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The SIGNA Hero 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 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, TMI, spine, 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 Hero 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™ Hero is a whole body magnetic resonance scanner designed to support high resolution, high signal to-noise ratio, and short scan times. The systems use 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 oblique planes, using various pulse sequences, imaging techniques and reconstruction algorithms. The system features a 3.0T superconducting magnet with 70cm bore size. The system is designed to conform to NEMA DICOM standards (Digital Imaging and Communications in Medicine).

The provided text describes information about the GE Healthcare SIGNA Hero MRI system (K213668). However, it does not contain details about specific acceptance criteria related to a specific diagnostic task (like detecting a particular disease), nor does it describe a study specifically designed to prove that the device meets such criteria for a diagnostic algorithm.

Instead, the document focuses on demonstrating substantial equivalence of the SIGNA Hero to its predicate device, the SIGNA Pioneer (K160621), as per FDA 510(k) premarket notification requirements.

Here's an analysis based on the provided text, addressing the requested points where information is available:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not provide a table of acceptance criteria for a specific diagnostic task or algorithm performance metrics (e.g., sensitivity, specificity, AUC). Instead, it states that the device was tested against safety and performance standards to demonstrate substantial equivalence to the predicate device.

Acceptance Criteria (General for MRI System Substantial Equivalence):

• Compliance with voluntary standards (ANSI/AAMI ES60601-1, IEC 60601-1-2, IEC 60601-2-33, IEC 62304, ISO 10993-1, IEC 62464-1).
• Compliance with NEMA standards (MS 3, MS 4, MS 8, PS3 for DICOM).
• Successful biocompatibility track record (ISO 10993-1 testing and history of patient contacting materials).
• Acceptable diagnostic image performance comparable to the predicate device in accordance with FDA Guidance "Submission of Premarket Notifications for Magnetic Resonance Diagnostic Devices."

Reported Device Performance:

• The SIGNA Hero and predicate device were subject to similar risk management testing.
• The SIGNA Hero complies with all listed standards.
• The SIGNA Hero has a successful biocompatibility track record.
• "The image quality of the SIGNA™ Hero is substantially equivalent to that of the predicate device."
• "The sample clinical images demonstrate acceptable diagnostic image performance of the SIGNA™ Hero..."
• The device performs as intended based on non-clinical tests.

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

The document mentions "sample clinical images" were included to demonstrate acceptable diagnostic image performance. However, it does not specify:

• The exact sample size of these clinical images.
• The provenance of the data (e.g., country of origin, retrospective or prospective).

3. Number of Experts Used to Establish Ground Truth and Qualifications

The document states that images are "interpreted by a trained physician" but does not specify:

• The number of experts involved in reviewing the "sample clinical images."
• The specific qualifications of these experts (e.g., radiologists with certain years of experience).

4. Adjudication Method for the Test Set

The document does not mention any adjudication method (e.g., 2+1, 3+1, none) for establishing ground truth or evaluating the clinical images.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study was Done

No, the document explicitly states: "The subject of this premarket submission, the SIGNA™ Hero, did not require clinical studies to support substantial equivalence." Therefore, an MRMC study comparing human readers with and without AI assistance was not performed or detailed in this submission.

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

This submission is for an MRI scanner, not a specific diagnostic algorithm or AI software. Therefore, the concept of "standalone performance" for an algorithm isn't directly applicable in the context of this document. The performance evaluation focuses on the image quality and safety of the MR system itself.

7. The Type of Ground Truth Used

For the "sample clinical images," the ground truth implicitly refers to the interpretation by a trained physician regarding the diagnostic image performance. Beyond this, for the system's overall performance and safety, ground truth is established through compliance with established industry standards and recognized risk management practices.

8. The Sample Size for the Training Set

The document describes the submission of an MR scanner, not an AI/ML algorithm that requires a distinct training set. Hence, there is no mention of a training set sample size.

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

As there is no mention of an algorithm or training set, this information is not applicable.

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The SIGNA Architect 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, breat, abdomen, pelvis, joints, prostate, blood vessels, and musculoskelatal regions of the region of interest being imaged, contrast agents may be used.

