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The ACHIEVA R4 1.5T and ACHIEVA R4 3.0T are magnetic resonance diagnostic devices that produce cross-sectional images, spectroscopy images and/or spectra in any orientation of the internal structure of the whole body. These images when interpreted by a trained physician, yield information that may assist in diagnosis. In addition, the ACHIEVA R4 1.5T and ACHIEVA R4 3.0T devices provide capabilities to perform interventional procedures in the head, body and extremities, which may be facilitated by MR techniques, such as real time imaging. Such procedures must be performed with MR compatible instrumentation as selected and evaluated by the clinical user.

Device Description

The next generation Philips MR platform consists of either a 1.5T or 3.0T field generating superconducting unit. The radiofrequency receive chain consists of multiple coil types delivering a simplified and unique data acquisition system. The system is configured with a time-varying magnetic field system (gradients). Additional RF transmission is provided through an integrated RF body coil. The base software for the above mentioned system will be called Release 4. The magnetic resonance diagnostic device is used to produce cross-sectional images, spectroscopic imaging and/or spectra in any orientation of the internal structures of the whole body. These images when interpreted by a trained physician, yield information that may assist in a diagnosis. In addition, the device provides the capabilities to perform interventional procedures in the head, body and extremities, which may be facilitated by MR techniques, such as real time imaging. Such procedures must be performed with MR compatible instrumentation as selected and evaluated by the clinical user.

AI/ML Overview

This submission is a 510(k) Pre-Market Notification for a Magnetic Resonance Diagnostic Device (MRDD). Unlike AI/ML device submissions, which often involve specific performance metrics like sensitivity and specificity, this type of submission focuses on demonstrating substantial equivalence to a predicate device. Therefore, the typical "acceptance criteria" and "device performance" in terms of clinical accuracy are not directly applicable in the same way. The "study" here refers to the comparisons made to establish substantial equivalence.

Here's the breakdown based on the provided text:

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

Since this is a 510(k) for an MRDD, the "acceptance criteria" and "reported device performance" are primarily focused on demonstrating that the new device (ACHIEVA R4 1.5T and ACHIEVA R4 3.0T) is equivalent to its predicate devices in terms of intended use, technological characteristics, and safety and effectiveness.

Acceptance Criteria (Implicit for MRDD 510(k))	Reported Device Performance / Declaration
Intended Use Equivalence: The device must have the same intended use as the predicate device(s).	The ACHIEVA R4 1.5T and ACHIEVA R4 3.0T are magnetic resonance diagnostic devices that produce cross-sectional images, spectroscopy images and/or spectra in any orientation of the internal structure of the whole body. These images when interpreted by a trained physician, yield information that may assist in diagnosis. In addition, the devices provide capabilities to perform interventional procedures in the head, body and extremities, which may be facilitated by MR techniques, such as real time imaging. This mirrors the stated intended use of the primary predicate device (K063559 and K043147).
Technological Characteristics Equivalence: The device must have similar technological characteristics or any differences must not raise new questions of safety or effectiveness.	The next generation Philips MR platform consists of either a 1.5T or 3.0T field generating superconducting unit. The radiofrequency receive chain consists of multiple coil types delivering a simplified and unique data acquisition system. The system is configured with a time-varying magnetic field system (gradients). Additional RF transmission is provided through an integrated RF body coil. The base software for the above mentioned system will be called Release 4. These descriptions indicate a successor model with enhancements over the predicate devices (ACHIEVA 1.5T and ACHIEVA 3.0T MR systems Release 2.5-series). The submission implicitly argues these enhancements do not alter the fundamental technological characteristics to a degree that compromises safety or effectiveness.
Safety and Effectiveness Equivalence: The device must be as safe and effective as the legally marketed predicate device(s).	"The ACHIEVA R4 1.5T and ACHIEVA R4 3.0T do not induce any other risks than already indicated for their predicate devices with the same safety and effectiveness." and "It is the opinion of Philips Medical Systems that the Philips ACHIEVA R4 1.5T and ACHIEVA R4 3.0T are substantially equivalent to their predicate device ACHIEVA 1.5T and ACHIEVA 3.0T MR systems Release 2.5-series." The performance standards referenced (NEMA voluntary standards, FDA MR Diagnostic Device Guidance, UL and IEC 60601) support the claim of safety and effectiveness.
Predicate Device Identification: Clear and appropriate identification of predicate devices.	The predicate devices are clearly identified as ACHIEVA 1.5T and ACHIEVA 3.0T MR systems Release 2.5-series with FDA references K063559, K043147, K041602, K052078, and K013344.
Use with imaging agents: Consistent or equivalent use of imaging agents.	The subject device indicates "gadolinium-based contrast media" for imaging agent use, which is consistent with the predicate devices that state "any gadolinium-based contrast agent."

The subsequent points (2-9) are typical for AI/ML device studies involving clinical validation. For this specific 510(k) submission for an MRDD, these points are largely not applicable in the context of a dedicated clinical performance study as one would see for an AI algorithm. The determination of substantial equivalence for an MR Diagnostic Device typically relies on a comparison of technical specifications, intended use, and a demonstration that any technological differences do not raise new questions of safety or effectiveness. There is no mention of a separate "test set" or "ground truth" derived by experts for clinical performance evaluation of the device's diagnostic output as if it were an AI algorithm.

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

Not Applicable. This submission does not describe a clinical performance study with a test set in the conventional sense used for AI/ML algorithms. Equivalence is based on comparison to predicate devices and adherence to performance standards.

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. No ground truth establishment by experts for a test set is described. The "ground truth" here is the established safety and effectiveness of the predicate devices.

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

Not Applicable. No test set or corresponding adjudication method is discussed.

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

Not Applicable. This is not an AI-assisted device, and no MRMC study is mentioned.

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

Not Applicable. This is a Magnetic Resonance Diagnostic Device (hardware and integrated software for image acquisition/reconstruction), not a standalone AI algorithm.

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

Not Applicable. The "ground truth" for this 510(k) is the regulatory acceptance and established safety/effectiveness of the predicate MR systems themselves, demonstrated through their successful clearance and long-term use.

8. The sample size for the training set

Not Applicable. This is not an AI/ML device, so there is no training set mentioned. The development process for an MRI system involves engineering design, testing against technical specifications, and adherence to regulatory standards rather than machine learning training.

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

Not Applicable. As there is no training set for an AI/ML algorithm, there is no discussion of how ground truth was established for it.

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