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510(k) Data Aggregation
(75 days)
BoneVue is a software tool for 3D visualization of bone micro-architecture using MRI datasets with the possibility of manual or automated measurements of descriptive parameters regarding cortical and trabecular morphology. BoneVue is a workflow tool intended to assist a trained physician in the assessment of high-resolution MRI datasets of bone.
BoneVye evaluates high-resolution MRI datasets containing bone tissue and provides 3D visualization of trabecular structures as well as measurements of descriptive parameters regarding cortical and trabecular morphology.
The following cortical measurements are reported: average cortical inner diameter, average cortical outer diameter, and average cortical thickness.
The following trabecular measurements are reported: average measures of bone volume/total volume, trabecular thickness, trabecular number, and trabecular separation.
The 3D visualization module is used to display a high resolution 3D model of the trabecular bone and its micro-architecture. The 3D visualization helps a trained physician make a qualitative assessment of bone micro-architecture, which may be viewed from different angles. BoneVue allows standard surface rendering views as well as standard maximum intensity projection views.
Additionally, BoneVue offers the display of a standard "bone plug", a surface rendering of a central cylinder of trabecular tissue, as a representative sample of the bone microarchitecture.
This submission for MicroMRI's BoneVue Software v1 provides a limited amount of information regarding its performance testing. Based on the provided text, a comprehensive study description is not available.
Here's an analysis of what can be extracted and the information that is not present:
1. Table of Acceptance Criteria and Reported Device Performance:
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Meets established acceptance criteria (general statement) | BoneVue has been successfully tested and has met acceptance criteria previously established in accordance with documented procedures. |
Missing Information: The document does not specify what the actual quantitative or qualitative acceptance criteria were, nor does it provide any specific numerical or categorical results for the device's performance against these criteria. It only states that the criteria were met.
2. Sample Size Used for the Test Set and Data Provenance:
Missing Information: The document does not state the sample size used for the test set or the provenance (country of origin, retrospective/prospective) of the data used in performance testing.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications:
Missing Information: The document does not mention the number of experts, their qualifications, or how ground truth was established for the test set.
4. Adjudication Method for the Test Set:
Missing Information: The document does not describe any adjudication method used for the test set.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:
Missing Information: The document does not mention whether an MRMC comparative effectiveness study was conducted, nor does it provide any effect size for human readers with or without AI assistance. The device is described as a "workflow tool intended to assist a trained physician," implying human-in-the-loop, but no comparative study is detailed.
6. Standalone (Algorithm Only) Performance Study:
Missing Information: The document does not detail a standalone performance study. The description focuses on its function as a tool to assist trained physicians, rather than as an independent diagnostic algorithm.
7. Type of Ground Truth Used for the Test Set:
Missing Information: The document does not specify the type of ground truth used (e.g., expert consensus, pathology, outcomes data).
8. Sample Size for the Training Set:
Missing Information: The document does not provide any information about a training set or its sample size.
9. How Ground Truth for the Training Set Was Established:
Missing Information: As no training set is mentioned, there is also no information on how its ground truth might have been established.
Summary of what is known from the provided text:
- Device: BoneVue Software v1, developed by MicroMRI, Inc.
- Purpose: 3D visualization of bone micro-architecture from MRI datasets and measurements of cortical and trabecular morphology.
- Indication for Use: A workflow tool to assist a trained physician in assessing high-resolution MRI datasets of bone.
- Performance Claim: "BoneVue has been successfully tested and has met acceptance criteria previously established in accordance with documented procedures." (This is a general statement of compliance, not a detailed report of the study itself.)
Crucially, the provided 510(k) summary does not contain the detailed study that proves the device meets specific acceptance criteria. It only states that such testing was performed and successful. Regulatory submissions often summarize performance testing rather than including the full study report.
