Search Results

The Optima MR430s MR Scanner is indicated to use as a magnetic resonance imaging device of the leg (excluding the thigh), knec, ankle, foot, elbow, forearm, wrist, and hand. The device produces transverse, sagittal, coronal, and oblique cross-sectional images, displaying the internal structure of the limbs and joints being imaged. If interpreted by a medical expert, these images can provide diagnostically useful information.

The Optima MR430s MRI Scanner utilizes a superconducting magnet to acquire 2D single-slice and multi-slice and 3D volume images. A wide variety of pulse sequences are provided to the operator, including spin echo, fast spin echo, 2D and 3D gradient echo acquisitions. Imaging options such as inversion recovery, flow compensation and fat/water suppression are provided to suppress artifacts due to physiological motion and improve image quality. The system is used as a stationary system.

The provided text describes the regulatory filing for the Optima MR430s MRI Scanner. It highlights that the device is substantially equivalent to a predicate device (MSK Extreme, K080048) and that non-clinical and clinical tests were performed to demonstrate its safety and effectiveness. However, the document does not contain specific quantitative acceptance criteria or detailed results of a study designed to prove the device meets such criteria in the way a clinical performance study for an AI algorithm would.

Instead, the document focuses on the substantial equivalence of the Optima MR430s to a predicate device, as per FDA 510(k) requirements. This means the primary "acceptance criteria" here relate to demonstrating that the new device is as safe and effective as the previously cleared predicate device, rather than achieving specific performance metrics against a defined ground truth.

Therefore, many of the requested sections below cannot be fully answered as they pertain to a type of study not detailed in this submission for this specific medical device (an MRI scanner, not an AI diagnostic algorithm).

Here's an attempt to extract and infer information based on the provided text, with significant caveats for the sections not directly addressed:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size (Clinical): "A non-significant risk study was performed to characterize onset levels for Peripheral Nerve Stimulation (PNS)..." and "The sample DICOM images included in this submission were also collected as part of a non-significant risk study." The exact number of subjects or images collected for these studies is not specified in the provided text.
• Data Provenance: Not explicitly stated (e.g., country of origin). The studies appear to be prospective as they were specifically performed for verification testing.

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

• This information is not detailed in the provided text. The document refers to images providing "diagnostically useful information" if "interpreted by a medical expert," but does not describe the specific process of establishing ground truth for the verification studies or the number/qualifications of experts involved. Given the nature of a 510(k) for an MRI scanner (not an AI diagnostic tool), this type of ground truth establishment is typically not a primary focus of the regulatory submission in the way it would be for an AI device.

4. Adjudication Method for the Test Set

• Not specified.

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 as described for an AI device. This submission is for an MRI scanner, not an AI diagnostic algorithm or an AI-assisted reading system. Therefore, the concept of human readers improving with AI assistance is not applicable here.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

• Not applicable. This device is an MRI scanner which produces images for interpretation by a human expert. It is not a standalone algorithm.

7. The Type of Ground Truth Used

• For the PNS study, the "ground truth" was the subject's reported sensation (or lack thereof) in response to the gradient slew rate changes.
• For the image quality assessment, the "ground truth" is implicitly tied to the established diagnostic utility of images from the predicate device and the ability of "medical experts" to interpret the images effectively. No specific pathology or outcomes data as "ground truth" is mentioned in the context of a formal validation study in this summary.

8. The Sample Size for the Training Set

• Not applicable. There is no mention of a "training set" in the context of an AI algorithm within this submission for an MRI scanner. The device is hardware-based.

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

• Not applicable. (See point 8.)

Ask a specific question about this device