The images produced by the SIGNA Architect 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 Architect is a whole body magnetic resonance scanner designed to support high resolution, high signal-to-noise ratio, and short scan times. The system features a superconducting magnet. The data acquisition system accommodates up to 128 independent receive channels in various increments and multiple independent coil elements per channel during a single acquisition series. Each system uses a combination of time varying magnetic fields (gradients) and RF transmissions to obtain information regarding the density and position of elements exhibiting magnetic resonance. Each system can imaqe in the saqittal, coronal, axial, oblique, and double oblique planes, using various pulse sequences and reconstruction algorithms.

This submission is prompted by the introduction of two new software features called HyperSense 2.0 and Star onto SIGNA Architect. HyperSense 2.0 is an acceleration technique based on sparse data compressibility allowing scan time reduction while maintaining SNR efficiency. Star is a motion-robust, free-breathing imaging technique. HyperSense 2.0 is a modification to the previously cleared HyperSense, while Star is a technique that can be used with the previously cleared DISCO feature. Both HyperSense and DISCO are listed above as reference devices along with their associated 510(k) submission numbers.

The addition of both the HyperSense 2.0 and Star features involved modifications to the SIGNA Architect system software. There were no changes from either of these features that were related to the system's hardware components.

The provided text describes a 510(k) premarket notification for a medical device, the SIGNA Architect, a Magnetic Resonance (MR) system with new software features (HyperSense 2.0 and Star). Here's a breakdown of the acceptance criteria and study proving the device meets them, based on the information provided:

Disclaimer: The provided document is a 510(k) summary, which is a high-level overview. It does not contain detailed information about the specific acceptance criteria, statistical methodologies, or all aspects of the studies that would be present in the full submission. Therefore, some sections below will indicate "Not explicitly stated in the provided document."

Acceptance Criteria and Device Performance

The core acceptance criterion for a 510(k) submission is Substantial Equivalence (SE) to a legally marketed predicate device. This means the new device is as safe and effective as the predicate, and does not raise new questions of safety and effectiveness.

The document indicates that studies were performed to demonstrate that the new features (HyperSense 2.0 and Star) do not negatively impact image quality or diagnostic utility compared to the predicate/existing techniques.

Table of Acceptance Criteria and Reported Device Performance:

Study Details

The document mentions two main types of studies: non-clinical and clinical. The clinical evaluation focuses on the new features: HyperSense 2.0 and Star.

1. Sample Size Used for the Test Set and Data Provenance:

• HyperSense 2.0: "The images involved were generated using 3 different reconstruction techniques across different anatomies."
  • Sample Size: Not explicitly stated (e.g., number of patients/cases, number of images).
  • Data Provenance: Not explicitly stated (e.g., country of origin, retrospective or prospective).
• Star: "Images from the assessment are provided."
  • Sample Size: Not explicitly stated (e.g., number of patients/cases, number of images).
  • Data Provenance: Not explicitly stated (e.g., country of origin, retrospective or prospective).

2. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications:

• HyperSense 2.0: "Radiologists were asked to evaluate side-by-side image quality of the HyperSense 2.0 images compared to the predicate."
  • Number of Experts: "Radiologists" (plural), but specific number not stated.
  • Qualifications: Not explicitly stated (e.g., years of experience, subspecialty).
• Star: "Images produced by Star were judged to be of sufficient quality for diagnostic use by a a U.S. Board Certified radiologist."
  • Number of Experts: "a U.S. Board Certified radiologist" (singular).
  • Qualifications: "U.S. Board Certified radiologist." (Years of experience or subspecialty not stated).

3. Adjudication Method (for the test set):

• HyperSense 2.0: "Radiologists were asked to evaluate side-by-side image quality of the HyperSense 2.0 images compared to the predicate." This suggests individual evaluation rather than a formal adjudication process between multiple readers.
  • Method: Not explicitly stated beyond individual reader evaluation of side-by-side images. No mention of 2+1, 3+1, or consensus.
• Star: "Images produced by Star were judged to be of sufficient quality for diagnostic use by a U.S. Board Certified radiologist."
  • Method: Single reader evaluation. No adjudication described.

4. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done:

• HyperSense 2.0: The description "An external reader evaluation study was performed" and "Radiologists were asked to evaluate side-by-side image quality" suggests a multi-reader study, but it's not explicitly labeled as a formal MRMC study. The details provided are insufficient to confirm the rigor of a full MRMC design (e.g., statistical analysis of reader performance differences).
  • Effect Size of Human Readers Improve with AI vs. without AI assistance: This specific metric is not applicable here as the described studies focus on image quality assessment of a new image acquisition/reconstruction technique, not directly on AI assisting human readers in a diagnostic task for a specific condition. The "AI" implied (HyperSense 2.0 and Star) are image processing algorithms, not diagnostic AI systems assisting in interpretations.

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

• The non-clinical testing for both features would implicitly include standalone performance evaluation of the algorithms (e.g., technical measures of SNR, resolution, artifact reduction), but the document does not elaborate on these specific "standalone" metrics or a formal standalone study results. The clinical evaluations do involve human assessment of the images produced by the algorithms.

6. The Type of Ground Truth Used:

• The "ground truth" for these studies appears to be expert consensus/opinion on image quality and diagnostic sufficiency.
  • For HyperSense 2.0, the radiologists' evaluation of "overall image quality and uniformity" served as the basis for acceptance.
  • For Star, the "U.S. Board Certified radiologist's" judgment of "sufficient quality for diagnostic use" served as the basis for acceptance.
  • There is no mention of pathology, long-term outcomes data, or other objective ground truths beyond expert interpretation of the images themselves.

7. The Sample Size for the Training Set:

• This information is Not explicitly stated in the provided document. The document details the testing of the software features, but not the development or training set size (if algorithms involved machine learning).

8. How the Ground Truth for the Training Set was Established:

• This information is Not explicitly stated in the provided document. As the training set size isn't mentioned, neither is its ground truth establishment.

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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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1.5T SIGNA Creator and 1.5T SIGNA Explorer 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 1.5T SIGNA Creator and 1.5T SIGNA Explorer 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.

1.5T SIGNA Creator and 1.5T SIGNA Explorer is a whole body magnetic resonance scanner designed to support high resolution, high signal-to-noise ratio, and short scan times. The 1.5T SIGNA Creator and 1.5T SIGNA Explorer features a superconducting magnet operating at 1.5 Tesla. The system uses a combination of time-varying magnetic fields (gradients) and RF transmissions to obtain information regarding the density and position of nuclei exhibiting magnetic resonance. The data acquisition system accommodates 16 independent receive channels and multiple independent coil elements per channel during a single acquisition series.

Here's an analysis of the provided text regarding the acceptance criteria and study for the 1.5T SIGNA Creator and 1.5T SIGNA Explorer:

The provided document is a 510(k) Premarket Notification Summary from the FDA for GE Healthcare's 1.5T SIGNA Creator and 1.5T SIGNA Explorer Magnetic Resonance Imaging (MRI) systems. The primary purpose of a 510(k) submission is to demonstrate substantial equivalence to a legally marketed predicate device, rather than proving performance against specific acceptance criteria in a detailed clinical study for a novel device.

Therefore, the information you're requesting regarding explicit acceptance criteria and a dedicated study to prove precise performance metrics is largely not present in this type of regulatory document. Instead, the document focuses on demonstrating that the new device meets the same safety and effectiveness standards as its predicate.

Here's a breakdown of the information that can be extracted and what is not available based on your request:

1. Table of Acceptance Criteria and Reported Device Performance

Not explicitly provided in this document.

This 510(k) submission does not include a table of quantitative acceptance criteria (e.g., specific sensitivity, specificity, accuracy thresholds for a diagnostic task) and corresponding performance metrics for the 1.5T SIGNA Creator and 1.5T SIGNA Explorer. The acceptance criteria for a 510(k) are generally around demonstrating that the new device is as safe and effective as the predicate device(s).