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(73 days)
The MicroMRI Wrist Coll is a transmitting and receiving device to be used in conjunction with GE Healthcare and Siemens 1.5T MRI systems to image the wrist. The images acquired using the MicroMRI Wrist Coil along multiple axes (axial, coronal, sagittal, and oblique) are a measure of the spatial distribution of protons (hydrogen nuclei) exhibiting magnetic resonance. The NMR properties that determine the spatial image are proton density, spin-lattice (TI) and spin-spin (T2) relaxation times, flow velocity and chemical shift. These wrist MRU images when interpreted by a trained physician can be useful in determining a diagnosis. The MicroMRI Wrist Coil cannot be used with an MRI system that supports the use of only receive coils.
The MicroMRI Wrist Coil uses an elliptical birdcage design to provide the highest possible signal-to-noise ratio (SNR) for imaging a human wrist. The two piece structure facilitates the use of the coil for a wide range of wrist sizes. The elliptical design and ability to conform to the subject's wrist makes the coil unique and efficient in transmitting and receiving the RF signal to the anatomy (i.e., the wrist).
The accompanying immobilization system provides a means to stabilize the patient's arm during the scan. The immobilization system provides a means for positioning the patient's arm consistently in the same position and orientation.
Here's an analysis of the provided text regarding the MicroMRI Wrist Coil, focusing on acceptance criteria and study details:
Device: MicroMRI Wrist Coil
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Safety | Performance testing was conducted in accordance with IEC/UL/CSA/EN 60601-1 standards. |
| Performance | Performance testing was conducted in accordance with NEMA standards. The performance test results indicate that the MicroMRI Wrist Coil is comparable to the predicate devices (Mayo Clinic BC-10 1.5T wrist coil and Invivo HRW Array Coil). |
| Equivalence (Primary differences from predicates) | Vs. BC-10 1.5T Wrist Coil: MicroMRI Wrist Coil scans only the wrist, while BC-10 can scan hand and wrist. Vs. HRW Array Coil: MicroMRI Wrist Coil is a transmit and receive coil (quadrature birdcage design, requiring less power), while HRW Array Coil is a receive-only coil. Despite these differences, the device is considered substantially equivalent. |
| Intended Use | The device is a transmitting and receiving device for imaging the wrist with GE Healthcare and Siemens 1.5T MRI systems. Images are used by trained physicians for diagnosis. (Device performance meets this intended use implicitly through equivalence to predicate devices for similar wrist imaging). |
| No New Indications | The device does not include any new indications for use. |
| No New Safety/Efficacy Issues | The device does not present any new safety or efficacy issues. |
2. Sample Size Used for the Test Set and Data Provenance
The provided text does not specify a sample size for a test set. This submission is for a medical device (an MRI coil), and the performance evaluation focused on adherence to engineering and safety standards (NEMA, IEC/UL/CSA/EN 60601-1) and comparison to predicate devices, rather than a clinical study with a patient test set in the traditional sense for an AI/algorithm.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications of Those Experts
This information is not applicable to this submission. The device is hardware (an MRI coil), not an AI algorithm that would require expert-established ground truth on a test set of medical images. The "ground truth" related to this device's performance would be derived from physical measurements according to engineering standards.
4. Adjudication Method for the Test Set
This information is not applicable for the reasons stated in point 3.
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 device is an MRI coil, not an AI algorithm designed to assist human readers or for standalone diagnosis.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done
No, a standalone performance study was not done. This device is a hardware component (an MRI coil), not an algorithm.
7. The Type of Ground Truth Used
The "ground truth" for this device's performance relies on:
- Engineering Standards: Adherence to NEMA and IEC/UL/CSA/EN 60601-1 standards for safety and performance (e.g., signal-to-noise ratio, power requirements, electrical safety).
- Comparison to Predicate Devices: Demonstrating comparable performance characteristics to existing, legally marketed MRI wrist coils. This implicit ground truth is that if it performs like a legally marketed predicate device, it is acceptable.
8. The Sample Size for the Training Set
This information is not applicable. This device is a hardware component and does not utilize a "training set" in the context of machine learning or AI.
9. How the Ground Truth for the Training Set was Established
This information is not applicable for the reasons stated in point 8.
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