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The OASIS MRI 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 crosssectional 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.

Anatomical Region: Head, Body, Spine, Extremities
Nucleus excited: Proton
Diagnostic uses: T1, T2, proton density weighted imaging
Diffusion weighted imaging
MR Angiography
Image processing
Spectroscopy
Whole Body

The OASIS MRI System is a Magnetic Resonance Imaging System that utilizes a 1.2 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 magnetic 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 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, a 510(k) Summary for the OASIS MRI System (K240571), describes the device and its equivalence to a predicate device (OASIS MRI System K211406). The acceptance criteria and performance study details are primarily focused on a new feature, DLR Rise.

Here's a breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state numerical acceptance criteria for DLR Rise. Instead, the acceptance is based on expert subjective evaluation of image quality metrics and clinical acceptability.

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The OASIS MRI 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 (T), 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 OASIS is a Magnetic Resonance Imaging System that utilizes a 1.2 Tesla superconducting maqnet in a qantry design.

The provided document is a 510(k) Premarket Notification from Hitachi Healthcare Americas for their OASIS MRI System. It primarily focuses on demonstrating substantial equivalence to a previously cleared predicate device (OASIS MRI System K202030) rather than presenting a detailed clinical study with acceptance criteria for a new device's performance.

Therefore, the document does not contain details about acceptance criteria or a study that specifically proves the device meets those criteria in the way typically expected for an AI/ML medical device.

However, I can extract information related to performance evaluation and testing that was conducted to support the substantial equivalence claim.

Here's an analysis based on the provided text:

No specific acceptance criteria and detailed study proving direct device performance against those criteria are provided in the document. The document primarily focuses on demonstrating substantial equivalence to a predicate device by evaluating changes and ensuring they do not affect safety or effectiveness.

Here's what can be extracted regarding the type of performance evaluation done:

1. Table of Acceptance Criteria and Reported Device Performance

As noted above, no explicit table of acceptance criteria with corresponding device performance metrics is provided in the document for the new features. The evaluation is primarily framed in terms of demonstrating that new features perform as intended and do not raise new questions of safety or effectiveness compared to the predicate device.

The document states:

• "Performance bench testing was conducted on the applicable new features. Test data confirmed that each new feature perform as intended for diagnostic use."
• "Clinical image examples are provided for each applicable new feature and or coil that we judged to be sufficient to evaluate clinical usability."
• "Clinical images were collected and analyzed, to ensure that images from the new feature meet user needs."

These are qualitative statements about performance rather than quantitative acceptance criteria.

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

• Test Set Sample Size: Not specified. The document states "Clinical image examples are provided for each applicable new feature..." This suggests a qualitative review of examples rather than a statistically powered study with a defined sample size.
• Data Provenance: Not specified. The context implies these are images generated by the OASIS MRI system itself, but no details about the patient population, imaging sites, or whether the data is retrospective or prospective are given.

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

• Number of Experts: Not specified. The evaluations were "judged to be sufficient to evaluate clinical usability" and to "meet user needs," implying expert review, but the number or specific roles of these experts are not detailed.
• Qualifications of Experts: Not specified. The indications for use state that "When interpreted by a trained physician, these images provide information that can be useful in diagnosis determination." This indirectly suggests that the "judges" and "users" are likely trained physicians or radiologists, but their specific qualifications (e.g., years of experience, subspecialty) are not mentioned.

4. Adjudication Method for the Test Set

• Adjudication Method: Not specified. Given the qualitative nature of the review ("judged to be sufficient," "meet user needs"), it's likely a consensus-based or individual expert assessment rather than a formal adjudicated process (e.g., 2+1, 3+1).

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

• MRMC Study: No, an MRMC comparative effectiveness study was not explicitly mentioned or described. The document focuses on demonstrating that the device (i.e., the MRI system itself) with new features is substantially equivalent to the predicate, not on how human readers' performance improves with or without AI assistance. The OASIS MRI System is an imaging device, and the changes described (coils, software functions) are enhancements to image acquisition and processing, not an AI-assisted diagnostic tool.