The document states:

• "The subject of this premarket submission, 1.5T SIGNA Creator and 1.5T SIGNA Explorer did not require clinical studies to support substantial equivalence."
• "Internal scans were conducted as part of validation for workflow and image quality, and sample clinical images are included in the submission."
• "Additionally, the result of the above described testing demonstrates that the device performs as intended."
• "GE Healthcare considers the 1.5T SIGNA Creator and 1.5T SIGNA Explorer to be as safe, as effective, and performance is substantially equivalent to the predicate device(s)."

These statements highlight that the absence of significant differences and the compliance with established standards are the "acceptance criteria" for substantial equivalence.

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

Not applicable/available as a formal "test set" for performance evaluation.

Since no dedicated clinical study was performed to assess diagnostic performance against specific acceptance criteria, there is no "test set" in the traditional sense for diagnostic accuracy. The document mentions "internal scans" as part of validation, but does not specify sample size or data provenance for these (e.g., country of origin, retrospective/prospective). These internal scans would likely be used to evaluate image quality and workflow, not necessarily diagnostic performance against a ground truth.

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

Not applicable.

Because there was no diagnostic performance study with a formal "test set," there's no mention of experts establishing ground truth for such a study.

4. Adjudication Method for the Test Set

Not applicable.

No formal test set or diagnostic performance study means no adjudication method is mentioned.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No, a MRMC comparative effectiveness study was not done.

The document explicitly states: "The subject of this premarket submission, 1.5T SIGNA Creator and 1.5T SIGNA Explorer did not require clinical studies to support substantial equivalence." This implies that no MRMC study or any other clinical study was deemed necessary to demonstrate the device's functionality beyond substantial equivalence to the predicate. Therefore, no effect size of human reader improvement with/without AI assistance is applicable, as this device is an MRI scanner, not an AI-assisted diagnostic tool.

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

Not applicable.

This is an MRI hardware device, not a diagnostic algorithm. Therefore, "standalone" algorithm performance is not relevant.

7. The Type of Ground Truth Used

Not applicable.

Without a diagnostic performance study, the concept of "ground truth" for diagnostic accuracy is not discussed in this document. The "ground truth" underpinning this submission is fundamentally that the new device is technologically equivalent and operates within the same safety and performance parameters as already-approved predicate devices.

8. The Sample Size for the Training Set

Not applicable.

This is not an AI/algorithm-driven device requiring a training set in the machine learning sense.

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

Not applicable.

As it's not an AI/algorithm-driven device, there is no training set or associated ground truth establishment process.

Summary of what the document does provide regarding "performance" and "acceptance criteria":

• Reliance on Substantial Equivalence: The core "acceptance criterion" for this 510(k) approval is demonstrating substantial equivalence to predicate devices (1.5T Brivo MR355 and 1.5T Optima MR360, K123417; and 1.5T Optima MR450w, K142085).
• Technological Equivalence: The document states, "Proposed 1.5T SIGNA Creator and 1.5T SIGNA Explorer Technology employs the same fundamental scientific technology as its predicate device..."
• Compliance with Voluntary and Recognized Standards: The device underwent testing to comply with various standards, which serve as performance benchmarks in lieu of specific clinical performance criteria:
  • IEC60601-1, IEC60601-2-33, IEC60601-1-1, IEC60601-1-2 (Electrical safety and performance for medical devices, specifically MRI)
  • NEMA MS1, MS2, MS3, MS4, MS5, MS8 (Performance standards for MRI systems, defining terms, measurements, and reporting for aspects like image quality, S/N, geometric distortion, etc.)
  • NEMA PS PS3.1-3.20 (DICOM standard for communication interface)
• Quality Assurance Measures: The device applied standard quality assurance measures, including Risk Analysis, Requirements Reviews, Design Reviews, Testing on unit level (Module verification), Integration testing (System verification), Performance testing (Verification), Safety testing (Verification), and Simulated use testing (Validation). These indicate that the device meets internal design and quality standards.
• Internal Scans for Validation: "Internal scans were conducted as part of validation for workflow and image quality." While not a formal clinical study, these would demonstrate that the system produces images and operates as expected.

