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The SIGNA 7.0T system is a whole-body magnetic resonance scanner designed to support high signal-tonoise 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 head and extremities.

The images produced by the SIGNA 7.0T system reflects 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.

The device is intended for patients > 20 kg / 44 lb.

SIGNA 7.0T is a high performance magnetic resonance imaging system designed to support high resolution imaging at 7.0T in particular anatomical regions determined by the available RF coils. The system includes a 7.0T superconducting magnet and an ultra-high performance gradient coil with a 60 cm patient bore, supporting scanning in axial, coronal, sagittal, oblique, and double oblique planes using a variety of pulse sequences, imaging techniques, acceleration methods, and reconstruction algorithms.

This 510(k) submission is for the SIGNA 7.0T MR System, and has been triggered by the addition of the AIR Recon DL software feature and inclusion of installed base magnet system upgrades.

The provided text describes a 510(k) submission for the GE SIGNA 7.0T MRI system, specifically focusing on the addition of the AIR Recon DL software feature. While it discusses performance testing and a reader study, it does not explicitly define specific numerical acceptance criteria for the device's performance in a table format, nor does it provide detailed quantitative results against such criteria. The document states that "The nonclinical testing passed the defined acceptance criteria," but these criteria are not enumerated.

However, based on the provided text, we can infer and report on the study details as much as possible:

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

As stated above, no explicit numerical acceptance criteria table is provided in the document. The text broadly states:

• "The nonclinical testing passed the defined acceptance criteria, and did not identify any adverse impacts to image quality or other concerns related to safety and performance."
• "The nonclinical testing demonstrated that AIR Recon DL does improve SNR and image sharpness while maintaining low contrast detectability."
• "Objective measures of in vivo images were analyzed to confirm that AIR Recon DL improves SNR and image sharpness for typical clinical use cases."
• "This study showed that AIR Recon DL feature provides images with better SNR and equivalent or better sharpness."
• "The radiologists uniformly preferred the AIR Recon DL images for clinical evaluation."

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

• Test Set Sample Size: Not explicitly stated for either the non-clinical or clinical tests. The text mentions "in vivo images" for clinical analysis and "typical clinical use cases," implying a dataset was used, but the size is absent.
• Data Provenance: Not explicitly stated (e.g., country of origin, retrospective/prospective).

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

• Number of Experts: "Radiologists" (plural), but the exact number is not specified.
• Qualifications of Experts: Only stated as "trained physician" for general image interpretation within the Indications for Use, and "Radiologists" for the reader study. No specific experience levels (e.g., years of experience) are provided for the radiologists in the study.

4. Adjudication method for the test set

• Adjudication Method: Not explicitly stated. The text says "Radiologists were asked to rate the images, and to comment on any notable aspects related to image quality," and that they "uniformly preferred" the AIR Recon DL images. This implies a consensus or preference-based evaluation rather than a formal adjudicated ground truth establishment process for specific findings.

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: Yes, a reader study was performed comparing images "with and without AIR Recon DL feature."
• Effect Size: No quantitative effect size (e.g., magnitude of improvement in diagnostic accuracy, AUC, or other metrics) is provided for how much human readers improved. The improvement is described qualitatively: "The radiologists uniformly preferred the AIR Recon DL images for clinical evaluation." and that images had "better SNR and equivalent or better sharpness."

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

• Standalone Performance: The document describes "nonclinical testing" and "objective measures of in vivo images" where AIR Recon DL's impact on SNR and sharpness was evaluated. This implies an algorithmic evaluation independent of human readers, where the algorithm processes images and its output is analyzed for objective metrics (SNR, sharpness, low contrast detectability). However, specific performance metrics for this standalone performance (e.g., a specific SNR improvement percentage, or sharpness metric) are not quantified in the text.

7. The type of ground truth used

• Clinical Study: For the reader study, the ground truth appears to be based on the radiologists' preference and qualitative assessment of image quality (SNR, sharpness, low contrast detectability) for clinical evaluation. It's not explicitly stated as a definitive "diagnosis" ground truth derived from pathology or outcomes data.
• Non-Clinical/Objective Measures: For objective measures, the "ground truth" aligns with quantifiable physical properties like Signal-to-Noise Ratio (SNR) and image sharpness, which are inherent characteristics of the image output itself. Low contrast detectability was also assessed.

8. The sample size for the training set

• Training Set Sample Size: Not specified in the provided text. The text mentions that "AIR Recon DL has been previously cleared for use with GE Healthcare's 3T SIGNA Premier system through K193282" and that "Due to the technical similarities, SIGNA Premier (K193282) is used as a reference device for this submission." This implies that the algorithm was likely trained on a separate dataset prior to this specific submission, but details of that training set are not in this document.

