the GE Healthcare SIGNA Premier magnetic resonance diagnostic device, specifically focusing on the addition of the AIRx software feature. The AIRx feature automates and standardizes aspects of MRI brain examinations using deep learning algorithms.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of acceptance criteria with corresponding performance metrics for the AIRx feature in terms of diagnostic accuracy (e.g., sensitivity, specificity, accuracy for a specific pathology). Instead, the performance is described in terms of "productivity and consistency benefits" and maintaining the imaging performance of the predicate device.

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

The document mentions "Internal scans were conducted as part of validation for AIRx workflow." However, it does not specify the sample size (number of patients or scans) used for this clinical testing. The data provenance is also not explicitly stated in terms of country of origin or whether it was retrospective or prospective, beyond being "internal scans."

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

The document does not provide any information regarding the number of experts or their qualifications used to establish ground truth for the clinical test set. The focus is on the automation and standardization capabilities of AIRx, and the claim is that the MR system's imaging performance remains the same as the predicate, which presumably means human interpretation of the resulting images is still the primary diagnostic method.

4. Adjudication Method for the Test Set

The document does not describe any adjudication method for the clinical test set.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance

The document does not describe an MRMC comparative effectiveness study and therefore does not report any effect size of human readers improving with or without AI assistance. The AIRx feature appears to be a "pre-scan algorithm" designed to improve workflow and consistency before image interpretation, rather than directly assisting in the diagnostic interpretation by a human reader in an MRMC setting.

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

The document states, "The AIRx feature is a pre-scan algorithm," and its purpose is to "automate and standardize a number of connected steps required for an MRI examination of the brain." This suggests that the algorithm's performance is in setting up the scan, and the final interpretation is still by a "trained physician." While "bench testing" and "non-clinical tests" were performed for the software, it's not clear if a standalone performance evaluation in terms of diagnostic accuracy (e.g., for detecting specific pathologies) was conducted without a human interpreting the resulting images. The statement "The MR System maintains the same imaging performance results as its predicate device (K171128)" implies that the imaging capability is maintained, and not that AIRx performs diagnosis standalone.

7. The Type of Ground Truth Used (Expert Consensus, Pathology, Outcomes Data, etc.)

The document does not explicitly state the type of ground truth used for the "internal scans" validating the AIRx workflow. Given the focus on "productivity and consistency benefits" in scan prescription, the ground truth might relate to objective measures of scan parameter consistency, successful automation of steps, or adherence to a predefined scan protocol, rather than definitive diagnostic outcomes like pathology.

8. The Sample Size for the Training Set

The document states, "AIRx was developed using deep learning algorithms." However, it does not provide the sample size used for the training set for these deep learning algorithms.

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

The document does not describe how the ground truth for the training set was established for the deep learning algorithms used in AIRx.

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The SIGNA(TM) Premier 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, 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(TM) Premier system 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.

SIGNAT™ Premier is a whole body magnetic resonance scanner designed to support high resolution, high signal-to-noise ratio, and short scan times, and is designed for improved patient comfort and workflow. The system features a 3.0T superconducting magnet with a 70cm bore size and can image in the sagittal, coronal, axial, oblique, and double oblique planes, using various pulse sequences, imaging techniques and reconstruction algorithms. The system is designed to conform to NEMA DICOM standards (Digital Imaging and Communications in Medicine).

The provided text is related to the FDA's 510(k) premarket notification for the GE Medical Systems, LLC (GE Healthcare) SIGNA™ Premier device, a Magnetic Resonance Diagnostic Device.

The submission focuses on establishing substantial equivalence to a predicate device (SIGNA™ Architect, K163331) rather than conducting a de novo study with strict acceptance criteria and a detailed study proving the device meets them. Therefore, many of the requested elements (like specific numerical acceptance criteria, comprehensive device performance against these, ground truth establishment for training/test sets, MRMC studies, effect sizes, etc.) are not explicitly stated or detailed in this 510(k) summary.

The primary method to demonstrate equivalence here is through non-clinical testing (bench testing, compliance with standards) and sample clinical images to show acceptable diagnostic image performance.

Here's an attempt to answer the questions based on the provided text, highlighting what is present and what is absent:

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

The document does not provide a specific table of numerical acceptance criteria for image quality parameters. Instead, it states the overall conclusion regarding 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 mentions "Sample clinical images have been included in this submission" but does not specify the sample size for this clinical image test set. It also does not provide information on the data provenance (e.g., country of origin, retrospective or prospective status).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

The document does not describe the establishment of a formal "ground truth" for the sample clinical images. It states, "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 of experts or their qualifications who evaluated the "sample clinical images" to determine their diagnostic acceptability or equivalence.

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

The document does not specify any adjudication method for the clinical image evaluation.

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 was mentioned or performed. This device is a diagnostic imaging system (MRI scanner), not an AI-assisted diagnostic tool for interpretation. Therefore, the concept of "human readers improve with AI vs without AI assistance" is not directly applicable to this submission.

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

This question is not applicable. The SIGNA™ Premier is an MRI scanner, a hardware device that produces images. It is not an algorithm for image interpretation that would have standalone performance. Its performance relates to the quality of the images it generates, which are then interpreted by a human physician.

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

The document does not describe the use of formal ground truth (e.g., expert consensus, pathology, or outcomes data) for evaluating the sample clinical images. The evaluation appears to be based on the general diagnostic acceptability of the images by "trained physician[s]" as per FDA guidance for MR diagnostic devices.

8. The sample size for the training set

The document does not mention or describe a training set. As a hardware device (MRI scanner) rather than an AI/ML algorithm, a "training set" in the traditional sense is not applicable for its performance evaluation for regulatory submission.

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

Not applicable, as no training set is described.

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