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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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