In conclusion, for a 510(k) submission regarding an MRI system like this, the "acceptance criteria" are tied to demonstrating that the device is as safe and effective as a previously approved predicate device, primarily through technological comparison, compliance with recognized performance standards, and internal validation of image quality and workflow, rather than detailed clinical performance metrics from controlled diagnostic studies.

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The Discovery MR750 3.0T, Discovery MR450 1.5T, Discovery MR750w 3.0T and the Optima MR450w 1.5T systems are whole body magnetic resonance scanners 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, coronal, and oblique images, spectroscopic images, parametric maps, and or spectra, dynamic images of the structures of the entire body, including, but not limited to, head, neck, TMJ, spine, breast, heart, abdomen, pelvis, 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 Discovery MR750 3.0T, Discovery MR750w 3.0T and the Optima MR450w I.ST systems reflect the spaial 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 Discovery MR750 3.0T. Discovery MR450 1.5T, Discovery MR750w 3.0T and the Optima MR450w 1.5T Systems are whole body magnetic resonance scanners designed to support high resolution, high signal-to-noise ratio, and short scan times. The Systems each feature a superconducting magnet. The data acquisition system accommodates up to 32 independent receive channels in various increments and multiple independent coil elements per channel during a single acquisition series. Each system uses a combination of time-varying magnetic fields (gradients) and RF transmissions to obtain information regarding the density and position of elements exhibiting magnetic resonance. Each system can image in the sagittal, coronal, axial, oblique, and double oblique planes, using various pulse sequences and reconstruction algorithms.

The DV24 release is introducing new software features onto these existing MR Systems. There are also hardware modifications to the GEM configurations for Silenz compatibility. The Silenz feature used to reduce the acoustic noise generated during an MR examination is only available on the Optima MR450w GEM and Discovery MR750w GEM configurations.

The Discovery MR750 3.0T. Discovery MR450 1.5T. Discovery MR750w 3.0T and the Optima MR450w 1.5T Systems are designed to conform to NEMA DICOM standards (Digital Imaging and Communications in Medicine).

The GE Healthcare Discovery MR750 3.0T, Discovery MR450 1.5T, Discovery MR750w 3.0T, and Optima MR450w 1.5T Magnetic Resonance Diagnostic Devices did not undergo a study with specific acceptance criteria related to new AI features or performance metrics. This is because the submission (K132376) primarily focused on the introduction of new software features (DV24 release) and hardware modifications for Silenz compatibility on existing MR systems.

The submission states: "The subject of this premarket submission... did not require external clinical studies to support substantial equivalence." Instead, the focus was on demonstrating that the updated systems maintain the same imaging performance as their predicate devices.

Here's a breakdown of the information based on the provided document, addressing the requested points where applicable, and noting where the information is not provided because it pertains to an AI/performance study that was not conducted:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: Not explicitly stated for a dedicated test set against specific performance criteria. "Internal scans were conducted as part of validation for workflow and image quality for the addition of the new features." The exact number of scans is not provided.
• Data Provenance: "Internal scans" suggests the data was generated within GE Healthcare. The country of origin and whether it was retrospective or prospective is not specified.

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

• This information is not provided. The study did not involve establishing a ground truth by experts in the context of a comparative performance study. The focus was on maintaining existing performance standards.

4. Adjudication Method for the Test Set

• Not applicable as a traditional adjudication method for a performance study was not described.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size

• No, a Multi Reader Multi Case (MRMC) comparative effectiveness study was not described or performed for this submission. The submission explicitly states, "The subject of this premarket submission... did not require external clinical studies to support substantial equivalence."

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

• Not applicable. This submission relates to physical MR imaging devices and software updates, not a standalone AI algorithm with specific performance metrics.

7. The Type of Ground Truth Used

• No explicit "ground truth" as typically defined for AI performance studies was established. The "ground truth" was implicitly the existing imaging performance of the predicate devices. The internal validation aimed to ensure the new features did not degrade this established performance.

8. The Sample Size for the Training Set

• This information is not applicable. The document does not describe the development of a machine learning model with a separate training set. The "new software features" refer to changes in the MR system's operational software, not an AI algorithm trained on a dataset.

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

• Not applicable, as there was no described training set for an AI algorithm.