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

• Training Set Ground Truth Establishment: Not specified in the provided text.

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The SIGNA 7.0T System is a whole-body magnetic resonance scanner designed to support high signal-tonoise 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 head and extremities.

The images produced by the SIGNA 7.0T 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.

The device is intended for patients > 20 kg / 44 lbs.

SIGNA 7.0T is a high performance magnetic resonance imaging system designed to support high resolution imaging at 7.0T in particular anatomical regions determined by the available RF coils. The system includes a 7.0T superconducting magnet and an ultra-high performance gradient coil with a 60 cm patient bore, supporting scanning in axial, coronal, sagittal, oblique, and double oblique planes using a variety of pulse sequences, imaging techniques, acceleration methods, and reconstruction algorithms.

Here's an analysis of the acceptance criteria and the study conducted for the GE SIGNA 7.0T Magnetic Resonance Diagnostic Device, based on the provided text:

Important Note: The provided text is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device rather than presenting a detailed clinical study for novel claims or specific performance metrics against pre-defined acceptance criteria for a new AI-powered diagnostic. Therefore, some of the requested information (like specific quantitative acceptance criteria for image quality, expert qualifications beyond "radiologist," or a detailed MRMC study effect size) is not explicitly present in the given document. The analysis below extracts what is available and makes inferences where appropriate based on the nature of a 510(k) submission for an imaging device.

1. Table of Acceptance Criteria and Reported Device Performance

Explanation of Implied Acceptance Criteria: The document describes the study's purpose as evaluating image diagnostic quality and usability against the predicate device. For a 510(k), the acceptance criterion for image quality is generally that the new device's images are diagnostically equivalent or non-inferior to the predicate device's images for the stated indications for use. "Acceptance" here means that the FDA agreed that the device met the requirements for substantial equivalence.

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

• Sample Size for Image Quality Study (Test Set):
  • Subjects: Not explicitly stated, but images were acquired from "normal subjects and subjects with self-reported common neuropathology" for brain scans, and individuals over a "broad age range representative of a range of knee health" for knee images. The specific number of subjects/cases is not provided.
  • Image Series: Included brain MR scans and knee images.
• Sample Size for PNS Limits Study: Not explicitly stated, but involved "adult human volunteers without reported pathology."
• Data Provenance: Not explicitly stated, but given it's a US submission and refers to "U.S. board-certified radiologists," it is highly probable the data was collected in the United States. The study was prospective in the sense that the images were acquired specifically for this evaluation using both the proposed and predicate devices.

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

• Number of Experts: 4
• Qualifications: "U.S. board-certified radiologists." No specific years of experience are provided, but board certification implies a certain level of expertise.

4. Adjudication Method for the Test Set

The document states, "The study involved 4 U.S. board-certified radiologists evaluating proposed device's image diagnostic quality and usability, personal preferences, and general commentary using radiology terms against same subject images scanned on the predicate device."

This description suggests that the radiologists independently evaluated images from both devices. It does not explicitly describe a formal adjudication method (like 2+1 or 3+1 consensus). It's more likely that their individual assessments were collected and analyzed, potentially looking for consistency or a majority opinion on diagnostic quality comparison. Without further detail, we cannot definitively state a formal adjudication method was used, beyond individual evaluations.

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

• MRMC Study: Yes, a reader evaluation study was performed. It involved multiple readers (4 radiologists) evaluating multiple cases (brain and knee images).
• Effect Size (AI Assistance): This device is an MR scanner, not an AI diagnostic algorithm. Therefore, the study did not involve human readers improving with AI assistance. The comparison was between the image quality from the new 7.0T MR scanner versus the predicate 3.0T MR scanner.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

• No, this question is not applicable. The SIGNA 7.0T is an imaging device, not an AI algorithm. No standalone algorithm performance was evaluated.

7. The Type of Ground Truth Used

For the image quality evaluation, the "ground truth" was established by expert consensus/opinion (or individual expert assessments, given the lack of explicit adjudication) regarding the diagnostic quality and usability of the images. They compared images from the proposed 7.0T system against the predicate 3.0T system. There is no mention of pathology, clinical outcomes, or other objective ground truth for the diagnostic findings themselves, but rather whether the images produced were suitable for diagnosis.

For the PNS limits, the ground truth was direct determination in human volunteers.

8. The Sample Size for the Training Set

• This device is an MR scanner, not an AI algorithm that requires a "training set" in the machine learning sense. Therefore, this question is not applicable. The device's "training" would refer to its engineering development and calibration, which is a different concept.

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

• This question is not applicable, as there is no "training set" in the context of an AI algorithm.

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