Search Results

The dS Knee Coil 8ch 1.5T is to be used in conjunction with Philips 1.5T Magnetic Resonance Scanner to produce diagnostic images of Knee that can be interpreted by a trained physician.

The dS Knee Coil 8ch 1.5T is an 8 channel phased array, receive only coil with a rigid volume and designed to be used in conjunction with 1.5T Magnetic Resonance Scanner for high resolution imaging of the left or right knee. The split design and single handle allows easy setup. The coil can be slightly rotated relative to its base plate to ease coil setup and enhance patient comfort. The coil conforms snugly to the anatomy for excellent signal-to-noise ratio. This coil is used independently and cannot be combined with any other coil and is available for Philips 1.5T Prodiva and MR 5300 MR Systems.

The provided text K242879 is a 510(k) summary for the Philips dS Knee Coil 8ch 1.5T. It describes the device, its indications for use, and a summary of performance data. However, this document does not contain the detailed clinical study results or acceptance criteria in the format requested, particularly for an AI/software-based medical device performance evaluation.

The document states:

• "Acquired Image quality was assessed by a U.S. Board Certified radiologist to confirm images produced on the subject coil are sufficient quality for diagnostic use."
• "Substantially equivalent performance is demonstrated by meeting all criterion in the quidance "Magnetic Resonance (MR) Receive-only Coil –Performance Criteria for Safety and Performance Based Pathway" issued on December 11, 2020."

This indicates that the performance evaluation for this hardware device (a medical coil) focused on image quality for diagnostic use, rather than the performance of an AI algorithm or software. Therefore, many of the requested elements, such as MRMC studies, standalone algorithm performance, ground truth establishment for training, and sample sizes for AI model development, are not applicable or detailed in this document because it's not an AI/ML device submission.

Based only on the provided text, I can infer some information relevant to the intent behind your questions, but I cannot provide the specific details of an AI/ML study.

Here's how I can interpret the provided text in the context of your request, filling in with "Not Applicable" or "Not specified" where the document does not provide the information for an AI/ML device:

Study Type: This is a performance study for a hardware medical device (MRI coil), not an AI/ML software device. The evaluation focuses on assuring the generated images are of diagnostic quality.

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

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

• Sample Size: Not specified for the clinical image quality assessment. The document only mentions "Acquired Image quality was assessed."
• Data Provenance: Not specified. It's likely prospective imaging data acquired with the device for validation, but the document doesn't explicitly state this.

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)

• Number of experts: Singular ("a U.S. Board Certified radiologist").
• Qualifications: "U.S. Board Certified radiologist." Specific experience level (e.g., 10 years) is not mentioned.

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

• Adjudication method: Not applicable/not specified. The assessment appears to be a single read. This is a hardware image quality assessment, not an AI diagnostic study usually requiring complex adjudication.

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, this was not an MRMC study. It was an assessment of the image quality produced by the coil, not a study comparing human reader performance with or without AI assistance.
• Effect size: Not applicable.

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

• Standalone performance: Not applicable. This is not an AI algorithm. The performance being evaluated is the image generation capability of the MRI coil.

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

• Type of ground truth: Expert assessment by a U.S. Board Certified radiologist to confirm "sufficient quality for diagnostic use." This is a form of expert consensus on image utility, rather than a clinical ground truth for a specific diagnosis (like pathology).

8. The sample size for the training set

• Sample Size for Training Set: Not applicable. This is a hardware device, not an AI/ML model that requires a training set.

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

• Ground Truth for Training Set: Not applicable.

Ask a specific question about this device