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510(k) Data Aggregation

    K Number
    K253780

    Validate with FDA (Live)

    Device Name
    SIGNA™ Bolt
    Manufacturer
    Ge Medical Systems, LLC
    Date Cleared
    2026-02-06

    (72 days)

    Product Code
    LNH,LNI,MOS
    Regulation Number
    892.1000
    Type
    Traditional
    Panel
    Radiology
    Age Range
    All
    Reference & Predicate Devices
    K223439,K193282
    Predicate For
    N/A
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticPediatricDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

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

    The images produced by the SIGNA™ Bolt 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.

    Device Description

    SIGNA™ Bolt 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 70 cm bore size and can image in the sagittal, coronal, axial, oblique, and double oblique planes, using various pulse sequences, imaging techniques and reconstruction algorithms. SIGNA™ Bolt is designed to conform to NEMA DICOM standards.

    The SIGNA™ Bolt system will be offered as two commercial configurations with the following features:

    • Magnet: 3.0T superconducting magnet with a wide (70 cm) bore size and active shielding
    • Maximum Gradient Strength: 80 mT/m (SuperXG Gradient), 65 mT/m (SuperXF Gradient)
    • Maximum Slew Rate: 200 T/m/s (SuperXG Gradient and SuperXF Gradient)
    • RF Transmit: A liquid cooled In-Scan-Room RF transmit architecture with a peak power capability of 36 kW and 3.0T Platform Body Coil
    • RF Receive Chain: 162 Ch available (SuperXG Gradient), 130 Ch available (SuperXF Gradient)
    • Patient Table: Detachable SIGNA One Patient Table with embedded 3.0T AIR PA XL coil and up to four 32-channel high density auto-coil sensing connection ports
    • Power Rating: 113 kVA (SuperXG Gradient), 90 kVA (SuperXF Gradient)
    • Software: Software platform featuring various productivity enhancement features, designed to improve workflow and reduce scan time
      • AIRx (previously cleared in K183231) – AI-based automated slice prescription tool now extended with new deep learning models for spine and prostate imaging
      • SIGNA One Camera – Real-time AI-enabled image guidance that assists with automated patient positioning
    • Gating Options: Wired, wireless, and contactless physiological gating options
    AI/ML Overview

    This document outlines the acceptance criteria and supporting studies for the SIGNA™ Bolt device, based on the provided FDA 510(k) clearance letter.

    Key Features and AI/ML Components of SIGNA™ Bolt:

    The SIGNA™ Bolt system includes several AI/Machine Learning components:

    1. AIRx: An AI-based automated slice prescription tool, previously cleared for brain and knee imaging (K183231), now extended with new deep learning models for spine and prostate imaging.
    2. SIGNA One Camera: Real-time AI-enabled image guidance that assists with automated patient positioning.
    3. Contactless Gating: This feature leverages underlying physiological signal detection that might involve advanced signal processing or AI techniques, though the document primarily describes its functional outcome.

    Acceptance Criteria and Reported Device Performance

    The following table summarizes the acceptance criteria and reported performance for the AI/ML components of the SIGNA™ Bolt device:

    Feature/ComponentAcceptance CriteriaReported Device Performance
    SIGNA One CameraLandmark Inference Accuracy: 90% successful detection of camera-predicted anatomical landmarks compared to ground truth annotations.Landmark Inference Accuracy: Achieved up to 99% successful detection across all evaluated anatomical regions.
    Landmark Acceptance (with obstructions): 95% success rate.Landmark Acceptance (with obstructions): Achieved 97% success rate.
    AIRx SpineAll deep learning models met their predefined acceptance criteria (specific criteria not detailed, but implied to be related to accuracy, variability reduction, and successful adaptation to spinal curvatures and complex scan setups).Model Performance: All models met their predefined acceptance criteria.
    Reduced scan prescription times and minimized inter-operator variability compared to manual workflows.Demonstrated reduced scan prescription times and minimized inter-operator variability (confirmed by SSIM analysis and visual comparisons). Successfully adapted prescriptions to patient-specific spinal curvatures and automated Pars Interarticularis and Cervical Foramina scans.
    AIRx ProstateAll deep learning models met predefined acceptance criteria (specific criteria not detailed, but implied to be related to accuracy and robustness to variations in anatomy, pathology, and implants).Model Performance: All models met predefined acceptance criteria, confirming robustness to variations in anatomy, pathology, and presence of implants.
    Contactless GatingAccurately detecting and displaying respiratory and peripheral cardiac waveforms without physical accessories. Supporting use of these waveforms for triggering MR acquisitions across multiple anatomical regions.Verified and validated to accurately detect and display respiratory and peripheral cardiac waveforms without physical accessories. Supports use of these waveforms for triggering MR acquisitions across multiple anatomical regions (meeting performance specifications).

