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The ECHELON Synergy System is an imaging device and is intended to provide the physician with physiological and clinical information, obtained non-invasively and without the use of ionizing radiation. The MR system produces transverse, coronal, sagittal, oblique, and curved cross sectional images that display the internal structure of the head, body, or extremities. The images produced by the MR system reflect the spatial distribution of protons (hydrogen nuclei) exhibiting magnetic resonance. The NMR properties that determine the image appearance are proton density, spin-lattice relaxation time (T1), spin-spin relaxation time (T2) and flow. When interpreted by a trained physician, these images provide information that can be useful in diagnosis determination.

The ECHELON Synergy is a Magnetic Resonance Imaging System that utilizes a 1.5 Tesla superconducting magnet in a gantry design. The control and image processing hardware and the base elements of the system software are identical to the predicate device.

This document describes the ECHELON Synergy MRI system's acceptance criteria and the studies conducted to demonstrate its performance. The submission for FDA 510(k) clearance (K251386) references a predicate device, the ECHELON Synergy MRI System (K241429), and outlines modifications to hardware and software.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of "acceptance criteria" against which a numeric performance metric is listed for each new feature. Instead, it details that certain functionalities (DLR Symmetry and AutoPose) underwent performance evaluations. The "performance" reported is described qualitatively or comparatively to conventional methods.

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

• DLR Symmetry:
  • Clinical Image Test Set: 89 unique subjects (patients and healthy subjects).
  • Data Provenance: From U.S. and Japan.
  • Data Type: Retrospective (clinical images collected).
• AutoPose:
  • Shoulder: 60 cases
  • Knee: 60 cases
  • HipJoint: 65 cases
  • Abdomen: 115 cases
  • Pelvis for male: 60 cases
  • Pelvis for female: 68 cases
  • Cardiac: 126 cases
  • Data Provenance: FUJIFILM Corp., FUJIFILM Healthcare Americas Corp., and clinical sites.
  • Data Type: Subject type includes healthy volunteers and patients, implying a mix of prospective data collection for testing new features and potentially retrospective for some patient data.

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

• DLR Symmetry:
  • Number of Experts: Three US board certified radiologists.
  • Qualifications: "US board certified radiologists." Specific years of experience are not mentioned.
• AutoPose:
  • Number of Experts/Evaluators: Three certified radiological technologists.
  • Qualifications: "Certified radiological technologists." Specific years of experience are not mentioned.

4. Adjudication Method for the Test Set

• DLR Symmetry: The document states that comparisons were made by "the reviewers" (plural) in terms of image quality metrics using a 3-point scale. It doesn't explicitly state an adjudication method like 2+1 or 3+1 if there were disagreements among the three radiologists. It implies a consensus or majority rule might have been used for the reported "superior" findings, but this isn't detailed.
• AutoPose: The evaluation results are simply described as "evaluation results showed," implying a summary of the technologists' findings. No specific adjudication method is described.

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

• DLR Symmetry: A form of MRMC study was conducted where three US board-certified radiologists reviewed images reconstructed with DLR Symmetry versus conventional methods.
• Effect Size of Human Readers with AI vs. Without AI Assistance: The document indicates that images with DLR Symmetry (AI-assisted reconstruction) were "superior to those in the conventional images with statistically significant difference (p<0.05)" across various image quality metrics. This shows an improvement in the perceived image quality for human readers when using DLR Symmetry, but it does not quantify the "effect size of how much human readers improve with AI vs. without AI assistance" in terms of diagnostic performance (e.g., improved sensitivity/specificity for a given task). Instead, it focuses on the quality of the image presented to the reader.

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

• DLR Symmetry: Yes, in part. Phantom testing was conducted to evaluate artifact reduction (NRMSE), SNR, Relative Edge Sharpness, and Contrast Change Rate. This is an algorithmic performance evaluation independent of human interpretation of clinical images, although it assesses image characteristics rather than diagnostic output.
• AutoPose: The evaluation by certified radiological technologists focuses on the algorithm's ability to set slice positions automatically, which is a standalone performance metric for the automation function.

