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The SIGNA™ Sprint 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 body. Depending on the region of interest being imaged, contrast agents may be used.

The images produced by SIGNA™ Sprint 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™ Sprint 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).

Key aspects of the system design:

• Uses the same magnet as a conventional whole-body 1.5T system, with integral active shielding and a zero boil-off cryostat.
• A gradient coil that achieves up to 65 mT/m peak gradient amplitude and 200 T/m/s peak slew rate.
• An embedded body coil that reduces thermal and enhance intra-bore visibility.
• A newly designed 1.5T AIR Posterior Array.
• A detachable patient table.
• A platform software with various PSD and applications, including the following AI features:
  • AIRx™ (Cleared in K183231)
  • AIR™ Recon DL (Cleared in K202238)
  • Sonic DL™ (Cleared in K223523)

The provided text is a 510(k) clearance letter and summary for a new MRI device, SIGNA™ Sprint. It states explicitly that no clinical studies were required to support substantial equivalence. Therefore, the information requested regarding acceptance criteria, study details, sample sizes, ground truth definitions, expert qualifications, and MRMC studies is not available in this document.

The document highlights the device's technical equivalence to a predicate device (SIGNA™ Premier) and reference devices (SIGNA™ Artist, SIGNA™ Champion) and relies on non-clinical tests and sample clinical images to demonstrate acceptable diagnostic performance.

Here's a breakdown of what can be extracted from the document regarding testing, and why other requested information is absent:

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

• Acceptance Criteria (Implicit): The document states that the device's performance is demonstrated through "bench testing and clinical testing that show the image quality performance of SIGNA™ Sprint compared to the predicate device." It also mentions "acceptable diagnostic image performance... in accordance with the FDA Guidance 'Submission of Premarket Notifications for Magnetic Resonance Diagnostic Devices' issued on October 10, 2023."
  • Specific quantitative acceptance criteria (e.g., minimum SNR, CNR, spatial resolution thresholds) are not explicitly stated in this document.
• Reported Device Performance: "The images produced by SIGNA™ Sprint 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."
  • No specific quantitative performance metrics (e.g., sensitivity, specificity, accuracy, or detailed image quality scores) are provided in this regulatory summary. The statement "The image quality of the SIGNA™ Sprint is substantially equivalent to that of the predicate device" is the primary performance claim.

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

• Test Set Sample Size: Not applicable/Not provided. The document explicitly states: "The subject of this premarket submission, the SIGNA™ Sprint, did not require clinical studies to support substantial equivalence."
• Data Provenance: Not applicable/Not provided for a formal clinical test set. The document only mentions "Sample clinical images have been included in this submission," but does not specify their origin or nature beyond being "sample."

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)

• Not applicable. Since no formal clinical study was conducted for substantial equivalence, there was no "test set" requiring ground truth established by experts in the context of an effectiveness study. The "interpretation by a trained physician" is mentioned in the Indications for Use, which is general to MR diagnostics, not specific to a study.

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

• Not applicable. No clinical test set requiring adjudication was conducted for substantial equivalence.

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™ Sprint, did not require clinical studies to support substantial equivalence." While the device incorporates AI features cleared in other submissions (AIRx™, AIR™ Recon DL, Sonic DL™), this specific 510(k) for the SIGNA™ Sprint system itself does not include an MRMC study or an assessment of human reader improvement with these integrated AI features. The focus is on the substantial equivalence of the overall MR system.

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

• No, not for the SIGNA™ Sprint as a whole system. This 510(k) is for the MR scanner itself, not for a standalone algorithm. Any standalone performance for the integrated AI features (AIRx™, AIR™ Recon DL, Sonic DL™) would have been part of their respective clearance submissions (K183231, K202238, K223523), not this one.

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

• Not applicable. No formal clinical study requiring ground truth was conducted for this submission.

8. The sample size for the training set

• Not applicable/Not provided. This submission is for the SIGNA™ Sprint MR system itself, not a new AI algorithm requiring a training set developed for this specific submission. The AI features mentioned (AIRx™, AIR™ Recon DL, Sonic DL™) were cleared in previous 510(k)s and would have had their own training and validation processes.

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

• Not applicable/Not provided. As explained in point 8, this submission does not detail the training of new AI algorithms.

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AIR Recon DL is a deep learning based reconstruction technique that is available for use on GE Healthcare 1.5T, 3.0T, and 7.0T MR systems. AIR Recon DL reduces noise and ringing (truncation artifacts) in MR images, which can be used to reduce scan time and improve image quality. AIR Recon DL is intended for use with all anatomies, and for patients of all ages. Depending on the anatomy of interest being imaged, contrast agents may be used.

AIR Recon DL is a software feature intended for use with GE Healthcare MR systems. It is a deep learning based reconstruction technique that removes noise and ringing (truncation) artifacts from MR images. AIR Recon DL is an optional feature that is integrated into the MR system software and activated through a purchasable software option key.

Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided FDA 510(k) summary for AIR Recon DL:

1. Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly defined by the desired improvements and non-inferiority claims of the AIR Recon DL feature. The reported device performance demonstrates how these criteria were met.