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

• Standalone Performance: The described device (OASIS MRI System) is an imaging system, not an algorithm intended for standalone diagnostic output. Therefore, a standalone performance evaluation in the context of an "algorithm only" is not applicable or described. The performance testing conducted for the new software functions (IP-Recon, IP-Scan, AutoPose Spine, AutoClip) would relate to their intended function within the MRI system (e.g., image reconstruction accuracy, scan automation effectiveness), not as standalone diagnostic algorithms.

7. The Type of Ground Truth Used

• Ground Truth Type: Not explicitly stated as "ground truth." The evaluation seems to rely on clinical usability and meeting user needs as assessed by qualified individuals (implicitly, physicians/radiologists). This would fall under a form of expert consensus/opinion regarding the quality and utility of the images produced by the new features. There's no mention of pathology, outcomes data, or other objective sources of ground truth.

8. The Sample Size for the Training Set

• Training Set Sample Size: Not applicable/not specified. The document describes an MRI system and its software/hardware enhancements. It does not mention a "training set" in the context of machine learning model development. The software functions like IP-Recon, IP-Scan, AutoPose Spine, and AutoClip are likely rule-based or optimized algorithms, not necessarily deep learning models requiring a distinct "training set" in the common sense.

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

• Ground Truth Establishment for Training Set: Not applicable/not specified, as no training set for a machine learning model is mentioned.

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The OASIS MRI 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-latice 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 OASIS is a Magnetic Resonance Imaging System that utilizes a 1.2 Tesla superconducting maqnet in a qantry design.

The provided text describes a 510(k) premarket notification for the Hitachi OASIS MRI System, focusing on demonstrating substantial equivalence to a predicate device (OASIS MRI System K192851). The document primarily outlines changes made to the device and provides a rationale for why these changes do not affect safety or effectiveness, rather than presenting a detailed study with specific acceptance criteria and performance metrics in the typical sense of a diagnostic AI device.

Therefore, the requested information, particularly regarding specific numerical acceptance criteria, performance metrics, sample sizes for test/training sets, expert qualifications, and adjudication methods, is not explicitly available in the provided text as it would be for an AI/CADe device. The submission focuses on demonstrating that modifications to an existing MRI system (hardware, coils, software updates) do not negatively impact its performance compared to the predicate.

Here's an analysis based on the information available in the document, and what is missing:

1. Table of Acceptance Criteria and Reported Device Performance

This information is not explicitly stated in a quantifiable table as requested. The document asserts "substantial equivalence" as the primary "acceptance criterion" indirectly. The performance evaluation is focused on demonstrating that new features and changes "perform as intended for diagnostic use" and that the device modifications "do not raise different questions of safety and effectiveness."

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

The document mentions "Clinical image examples are provided for each applicable new feature." It describes these examples as "sufficient to evaluate clinical usability."

• Sample Size: Not specified. It's implied to be a collection of "examples" rather than a statistically powered test set for quantitative performance.
• Data Provenance: Not specified (e.g., country of origin, retrospective/prospective). It likely refers to internal testing data.

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

• Number of Experts: Not specified. The phrase "we judged to be sufficient to evaluate clinical usability" suggests internal evaluation.
• Qualifications of Experts: Not specified.

4. Adjudication method for the test set

• Adjudication Method: Not specified.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and effect size of human readers improvement with AI vs without AI assistance

• MRMC Study: No, this is not an AI/CADe device. The submission concerns an MRI system itself and its modifications, not an AI-powered diagnostic aid. Therefore, no MRMC study comparing human readers with and without AI assistance was performed or needed.

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

• Standalone Performance: No, this is not an AI/CADe device. The assessment is on the MRI system's ability to produce diagnostic images. The "standalone" performance here refers to the system meeting technical standards (NEMA, IEC) and producing images deemed clinically usable.

7. The type of ground truth used

The "ground truth" implicitly used for the clinical image examples is expert judgment of clinical usability of the images. This is not pathology, outcomes data, or a pre-established consensus for specific findings, but rather an assessment that the images produced by the modified system remain suitable for diagnosis by a trained physician.

8. The sample size for the training set

• Training Set Sample Size: Not applicable. This device is an MRI system, not a machine learning algorithm that requires a "training set" in the context of AI.