7. The Type of Ground Truth Used

• DLR Symmetry:
  • For phantom testing: "Ground truth" refers to the known characteristics of the phantom and metrics like NRMSE, SNR, etc.
  • For clinical image review: The ground truth was established by expert consensus or individual assessment of the "clinical acceptability" of the images and comparative judgment (superiority) of image quality metrics by three US board-certified radiologists. This isn't pathology or outcomes data, but rather expert radiological opinion on image quality and clinical utility.
• AutoPose: The "ground truth" was whether the automated positioning successfully set the slice positions without manual adjustment, as evaluated by certified radiological technologists.

8. The Sample Size for the Training Set

The document explicitly states regarding DLR Symmetry: "The test dataset was independent of the training and validation datasets." However, it does not provide the sample size or details for the training set (or validation set) for DLR Symmetry or AutoPose.

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

The document does not provide details on how the ground truth for the training set was established for either DLR Symmetry or AutoPose, as the training set details are not included in the provided text.

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The ECHELON Synergy System is an imaging device and is intended to provide the physician with physiological and clinical information, obtained non-invasively and without the use of ionizing radiation. The MR system produces transverse, coronal, sagittal, oblique, and curved cross sectional images that display the internal structure of the head, body, or extremities. The images produced by the MR system reflect the spatial distribution of protons (hydrogen nuclei) exhibiting magnetic resonance. The NMR properties that determine the image appearance are proton density, spinlattice relaxation time (TI), spin-spin relaxation time (T2) and flow. When interpreted by a trained physician, these images provide information that can be useful in diagnosis determination.

Anatomical Region: Head, Body, Spine, Extremities
Nucleus excited: Proton

Diagnostic uses:

• · TI, T2, proton density weighted imaging
• · Diffusion weighted imaging
• · MR Angiography
• · Image processing
• · Spectroscopy
• · Whole Body

The ECHELON Synergy is a Magnetic Resonance Imaging System that utilizes a 1.5 Tesla superconducting magnet in a gantry design.

The provided document is a 510(k) summary for the FUJIFILM Healthcare Corporation's ECHELON Synergy MRI System. This document asserts substantial equivalence to a predicate device and primarily focuses on technical characteristics and adherence to standards rather than detailed performance studies with acceptance criteria for a diagnostic aid.

Here's an analysis of the acceptance criteria and study information derived from the document:

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

The document doesn't explicitly state quantitative acceptance criteria in terms of diagnostic performance metrics (e.g., sensitivity, specificity, AUC) because it's a 510(k) submission for an MRI system with an added coil, not a diagnostic algorithm. The acceptance criteria for the added Breast Coil 17 are implicitly tied to the performance and safety standards of the predicate device (ECHELON Synergy V10.0 K233687).

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

• Sample size for test set: Not explicitly stated as a number of cases or patients. The document mentions "Clinical image examples."
• Data provenance: Not explicitly stated (e.g., country of origin, retrospective or prospective). It only states that "Clinical images were collected and analyzed."

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

• Number of experts: One radiologist.
• Qualifications of experts: A "radiologist" validated the clinical images. No further details on experience level are provided.

4. Adjudication method for the test set:

• Adjudication method: None mentioned beyond a single radiologist's validation of image quality for clinical use.

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: No, an MRMC comparative effectiveness study was not explicitly mentioned or implied. This submission is for an MRI system with an added coil, not an AI-powered diagnostic algorithm.

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

• Standalone performance: Not applicable. This device is an MRI system, not a standalone AI algorithm. The performance evaluation focused on the technical aspects and image quality of the MRI machine and its new coil.

7. The type of ground truth used:

• Type of ground truth: Expert opinion (a single radiologist's validation of "acceptable image quality for clinical use"). This is tied to the demonstrative aspect of clinical image examples, rather than a definitive diagnostic truth for a disease state.

