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The Siemens Magnetom CP Angio Array Coil is indicated for use as a diagnostic imaging device accessory to produce transverse, saggital, coronal and oblique images of vascular structures and soft tissue of the abdomen, pelvis and lower limbs. The design of this coil facilitates imaging larger peripheral vascular areas.

The CP Peripheral Angio Array Coil is a receiver only coil for the Magnetom Symphony system.

The provided text describes a 510(k) submission for a medical device (CP Peripheral Angio Array Coil for the Magnetom Symphony). It focuses on establishing substantial equivalence to a predicate device, rather than defining specific performance acceptance criteria for a novel AI or diagnostic algorithm and then proving the device meets them through a dedicated study.

Therefore, many of the requested elements (acceptance criteria table, detailed study design for performance, sample sizes for test/training sets, ground truth methodology, expert qualifications, adjudication methods, MRMC studies, standalone performance) are not present in the provided document.

Here's an analysis of what is available and what isn't:

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

• Not present in this format. The document describes a general approach to demonstrating "substantial equivalence" based on safety and effectiveness.

• Implied Acceptance Criteria (based on predicate device): The closest the document comes to acceptance criteria are the safety and performance parameters that are stated to be "unaffected by the modifications" and measured to be "equivalent to the currently available Siemens coils." These are:

  • Maximum Static Field Strength
  • Rate of Change in Magnetic Field
  • RF Power deposition
  • Acoustic Noise Levels
  • Specification Volume
  • Signal to Noise (SNR)
  • Image Uniformity
  • Geometric distortion
  • High contrast spatial resolution
  • Slice profile, thickness and gap

• Reported Device Performance:

  • "The new CP peripheral Angio Array Coil was tested for SNR and Image uniformity and the results presented in this submission show that they are equivalent to the currently available Siemens coils." (No specific numerical values are given, only a statement of equivalence).
  • For the other listed parameters (Static Field Strength, Rate of Change, RF Power, Acoustic Noise, Specification Volume, Geometric distortion, High contrast spatial resolution, Slice profile, thickness and gap), the document states they are "unaffected by the modifications," implying they remain within acceptable limits as established for the predicate device.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Not explicitly stated in the provided text. The document mentions "Laboratory and clinical testing were performed," but no details on sample size, data provenance, or study design (retrospective/prospective) are provided for these tests.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• Not applicable/Not present. This device is an imaging coil, not an interpretive AI algorithm. The "ground truth" concept as typically applied to AI performance studies (e.g., medical diagnosis) does not directly apply here. Instead, performance is assessed against physical and imaging quality metrics. There is no mention of human experts establishing a diagnostic ground truth for comparison.

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

• Not applicable/Not present. As above, this is not a diagnostic device with an AI component requiring expert adjudication for ground truth.

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 a study of AI-assisted human reading. It is a study to demonstrate substantial equivalence of an MRI coil's physical and technical performance.

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

• Yes, in spirit, but not for an "algorithm." The "standalone" performance assessment here refers to the engineering and imaging quality tests of the coil itself (SNR, Image Uniformity, etc.) performed in a laboratory or clinical setting (without human diagnostic interpretation being the primary endpoint). The statement "The new CP peripheral Angio Array Coil was tested for SNR and Image uniformity and the results presented in this submission show that they are equivalent to the currently available Siemens coils" indicates this standalone testing.

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

• Engineering/Physics-based measurement standards. For parameters like SNR, Image Uniformity, Static Field Strength, etc., the "ground truth" would be derived from established physical measurement techniques and engineering specifications, often compared against performance benchmarks of the predicate device or industry standards. It's not a clinical ground truth like pathology or outcomes data.

8. The sample size for the training set

• Not applicable/Not present. This device is an imaging coil; there is no "training set" in the context of machine learning or AI algorithms.

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

• Not applicable/Not present. As above, no training set or associated ground truth establishment is described for this type of device.

In summary:

The provided document is a 510(k) summary for an MRI coil, focused on demonstrating substantial equivalence to a predicate device. It highlights that the new coil maintains the safety and effectiveness characteristics of the predicate, as evidenced by laboratory and clinical testing on physical and imaging parameters (like SNR and image uniformity). It does not contain the kind of detailed performance study information typically associated with AI-driven diagnostic devices, such as specific acceptance criteria for a diagnostic task, sample sizes for AI model validation, expert ground truth establishment, or human-reader studies.

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