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

• Training Set Ground Truth Establishment: Not applicable, as it's not an AI/ML device.

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The OASIS MRI 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 OASIS is a Magnetic Resonance Imaging System that utilizes a 1.2 Tesla superconducting maqnet in a qantry design.

After reviewing the provided text, it's immediately clear that this document is a 510(k) Premarket Notification from the FDA for a new version of an MRI system (OASIS MRI System).

The key takeaway is that this document DOES NOT describe a study involving an "AI" or "algorithm" with human-in-the-loop performance, nor does it provide detailed quantitative acceptance criteria and study results for such a device.

Instead, it's a submission for the OASIS MRI System w/ V6.0F being substantially equivalent to its predicate device, the OASIS MRI System (K093044). The "performance evaluation" mentioned focuses on demonstrating that new features (like updated coils, operating systems, and software versions) do not negatively impact the device's safety and effectiveness and that the system performs as intended for diagnostic use.

The "performance evaluation" is primarily rational analysis and bench testing, with "clinical image examples" provided for usability, rather than a rigorous clinical performance study with defined quantitative acceptance criteria. There's no mention of:

• Specific quantitative metrics like sensitivity, specificity, AUC.
• Sample sizes for test sets in the typical sense of a clinical trial.
• Ground truth establishment by experts for specific diagnostic performance.
• MRMC studies or human reader improvement with AI assistance.
• Standalone algorithm performance.

Therefore, I cannot fulfill most of your request directly from the provided text, as the information regarding the acceptance criteria and the study design for an AI/algorithm-based device is not present. The document focuses on demonstrating substantial equivalence of a general MRI system, not the performance of an AI application within it.

I can, however, extract information about the predicate device and the general approach to demonstrating equivalence, which might give you some insight into the FDA 510(k) process for MRI systems, but not for an AI/algorithm.

Based on the provided document, here's what can be extracted, and where limitations exist regarding your specific request:

Limitations: The document does not describe the acceptance criteria and a study for an AI/algorithm-based device. It is a 510(k) submission for a new version of an MRI system (hardware and software updates) demonstrating substantial equivalence to a legally marketed predicate device. Therefore, many of the requested points related to AI/algorithm performance, ground truth establishment, expert readers, and MRMC studies are not applicable or not detailed in this context.

Information Extracted (with caveats for non-AI focus):

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

   • Acceptance Criteria (Implied for Substantial Equivalence): The primary "acceptance criterion" for this 510(k) is that the modified OASIS MRI System (V6.0F) is substantially equivalent to its predicate (OASIS K093044) in terms of safety, effectiveness, and intended use, despite specific updates in coils, operating system, CPU, and application software. This is demonstrated by showing that new features "perform as intended for diagnostic use" and that differences do not "raise different questions of safety and effectiveness."
   • Reported Device Performance: The "performance" is qualitative, focusing on whether new features function correctly and that fundamental safety/performance characteristics (like signal-to-noise ratio, uniformity, acoustic noise, electrical safety, EMC) remain acceptable or are not negatively impacted.
   • Table 1: Performance Analysis

     Testing Type	Rationale Analysis	Reported Device Performance
     Performance Testing - Bench	Performance bench testing was conducted on the applicable new features.	Test data confirmed that each new feature perform as intended for diagnostic use.
     Performance Testing - Clinical	Clinical image examples are provided for each applicable new feature and that we judged to be sufficient to evaluate clinical usability.	[Details of usability are not quantified, but the judgment was "sufficient"]

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

   • The document mentions "clinical image examples" for usability but does not specify a sample size for a clinical test set in the way one would for an AI performance study.
   • Data Provenance: Not specified. The clinical images are "examples" and likely collected by Hitachi, but whether they are retrospective or prospective, or from specific countries, is not stated.

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

   • Not applicable directly. This document is for an MRI system, not an AI/algorithm that requires expert-established ground truth for diagnostic accuracy. The "clinical image examples" were "judged to be sufficient to evaluate clinical usability," which implies interpretation by presumably qualified personnel (likely radiologists or technologists), but the number and qualifications are not specified nor is there a formal "ground truth" establishment process described for a test set.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

   • Not applicable. There is no formal adjudication method described for a test set, as this is not a study assessing diagnostic performance of an algorithm.