8. The sample size for the training set:

• Sample size for training set: Not applicable. This document is about a hardware modification (an added coil) to an existing MRI system. It does not involve machine learning models that require training sets in the conventional sense.

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

• How ground truth for training set was established: Not applicable, as there is no mention of a training set or machine learning model.

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The ECHELON Synergy System is an imaging device and is intended to provide the physician with physiological and clinical information, obtained non-invasively and without the use of ionizing radiation. The MR system produces transverse, coronal, sagittal, oblique, and curved cross sectional images that display the internal structure of the head, body, or extremities. The images produced by the MR system reflect the spatial distribution of protons (hydrogen nuclei) exhibiting magnetic resonance. The NMR properties that determine the image appearance are proton density, spinlattice relaxation time (TI), spin-spin relaxation time (T2) and flow. When interpreted by a trained physician, these images provide information that can be useful in diagnosis determination.

Anatomical Region: Head, Body, Spine, Extremities Nucleus excited: Proton

Diagnostic uses:

• · TI, T2, proton density weighted imaging
• · Diffusion weighted imaging
• · MR Angiography
• · Image processing
• · Spectroscopy
• · Whole Body

The ECHELON Synergy is a Magnetic Resonance Imaging System that utilizes a 1.5 Tesla superconducting magnet in a gantry design. Magnetic Resonance imaging (MRI) is based on the fact that certain atomic nuclei have electromagnetic properties that cause them to act as small spinning bar magnets. The most ubiquitous of these nuclei is hydrogen, which makes it the primary nuclei currently used in magnetic resonance imaging. When placed in a static maqnetic field, these nuclei assume a net orientation or alignment with the magnetic field, referred to as a net magnetization vector. The introduction of a short burst of radiofrequency (RF) excitation of a wavelength specific to the magnetic field strength and to the atomic nuclei under consideration can cause a re-orientation of the net magnetization vector. When the RF excitation is removed, the protons relax and return to their original vector. The rate of relaxation is exponential and varies with the character of the proton and its adjacent molecular environment. This re-orientation process is characterized by two exponential relaxation times, called T1 and T2. A RF emission or echo that can be measured accompanies these relaxation events. The emissions are used to develop a representation of the relaxation events in a three dimensional matrix. Spatial localization is encoded into the echoes by varving the RF excitation. applying appropriate magnetic field gradients in the x, y, and z directions, and changing the direction and strength of these gradients. Images depicting the spatial distribution of the NMR characteristics can be reconstructed by using image processing techniques similar to those used in computed tomography.

The provided document describes the Fujifilm ECHELON Synergy V10.0 MRI system, which is an updated version of a previously cleared device. The submission focuses on demonstrating substantial equivalence to the predicate device (ECHELON Synergy MRI System K223426) by highlighting changes and providing performance evaluations.

Here's an analysis of the acceptance criteria and study information:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of "acceptance criteria" for the overall device in a quantifiable format. Instead, it demonstrates the new features' performance through clinical image testing and phantom studies, comparing them to conventional methods or manual positioning. The acceptance criteria for "DLR Clear" are implied through achieving statistical significance for superiority in certain image quality metrics over conventional imaging and clinical acceptability. For "AutoPose," the criteria are implied through reduction or equivalence in time and steps for slice positioning.

Here's a summary of the performance results for the new features (DLR Clear and AutoPose):

2. Sample Sizes and Data Provenance

DLR Clear:

• Test Set Sample Size: 53 unique subjects (patients and volunteers).
• Data Provenance: U.S. and Japan.
• Retrospective/Prospective: Not explicitly stated, but the description "scanned in the anatomical regions... for the test datasets separately from the training and validation datasets" suggests a prospective collection for the test set or careful selection from retrospective archives to act as a distinct test set.

AutoPose (Spine, Breast, HipJoint):

• Test Set Sample Size:
  • Spine: 177 cases
  • Breast: 66 cases
  • HipJoint: 65 cases
• Data Provenance: FUJIFILM Healthcare Corporation and clinical site (country not specified for clinical site, but assuming Japan given the company origin and DLR Clear's data provenance).
• Retrospective/Prospective: Not explicitly stated.