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The Optima™ MR450w 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 Optima™ MR450w 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 1.5 GE Optima MR450w features a superconducting magnet operating at 1.5 Tesla. The data acquisition system accommodates up to 32 independent receive channels in various increments, and multiple independent coil elements per channel during a single acquisition series. The system uses a combination of time-varying magnetic 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 and reconstruction algorithms. The Silenz Imaging Application using the 3D Radial Pulse sequence reduces the acoustic noise that is generated during an MR examination. This application is compatible on the Optima MR450w system with GEM configuration. The 1.5T GE Optima MR450w is designed to conform to NEMA DICOM standards (Digital Imaging and Communications in Medicine).

Here's an analysis of the provided text regarding the Optima MR450w, focusing on acceptance criteria and supporting studies:

It is important to note that the provided text is a 510(k) Summary for a modification to an existing MRI device (addition of the Silenz Imaging Application to the Optima MR450w). As such, it primarily focuses on demonstrating substantial equivalence to the predicate device, rather than proving novel clinical efficacy or establishing new clinical performance targets as would be the case for an entirely new device.

Acceptance Criteria and Reported Device Performance

The document does not explicitly state specific quantitative acceptance criteria or a table of performance metrics for the Optima MR450w with the Silenz application in the traditional sense of a clinical performance study (e.g., sensitivity, specificity, accuracy against a gold standard).

Instead, the acceptance criteria are implicitly met by demonstrating compliance with recognized standards and by verification and validation activities ensuring the device performs as intended and is equivalent to the predicate.

The reported device performance is qualitative, focused on maintaining existing standards and functionality:

Study Details

The document explicitly states that no external clinical studies were required to support substantial equivalence. The "studies" mentioned are internal verification and validation activities.

1. Sample size used for the test set and the data provenance:
* Test Set Sample Size: Not specified. The document mentions "Internal scans were conducted as part of validation for workflow and image quality." It does not provide a number of scans or distinct subjects/patients used for these internal tests.
* Data Provenance: "Internal scans" implies the data was generated within GE Healthcare, likely on their own systems for testing purposes. The country of origin and whether it was retrospective or prospective is not specified, but typically, internal validation scans are prospective as they are specifically generated for testing.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
* Not specified. The document highlights "Testing on unit level," "Integration testing," and "Performance testing" with "passing results per the pass/fail criteria defined in the test cases." It also mentions "Simulated use testing."
* The interpretation of images is for "a trained physician" to yield information for diagnosis, but this refers to the intended use of the device, not the ground truth establishment for the internal validation studies.

3. Adjudication method for the test set:
* Not applicable/Not specified. The internal validation focused on technical performance rather than clinical diagnostic accuracy requiring adjudicated ground truth for a test set. The validation used "pass/fail criteria defined in the test cases."

4. 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 done.
* This device is an MRI scanner, and the "Silenz Imaging Application" is a feature to reduce acoustic noise, not an AI or CAD system intended to assist human readers in diagnosis. Therefore, the concept of improving human readers with AI assistance does not apply here.

5. If a standalone (i.e. algorithm only without human-in-the loop performance) was done:
* Not applicable in the context of an "algorithm only" performance study for diagnostic AI. The device is a whole-body MRI scanner. Its performance is inherent in the image acquisition and reconstruction, not a standalone diagnostic algorithm.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
* For the internal validation "workflow and image quality" scans, the ground truth would likely be based on technical specifications, industry standards for image quality, and expert review (though not explicitly detailed) of the acquired images to confirm they met predefined quality metrics (e.g., signal-to-noise ratio, spatial resolution, artifact levels, and successful noise reduction). It would not be clinical ground truth like pathology or outcomes data.

7. The sample size for the training set:
* Not applicable. This device is an MRI scanner, not a machine learning algorithm that requires a "training set" in the typical sense for diagnostic AI. While the internal development of the Silenz application (which likely involves signal processing and perhaps some optimization algorithms) would have utilized data, it's not described as a "training set" for a diagnostic AI.

8. How the ground truth for the training set was established:
* Not applicable, as there is no "training set" for a diagnostic AI algorithm in this context.

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