2. Sample Size and Data Provenance

• Test Set Sample Size: 133 cases.
  • 129 patient cases
  • 4 healthy subjects
• Data Provenance:
  • Country of Origin: Not explicitly stated, but "10 different clinical sites" suggests a multi-center study, and "a GE Healthcare facility" could indicate a US or international site. Given the FDA submission, it's highly likely to include US data.
  • Retrospective or Prospective: Not explicitly stated, but the description "images acquired across a variety of pulse sequences and anatomies" and involvement of "10 different clinical sites" could imply a retrospective collection for the reader study, where images were pre-collected. However, without explicit mention, it's not definitive. The phrasing "acquired from the same acquired raw data" suggests a paired comparison based on existing data.

3. Number of Experts and Qualifications

• Number of Experts: Three radiologists.
• Qualifications: "Radiologists" implies medical doctors specialized in radiology. No further specifics on years of experience or subspecialty were provided.

4. Adjudication Method for the Test Set

The adjudication method appears to be 2+1 (or 3/3 agreement is ideal, but 2 out of 3 for consensus is common).
"Each image pair was evaluated independently by three radiologists."
"The results confirmed that the AIR Recon DL feature provides images with equivalent or better image quality in terms of apparent signal to noise ratio (133 out of 133 cases), sharpness (133 out of 133 cases), and lesion conspicuity (123 out of 124 cases with pathology)."
"The radiologists reading the images also indicated a preference for the AIR Recon DL images over conventional images in 99% of the evaluations."
This indicates that the claims are based on the collective agreement or majority opinion of the three readers for each case. The exact decision rule (e.g., simple majority, unanimous) is not stated, but the high consistency (e.g., 133/133, 123/124) implies strong agreement or effective resolution.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

Yes, a multi-reader multi-case (MRMC) comparative effectiveness study was done.

• Comparison: Radiologists compared AIR Recon DL images to conventional images (without AIR Recon DL) reconstructed from the same acquired raw data.
• Effect Size of Human Reader Improvement: The study demonstrates a significant preference and improvement in perceived image quality attributes by human readers when using AIR Recon DL assisted images compared to conventional images.
  • Preference: Radiologists preferred AIR Recon DL images over conventional images in 99% of evaluations. This indicates a strong positive effect on reader perception and diagnostic confidence.
  • Qualitative Improvement:
    • Equivalent or better SNR in 100% of cases (133/133).
    • Equivalent or better sharpness in 100% of cases (133/133).
    • Equivalent or better lesion conspicuity in ~99.2% of pathological cases (123/124).
  • Enablement of Shorter Scans: For shorter scan time acquisitions, AIR Recon DL images were rated as better or equivalent image quality in 100% of 22 image pairs, which suggests human readers are able to maintain or even improve their assessment quality despite reduced acquisition time. Overall, the effect size is very large and consistently positive across all measured subjective criteria.

6. Standalone (Algorithm Only) Performance

The document describes "nonclinical testing" on phantoms, which represents a form of standalone testing where the algorithm's output is directly measured against predefined physical criteria:

• "AIR Recon DL has undergone phantom testing to evaluate the feature and its impact on image quality, including SNR, sharpness, and low contrast detectability."
• "The nonclinical testing demonstrated that AIR Recon DL does improve SNR and image sharpness while maintaining low contrast detectability."
• "ADC maps were not adversely impacted by the use of AIR Recon DL."
  This evaluates the algorithm's effect on image characteristics absent human interpretation of clinical cases.

7. Type of Ground Truth Used

The study primarily used expert consensus (radiologist agreement) as the ground truth for evaluating image quality attributes (SNR, sharpness, lesion conspicuity, overall preference) and the impact of the algorithm.
For the "presence of pathology" in the test set, it's assumed that this was either identified beforehand from clinical reports or pathology (e.g., biopsy results) or by consensus among the evaluating radiologists prior to their evaluation of the AI-enhanced images. However, the exact source of ground truth for pathology presence/absence isn't detailed, only that 124 cases "with pathology" and 9 cases "without pathology" were included.

8. Sample Size for the Training Set

The document states: "Both the proposed AIR Recon DL and the predicate device use neural networks that have similar architecture, and were trained using similar methods and data."
However, the specific sample size for the training set is NOT provided in this summary.

9. How Ground Truth for Training Set was Established

The document states: "Both the proposed AIR Recon DL and the predicate device use neural networks that have similar architecture, and were trained using similar methods and data."
The method for establishing ground truth for the training set is NOT explicitly detailed. Typically, for deep learning reconstruction, the "ground truth" during training often refers to high-quality, fully sampled MR images (or images from a prior, high-quality reconstruction method) that the AI attempts to match or improve upon, rather than a clinical diagnosis per se. The goal during training would be to generate images that are less noisy and sharper while preserving underlying anatomical and pathological information, learned by comparing "corrupted" (e.g., undersampled, noisy) inputs to "clean" reference images.

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