3. Number of Experts and Qualifications for Ground Truth for the Test Set

DLR Clear:

• Number of Experts: Three (3) US board-certified radiologists.
• Qualifications of Experts: US board-certified radiologists. (Specific experience levels not provided).

AutoPose:

• Number of Experts: "Certified radiological technologists." (Number not specified, specific experience not provided). Their evaluation was on the reduction/equivalence of time and steps for slice positioning.

4. Adjudication Method for the Test Set

DLR Clear:

• The images were randomized and blinded to the reviewers.
• Reviewers compared image quality metrics (truncation artifact reduction, image sharpness, lesion conspicuity, and overall image quality) using a 3-point scale.
• The results indicate statistical analysis (p<0.05), implying that the individual ratings were aggregated or compared statistically to determine superiority. It does not explicitly state a consensus-based adjudication method (like 2+1 or 3+1). It seems individual reviewer scores were used.

AutoPose:

• Evaluated by "certified radiological technologists." The method of combining their evaluations or achieving consensus is not explicitly described. It states "They evaluated that almost cases..." suggesting a collective assessment or majority opinion, but no formal adjudication process is detailed.

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

There is no explicit MRMC comparative effectiveness study directly comparing human reader performance with and without AI assistance (i.e., effect size of human improvement) described in this document.

The study for DLR Clear involved radiologists reviewing images reconstructed with DLR Clear versus conventional methods. This evaluates the impact of the AI-powered reconstruction on image quality, which in turn facilitates human reading, but it does not measure the human reader's diagnostic performance improvement (e.g., AUC, sensitivity, specificity) when using the AI-processed images compared to conventional images.

6. Standalone Performance Study

DLR Clear:
Yes, a form of standalone performance was done for DLR Clear. The phantom testing ("Total Validation, Relative Edge Sharpness, and FWHM") demonstrates the algorithm's direct performance on objective metrics before human interpretation. The clinical image review also evaluates the quality of the algorithm's output directly, without explicitly involving an "AI-assisted reading" scenario. The radiologists were evaluating the images produced by the algorithm, not acting as diagnosticians making decisions with or without AI output.

AutoPose:
Yes, the AutoPose feature's performance (reduction in time/steps for positioning) was evaluated by radiological technologists, which can be considered a standalone performance assessment for the positioning automation.

7. Type of Ground Truth Used

DLR Clear:

• Phantom Testing: The ground truth for phantom testing is the physical properties of the cylindrical and ACR phantoms themselves, measured by objective metrics (e.g., FWHM for spatial resolution).
• Clinical Testing: The ground truth for clinical image quality assessment relies on expert consensus/opinion (ratings by 3 US board-certified radiologists) regarding image quality metrics (truncation artifact, sharpness, lesion conspicuity, overall image quality) and clinical acceptability. There's no mention of pathology or outcomes data as ground truth for these image quality assessments.

AutoPose:

• The ground truth for AutoPose is implicitly the objective measurement of time and steps required for slice positioning, compared against manual methods, as evaluated by certified radiological technologists.

8. Sample Size for the Training Set

DLR Clear: Not explicitly stated. The document mentions "test datasets separately from the training and validation datasets" for DLR Clear, indicating that training data existed, but its size is not provided.

AutoPose: Not explicitly stated.

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

DLR Clear: Not explicitly stated. Since it's a deep learning reconstruction (DLR), the ground truth for training would typically involve pairs of raw MRI data and high-quality, potentially "gold standard" reconstructed images (e.g., using more extensive acquisition or advanced reconstruction techniques) or specific labels for image quality characteristics.

AutoPose: Not explicitly stated. For an automated positioning algorithm, training ground truth would likely involve expert-defined optimal slice prescriptions on a large dataset of diverse anatomies.