    Study Details for AI/ML Components:

    1. SIGNA One Camera

    • Sample Size for Test Set: Data collected from 80 volunteers.
    • Data Provenance: US and China (to ensure diverse datasets).
    • Number of Experts & Qualifications for Ground Truth: Not explicitly stated for this component. Ground truth is described as "MR system coordinates of the camera-predicted anatomical landmarks against ground truth annotations," suggesting a technical or measurement-based ground truth rather than expert reads.
    • Adjudication Method: Not specified.
    • MRMC Comparative Effectiveness Study: Yes, a "time on task study" was conducted with 11 MR Scan Operators comparing the AI-powered workflow to conventional laser landmarking.
      • Effect Size: The camera workflow "consistently enabled faster setup times for landmarking." Specific quantitative improvement (e.g., % reduction in time) is not provided in text.
    • Standalone Performance: Yes, "Accuracy was evaluated by comparing the MR system coordinates of the camera-predicted anatomical landmarks against ground truth annotations." This indicates an algorithm-only evaluation.
    • Type of Ground Truth: MR system coordinates.
    • Sample Size for Training Set: Not explicitly stated, but the test dataset was "entirely separate from the training and validation datasets."
    • Ground Truth for Training Set: Not specified, but likely established in a similar manner to the test set (MR system coordinates or similar technical measurements).

    2. AIRx Spine

    • Sample Size for Test Set: 376 subjects.
    • Data Provenance: Multiple clinical sites and internal GE HealthCare sites.
    • Number of Experts & Qualifications for Ground Truth: Not explicitly stated. Ground truth is implied to be established for "accurate multi-slice, multi-angle prescriptions."
    • Adjudication Method: Not specified.
    • MRMC Comparative Effectiveness Study: Yes, "Comparative studies demonstrated that AIRx Spine reduced scan prescription times compared to manual workflows and minimized inter-operator variability."
      • Effect Size: "Reduced scan prescription times" and "minimized inter-operator variability" (confirmed by Structural Similarity Index (SSIM) analysis and visual comparisons). Specific quantitative improvement is not provided.
    • Standalone Performance: Yes, "Performance testing was conducted on the AIRx Spine deep learning models," indicating an algorithm-only evaluation.
    • Type of Ground Truth: Not explicitly stated but implied to be based on accurate anatomical prescriptions suitable for diagnostic imaging. SSIM analysis and visual comparisons suggest a comparison against an ideal or expert-defined prescription.
    • Sample Size for Training Set: Not explicitly stated, but the test dataset was "held separate from training and validation data."
    • Ground Truth for Training Set: Not specified, but likely established to enable the model to learn "patient-specific spinal curvatures" and "accurate multi-slice, multi-angle prescriptions."

    3. AIRx Prostate

    • Sample Size for Test Set: 785 exams.
    • Data Provenance: Clinical sites in the US and Europe.
    • Number of Experts & Qualifications for Ground Truth: Not explicitly stated.
    • Adjudication Method: Not specified.
    • MRMC Comparative Effectiveness Study: Not explicitly mentioned for this specific feature in the provided text.
    • Standalone Performance: Yes, "Performance testing was conducted on the six deep learning models that comprise the AIRx Prostate feature," evaluating automated prostate scan plane prescription, indicating an algorithm-only evaluation.
    • Type of Ground Truth: Not explicitly stated but implied to be based on accurate anatomical prescriptions for the prostate, using SSFSE localizer images.
    • Sample Size for Training Set: Not explicitly stated, but the test dataset was "kept separate from the training and validation data."
    • Ground Truth for Training Set: Not specified, but likely established to enable the model to learn "automated prostate scan plane prescription."

    4. Contactless Gating

    • Sample Size for Test Set: Not explicitly stated for this particular feature's performance validation.
    • Data Provenance: Not specified.
    • Number of Experts & Qualifications for Ground Truth: Not specified.
    • Adjudication Method: Not specified.
    • MRMC Comparative Effectiveness Study: Not mentioned.
    • Standalone Performance: Yes, "Verification and validation testing confirmed that the contactless gating feature meets its performance specifications by accurately detecting and displaying respiratory and peripheral cardiac waveforms," indicating a system performance evaluation.
    • Type of Ground Truth: Underlying physiological waveforms (respiratory and cardiac).
    • Sample Size for Training Set: Not specified.
    • Ground Truth for Training Set: Not specified, but likely established from physiological signal data.

    Overall Conclusion from Performance Testing:

    GE HealthCare concludes that the SIGNA™ Bolt is as safe and effective, with performance substantially equivalent to the predicate device, based on the nonclinical testing, including extensive software verification and validation, as well as specific performance evaluations for its new AI-enabled features. No clinical studies were required to support substantial equivalence.

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