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:

   • No, an MRMC comparative effectiveness study was NOT done. This document does not describe a study involving AI assistance for human readers.

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

   • No, a standalone algorithm performance study was NOT done. This document describes a medical imaging device (MRI system), not an AI algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc):

   • Not applicable in the context of an AI algorithm. For the MRI system itself, the "ground truth" for demonstrating substantial equivalence relies on established industry standards (NEMA, IEC) for image quality, safety parameters (e.g., acoustic noise, SAR), and the system's ability to produce images useful for diagnosis, interpreted by a "trained physician". This is not a ground truth for a specific diagnostic outcome.

8. The sample size for the training set:

   • Not applicable. This document describes an MRI system, not an AI model requiring a training set. The changes are primarily software version updates and new coils for an existing hardware platform.

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

   • Not applicable. See point 8.

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The Hitachi Oasis MRI with Spectroscopy is intended for use as a non-invasive diagnostic device that provides information based on relative concentrations of metabolites in body tissues. This NMR data in the form of spectra or spectral images reflect the NMR properties of proton density, spin-lattice relaxation time (T1), spin-spin relaxation time (T2), and chemical shift. When interpreted by a trained medical practitioner, these spectral data provide information that can be useful in diagnosis determination.

This package is indicated for use as follows:

Anatomical Region: Head, whole body Nuclei Excited: "H

MR Spectroscopy is an imaging and analysis feature that can provide unique information about tissue within a human body. Spectroscopy can be used to complement or augment information and images obtained through other MR imaging and analysis techniques.

The acquisition of spectroscopic information is fundamentally the same as for other MR imaging techniques. For example, a modified spin echo sequence is used to collect data for one or more voxels of tissue. This data collection utilizes existing MR hardware and software, for example, the main magnetic field, gradient coils, RF transmitter, RF receiver coils, and memory or "K-Space".

The analysis of the collected data is what differentiates MR Spectroscopy from other more conventional MR imaging and analysis techniques. The data that is collected from the MR pulse sequence as described above is processed through MR spectroscopy algorithms. After computation and analysis on this data, information is displayed for the operator/reviewer. Spectroscopy data may be displayed in multiple ways, for example as data, graphs or images.

Here's an analysis of the provided text regarding the acceptance criteria and supporting study for the Hitachi Oasis MRI with Spectroscopy:

Device: Oasis MRI with Spectroscopy

1. Table of Acceptance Criteria and Reported Device Performance

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

The provided documentation does not specify a separate test set, its sample size, or the provenance of any data used for a performance study. The submission relies on substantial equivalence to a predicate device.

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

This information is not provided as the submission relies on substantial equivalence and does not detail a separate performance study with a test set requiring expert-established ground truth.

4. Adjudication Method for the Test Set

This information is not provided as the submission relies on substantial equivalence and does not detail a separate performance study with a test set.

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

A multi-reader multi-case (MRMC) comparative effectiveness study was not performed or reported in this submission. The basis for approval is substantial equivalence to a predicate device, not a head-to-head comparison demonstrating improved human reader performance with AI assistance.

6. Standalone (Algorithm Only) Performance Study

A standalone performance study of the algorithm without human-in-the-loop performance was not performed or reported in this submission. The document describes the system as a diagnostic device where the spectral data is "interpreted by a trained medical practitioner."

7. Type of Ground Truth Used

The concept of a "ground truth" for a performance study is not directly applicable or mentioned in this submission, as the basis for approval is substantial equivalence to a predicate device rather than a de novo clinical performance study against a defined ground truth. The device's utility is described as providing information useful in diagnosis when interpreted by a medical practitioner.

8. Sample Size for the Training Set

This information is not provided. The submission does not describe the development or training of new algorithms for which a training set would be required. It focuses on the capabilities of the spectroscopy package using existing MR hardware and software.

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

This information is not provided, as a training set and its ground truth establishment are not discussed in the context of this substantial equivalence submission.

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