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The ECHELON Synergy System is an imaging device and is intended to provide the physician with physiological and clinical information, obtained non-invasively and without the use of ionizing radiation. The MR system produces transverse, coronal, sagittal, oblique, and curved cross-sectional images that display the internal structure of the head, body, or extremities. The images produced by the MR system reflect the spatial distribution of protons (hydrogen nuclei) exhibiting magnetic resonance. The NMR properties that determine the image appearance are proton density, spin-lattice relaxation time (T1), spin-spin relaxation time (T2) and flow. When interpreted by a trained physician, these images provide information that can be useful in diagnosis determination.

The ECHELON Synergy is a Magnetic Resonance Imaging System that utilizes a 1.5 Tesla superconducting magnet in a gantry design. The design was based on the ECHELON OVAL V6.0A MRI system. The ECHELON Synergy has been designed to enhance clinical utility as compared to the ECHELON OVAL V6.0A by taking advantage of open architecture.

The provided document, K223426, is a 510(k) premarket notification for the FUJIFILM Healthcare Corporation's ECHELON Synergy MRI system. This submission primarily focuses on demonstrating substantial equivalence to a predicate device (ECHELON OVAL V6.0A MRI system, K172110) rather than presenting a detailed performance study with explicit acceptance criteria for an AI/ML powered device as typically required for novel AI products.

However, the document mentions several new features powered by Machine Learning (ML), specifically Deep Learning Reconstruction (DLR), AutoClip, AutoPose Spine, AutoPose Shoulder, and AutoPose Knee. For DLR, some form of evaluation was performed. For AutoClip and AutoPose functions, performance comparisons were made against manual operations.

Based on the provided text, a comprehensive table of acceptance criteria and reported device performance, as one would expect for a dedicated AI/ML device approval, is not explicitly stated with numerical thresholds. The evaluations are largely qualitative or comparative to existing methods.

Below is an attempt to extract the closest information to your request, specifically focusing on the DLR, AutoClip, and AutoPose functions, as they are the only "AI/ML powered" components mentioned with specific evaluations.

1. Table of Acceptance Criteria and Reported Device Performance

As explicit numerical acceptance criteria are not provided for the AI/ML components, the table below consolidates the stated evaluative goals and findings from the "Summary of Clinical Testing" section.

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

• Deep Learning Reconstruction (DLR):

  • Number of cases: 110 cases for DLR image quality evaluation (including 81 cases for SNR/sharpness/overall IQ, 45 cases with pathology for lesion conspicuity, 3 cases for motion artifacts, and 18 cases for shorter scan time evaluation). The exact breakdown per sub-analysis is specified.
  • Data Provenance: ECHELON OVAL, ECHELON Smart, and ECHELON Synergy MRI systems (all FUJIFILM Healthcare Corporation 1.5T MRI systems). Data acquired at "FUJIFILM Healthcare Corporation and clinical site."
  • Subject Type: Healthy volunteer and patient.
  • Anatomical Coverage: Head, Spine, Cardiac, Breast, Abdomen, Pelvis, Shoulder, Wrist, Knee, Ankle.

• AutoClip:

  • Number of cases: 40 cases.
  • Data Provenance: ECHELON Synergy MRI system (FUJIFILM Healthcare Corporation 1.5T MRI system). Data acquired at "FUJIFILM Healthcare Corporation."
  • Subject Type: Japanese healthy volunteers.
  • Anatomical Coverage: Brain (using 3D TOF, 3D Soft TOF scan sequences).

• AutoPose (Spine, Shoulder, Knee):

  • Number of cases: Spine (146 cases), Shoulder (48 cases), Knee (38 cases).
  • Data Provenance: ECHELON Synergy MRI system (FUJIFILM Healthcare Corporation 1.5T MRI system). Data acquired at "FUJIFILM Healthcare Corporation."
  • Subject Type: Japanese healthy volunteers.
  • Anatomical Coverage: Spine, Shoulder, Knee.

