(94 days)
The Contour Hand/Wrist is intended for use with Siemens 0.55T MR systems to produce diagnostic images of hand and wrist anatomy that can be interpreted by a trained physician.
The Contour Hand/Wrist is a receive-only, 12-channel phased array coil designed for magnetic resonance imaging (MRI) using the Siemens 0.55T MR systems. The Contour Hand/Wrist is intended to be used for imaging hand and wrist anatomy.
The provided text describes a 510(k) premarket notification for a medical device called "Contour Hand/Wrist (Q7000232)," which is a magnetic resonance (MR) diagnostic device. The documentation focuses on demonstrating substantial equivalence to a legally marketed predicate device, the "Contour Knee." However, it does not contain the detailed information required to fully answer all aspects of your request regarding acceptance criteria and the study that proves the device meets them, particularly for a software-based AI device.
The device described, "Contour Hand/Wrist," is a hardware component (a receive-only, 12-channel phased array coil) for an MRI system, not an AI software. The performance testing mentioned (SNR, uniformity, image quality) is typical for MR coils, not AI algorithms.
Therefore, many of your specific questions, especially those related to AI algorithm performance (e.g., MRMC study, standalone algorithm performance, AI assistance effect size, training set details, ground truth for AI) cannot be answered from this document.
However, I can extract information relevant to the device's performance assessment as described:
1. Table of Acceptance Criteria and Reported Device Performance
Based on the document, the acceptance criteria are related to the physical characteristics and imaging performance of the MRI coil. The document states:
| Criteria Category | Acceptance Criteria (Stated) | Reported Device Performance (Implied/Stated) |
|---|---|---|
| Signal-to-Noise Ratio (SNR) | Pre-determined acceptance criteria per NEMA MS-9 (using alternate method 2,5 from MS-6) | "The SNR... were measured... and analyzed per NEMA MS-9... This testing demonstrates that the Contour Hand/Wrist performs as well as or better than the predicate device." |
| Image Uniformity | Pre-determined acceptance criteria per NEMA MS-9 (primary method from MS-6) | "Uniformity was analyzed using NEMA MS-9... This testing demonstrates that the Contour Hand/Wrist performs as well as or better than the predicate device." |
| Diagnostic Image Quality | Produce diagnostic quality images of the intended anatomy (hand/wrist) | "Clinical images from volunteer scanning of hand/wrist anatomy were obtained... These images were used to demonstrate that the Contour Hand/Wrist produces diagnostic quality images of the intended anatomy." |
| Electrical Safety | Compliance with IEC standards | "The electrical safety... data support the safety of the Contour Hand/Wrist and the bench testing per the IEC standards..." |
| Electromagnetic Compatibility (EMC) | Compliance with IEC standards | "The... electromagnetic compatibility... data support the safety of the Contour Hand/Wrist and the bench testing per the IEC standards..." |
| Biocompatibility | Compliance with standards (implied) | "The... biocompatibility data support the safety of the Contour Hand/Wrist..." |
| Adverse Events | No adverse events reported/recorded | "No adverse events were reported or recorded." |
2. Sample size used for the test set and the data provenance:
- Test Set (Clinical Images): The document states "clinical images from volunteer scanning of hand/wrist anatomy were obtained." It does not specify the sample size (number of volunteers or images).
- Data Provenance: The images were obtained from a Siemens 0.55T MR system in an unspecified location (likely the manufacturer's facility or a collaborating site). The document does not explicitly state if it was retrospective or prospective, but "volunteer scanning" implies prospective collection for the test.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Not Applicable in the AI Sense: This device is an MRI coil, not an AI algorithm that produces interpretations needing expert ground truth. The "diagnostic quality images" verification is implicitly against the expectation that a "trained physician" can interpret them.
- The document states the images "can be interpreted by a trained physician," implying the assessment of 'diagnostic quality' is based on the inherent ability of the images to be interpretable by medical professionals, rather than a comparison to a pre-established ground truth for specific pathologies. No explicit number of experts or their qualifications for assessing image quality are provided.
4. Adjudication method for the test set:
- Not Applicable: There is no indication of an adjudication method as would be used for clinical endpoints or AI algorithm performance assessment. The "diagnostic image quality" evaluation seems to be a qualitative assessment based on the technical characteristics of the images produced by the coil.
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: This is not an AI device. No MRMC study was conducted or is applicable for this type of device.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
- Not Applicable: This is a hardware component. It does not perform as a standalone algorithm.
7. The type of ground truth used:
- Implied Technical / Interpretability Ground Truth: For the "diagnostic image quality," the ground truth is the inherent quality, clarity, and detail present in the images that allows a "trained physician" to make a diagnosis. It's not a ground truth for specific disease presence, but rather for imaging capability. For SNR and Uniformity, the ground truth is the NEMA MS-9 standard.
8. The sample size for the training set:
- Not Applicable: This is not an AI device. There is no training set in the context of machine learning.
9. How the ground truth for the training set was established:
- Not Applicable: As above, no training set for AI.
In summary, this document is a 510(k) for an MRI hardware coil, not an AI diagnostic software. Therefore, most of the detailed questions regarding AI acceptance criteria and study design are not addressed and are not relevant to this specific premarket notification. The study described focuses on demonstrating the technical performance (SNR, uniformity) and diagnostic image quality of the MR coil itself.
§ 892.1000 Magnetic resonance diagnostic device.
(a)
Identification. A magnetic resonance diagnostic device is intended for general diagnostic use to present images which reflect the spatial distribution and/or magnetic resonance spectra which reflect frequency and distribution of nuclei exhibiting nuclear magnetic resonance. Other physical parameters derived from the images and/or spectra may also be produced. The device includes hydrogen-1 (proton) imaging, sodium-23 imaging, hydrogen-1 spectroscopy, phosphorus-31 spectroscopy, and chemical shift imaging (preserving simultaneous frequency and spatial information).(b)
Classification. Class II (special controls). A magnetic resonance imaging disposable kit intended for use with a magnetic resonance diagnostic device only is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 892.9.