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The Philips Gyroscan NT systems are indicated for use as diagnostic devices that produce transverse, sagittal, coronal, and oblique cross-sectional images, spectroscopic images and/or spectra, based upon 1H and 31P metabolites, and that display the internal structure and/or function of the head, body, or extremities. These images and/or spectra when interpreted by a trained physician, yield information that may assist in diagnosis.

The GYROSCAN T5-NT (0.5T), GYROSCAN T10-NT (1.0T), GYROSCAN ACS-NT (1.5T) Release 5 series are designed and manufactured to comply with the relevant safety standards. Adequate safety precautions include RF-limit protection, rate of gradient change, and selection/decoupling circuitry for the applicable coils. The systems, as their predecessors, are indicated for use as diagnostic devices producing transverse, sagittal, coronal, and oblique cross-sectional images and displaying the internal structure of the head, body, or extremities. These images, when interpreted by a trained physician, yield information useful in the determination of diagnosis. MR Imaging utilizes mature technology to visualize images with which the industry and users have many years of experience. A Comprehensive Operator's Manual contains adequate instructions and provides sufficient cautions and warnings to ensure safe operation. The software used in Release 5 is equivalent to the software used in the predicate device. The hardware used in Release 5 is identical to the predicte device.

Based on the provided text, the Philips GYROSCAN NT systems (T5-NT, T10-NT, and ACS-NT Release 5 series) are Magnetic Resonance (MR) Imaging devices. The information provided is a 510(k) summary, which focuses on demonstrating "substantial equivalence" to a predicate device, rather than detailed performance criteria and a specific study proving those criteria were met in the way a clinical trial for a new therapeutic would.

Here's an attempt to extract and interpret the information based on the typical requirements for such a request, acknowledging the limitations of a 510(k) summary for this type of detailed performance criteria.

1. Table of Acceptance Criteria and Reported Device Performance

The 510(k) summary does not explicitly state quantitative "acceptance criteria" for diagnostic accuracy (e.g., sensitivity, specificity, accuracy percentages) for the GYROSCAN NT systems, nor does it provide a specific study that reports these metrics. Instead, the "acceptance criteria" implied by a 510(k) submission are related to:

• Safety Standards Compliance: The device is designed and manufactured to comply with relevant safety standards.
• Equivalence to Predicate Device: The device performs as well as or similarly to a legally marketed predicate device.
• Expected Diagnostic Utility: The images produced, when interpreted by a trained physician, yield information useful in diagnosis, similar to MR imaging devices already on the market.

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

The provided 510(k) summary does not describe a specific "test set" of patient data or images used to evaluate the device's diagnostic performance in a clinical study. Substantial equivalence for devices like MR systems often relies on technical comparisons to predicate devices, adherence to standards, and sometimes phantom testing or limited clinical scanning to confirm expected performance, rather than a large-scale diagnostic accuracy study on a patient cohort that would have a "test set" in the context of AI product development.

• Sample Size: Not applicable/Not mentioned for a diagnostic performance test set.
• Data Provenance: Not applicable/Not mentioned.

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

Since a specific test set for diagnostic accuracy was not detailed, information about experts for ground truth establishment is not provided. The general statement, "These images, when interpreted by a trained physician, yield information useful in the determination of diagnosis," implies the expected use case, but not a formal ground truth process for a study.

• Number of Experts: Not applicable/Not mentioned.
• Qualifications of Experts: Not applicable/Not mentioned.

4. Adjudication Method for the Test Set

As there is no detailed test set described for diagnostic performance, an adjudication method is not applicable and not mentioned.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No MRMC study comparing human readers with and without AI assistance is mentioned. This is an MR Imaging device itself, not an AI-assisted diagnostic tool built upon MR images.

• MRMC Study Done: No.
• Effect Size of Human Improvement with AI: Not applicable.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

This is an MR imaging device, not a standalone AI algorithm. It produces images for human interpretation. Therefore, a standalone algorithm performance study as typically understood for AI is not applicable.

• Standalone Study Done: No.

7. Type of Ground Truth Used

Given that a specific clinical diagnostic accuracy study evaluating the device's output against a defined ground truth is not detailed in the summary, "ground truth" in the clinical diagnostic sense is not explicitly described for the purpose of validating the device's diagnostic performance. The ground truth for MR imaging is ultimately established by clinical diagnosis, pathology, or outcomes, but the 510(k) focuses on the device's ability to produce images from which a physician can make such diagnoses.

• Type of Ground Truth: Not explicitly detailed as this type of study was not presented. The fundamental "ground truth" for MR imaging's utility is the ability of trained physicians to interpret the images for clinical diagnosis, pathology correlation, or patient outcomes.

8. Sample Size for the Training Set

The concept of a "training set" is typically associated with machine learning or AI models. This 510(k) submission is for a conventional medical imaging device (an MRI scanner). Therefore, there is no "training set" in the context of an algorithm learning from data.

• Sample Size: Not applicable.

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

Since there is no training set mentioned, this question is not applicable.

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