Note: The document does not explicitly state if the data was retrospective or prospective. Given the nature of performance testing within a company and potentially a clinical site, it could be a mix or internal prospective collection, but it's not specified. The country of origin for the "clinical site" data is also not explicitly stated beyond "Japanese healthy volunteers" for AutoClip/AutoPose, implying at least part of the data is from Japan for those features.

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

• Deep Learning Reconstruction (DLR):

  • Number of Experts: Three (3) US certified radiologists.
  • Qualifications: "US certified radiologists." No specific years of experience or subspecialty are provided, beyond their certification.

• AutoClip & AutoPose (Spine, Shoulder, Knee):

  • Number of Experts: Not specified as "experts" establishing ground truth, but rather "certified radiological technologists" performed the performance comparison/evaluation. The ground truth for performance was implicitly "manual operation" by these technologists. Their qualifications are listed as "certified radiological technologists."

4. Adjudication Method for the Test Set

• Deep Learning Reconstruction (DLR): The document states "Readers compared pairs of DLR images and conventional images (without DLR) for each case to evaluate image quality of DLR images." It does not specify an explicit adjudication method (e.g., 2+1, 3+1). It merely presents the results as derived from the collective evaluation of the three radiologists. It's unclear if consensus was required, or if individual ratings were aggregated.

• AutoClip & AutoPose: The evaluation was done by "certified radiological technologists" comparing against manual operation. No formal adjudication process is described.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and Effect Size

• Deep Learning Reconstruction (DLR): A study involving multiple readers (3 US certified radiologists) and multiple cases (110 cases in total for DLR evaluation) was performed, which aligns with the spirit of an MRMC study. However, it's not explicitly labeled as such, and the methodology primarily focuses on qualitative comparison of image quality between DLR and conventional images rather than a comparative effectiveness study of human reader diagnostic performance with vs. without AI assistance for a specific diagnostic task.

  • Effect Size of Human Reader Improvement: This type of effect size (e.g., AUC uplift) is not reported. The study focused on assessing image quality attributes and acceptability for routine examinations from the DLR images themselves, as perceived by radiologists, not on how DLR assistance changes a radiologist's diagnostic accuracy or efficiency on a specific clinical task. The evaluation was primarily about the AI's impact on image characteristics, not human diagnostic performance.

• AutoClip & AutoPose: These evaluations were focused on the efficiency and equivalence of the automated process compared to manual operation, as assessed by technologists. They were not MRMC studies designed to measure impact on human readers' diagnostic effectiveness.

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

• The document implies a standalone assessment of the DLR output quality (SNR, sharpness, etc.) against conventional images, as rated by radiologists. The AutoClip and AutoPose functions are also inherently standalone algorithms that automate tasks, with their performance evaluated against manual methods. However, no formal "standalone performance study" with typical metrics like sensitivity, specificity, or AUC for a diagnostic task is presented for these AI/ML components in isolation. The evaluation focuses on product-level performance and usability.

7. The Type of Ground Truth Used

• Deep Learning Reconstruction (DLR): The ground truth for evaluation was expert consensus/opinion (or individual expert assessment) of the image quality attributes (SNR, sharpness, lesion conspicuity, overall image quality) when comparing DLR images to conventional images. The underlying "ground truth" for the cases themselves (e.g., presence of pathology) would presumably come from standard clinical diagnostic reports or other confirmed findings, but the DLR study's focus was on image quality as assessed by experts.

• AutoClip & AutoPose: The ground truth for these functions was the manual operation by certified radiological technologists. The evaluation aimed to determine if the automated function delivered equivalent or better performance (in terms of results and/or efficiency) compared to the human-performed task.

8. The Sample Size for the Training Set

• The document does not provide any details on the sample size used for the training set for DLR, AutoClip, or AutoPose. This information is typically proprietary and not included in 510(k) summaries unless specifically requested by the FDA or deemed critical for demonstrating substantial equivalence for a novel AI/ML device.

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

• Similar to the training set sample size, the document does not provide any details on how the ground truth for the training set was established for DLR, AutoClip, or AutoPose.

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