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The Infinion Software Features do not change the existing indications for the Infinion 1.5T MR Imaging Systems as defined below.

The Infinion 1.5T MR Imaging System is indicated for use as a NMR device that produces images that: (1) correspond to the distribution of protons exhibiting NMR, (2) depend upon the NMR parameters (proton density, flow velocity, spin-lattice relaxation time (T1), and spin-spin relaxation time (T2)) and (3) display the soft tissue structure of the head and whole body. When interpreted by a trained physician, these images vield information that can be useful in the determination of a diagnosis.

Diffusion tensor imaging is indicated for use by producing magnetic resonance (MR) images whose contrast is dependent on the local diffusion coefficient of water. Diffusion tensor imaging can be used to image the directional dependence of the diffusion coefficient in tissue with restricted diffusion from tissues with normal diffusion.

Philips's Infinion Software Features include rapid imaging SENSE, coil uniformity improvements of CLEAR, rapid imaging using the b-FFE technique, acoustic noise reduction of SofTone and other improvements to current imaging techniques.

Here's an analysis of the provided text regarding the acceptance criteria and supporting study for the Infinion Software Features:

Note: This 510(k) summary is for a software update to an existing Magnetic Resonance Imaging (MRI) system. The acceptance criteria and "study" described are primarily based on the principle of substantial equivalence to predicate devices, rather than a traditional clinical study with novel performance metrics. The software features aim to improve existing functionalities (rapid imaging, uniformity, noise reduction, and diffusion tensor imaging) without changing the fundamental indications for use of the MRI system. Therefore, the "acceptance criteria" revolve around demonstrating that these new features perform equivalently or better than the predicate technology, without introducing new safety or effectiveness concerns.

Description of Acceptance Criteria and Supporting Study

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for the Infinion Software Features are detailed by demonstrating substantial equivalence to predicate devices (Philips Infinion 1.5T MR Imaging System (K003853), Philips Intera (R7.5) Software Package (K001796), and General Electric (K003573)). The "reported device performance" is implicitly that the Infinion Software Features achieve the same functional performance characteristics as the predicate devices for specific parameters.

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

The document does not explicitly describe a "test set" in the context of a prospective clinical study with a specific sample size. The evidence for "acceptance criteria" and "device performance" is primarily based on the comparison of technical specifications and intended use with previously cleared predicate devices. The phrase "similar in technology characteristics and intended used" indicates the evaluation was against established performance of marketed devices rather than a new data set generated for the Infinion Software Features alone.

Therefore, there is no specified sample size for a test set or data provenance in the sense of patient data in this 510(k) summary. The "data provenance" for the predicate devices would be derived from their original clearance documents (K003853, K001796, K003573).

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

There is no mention of experts being used to establish ground truth for a test set for the Infinion Software Features. The "ground truth" for demonstrating substantial equivalence relies on the established and accepted performance of the predicate devices, which were cleared based on their own prior documentation. The interpretation of MR images for diagnosis is explicitly stated to be done by a "trained physician," but this pertains to the general use of the MRI system, not the evaluation of these specific software features for 510(k) clearance.

4. Adjudication Method

There is no adjudication method described for a test set. The review process is based on comparing the new device's specifications and performance claims against those of the predicate devices.

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 MRMC comparative effectiveness study was done or mentioned in this document. This 510(k) summary predates the widespread regulatory focus on AI/ML-driven devices and reader performance studies. The improvements described, such as reduced scan time and improved uniformity, are inherent technical enhancements to the imaging process, not direct AI-driven assistance to human readers for diagnostic tasks.

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

This document does not describe a "standalone" algorithm-only performance study. The Infinion Software Features are enhancements to a human-operated MRI system. The performance is assessed in the context of the overall MRI system, with a trained physician interpreting the resulting images.

7. The Type of Ground Truth Used

The "ground truth" for the clearance of the Infinion Software Features is based on the established performance and safety characteristics of the predicate devices as previously cleared by the FDA. This is a regulatory "ground truth" of substantial equivalence rather than a clinical ground truth like pathology, expert consensus, or outcomes data specifically generated for these software features. The technical specifications and intended use of the Infinion Software Features were found to be "same" as the predicate devices.

8. The Sample Size for the Training Set

There is no mention of a training set sample size. The Infinion Software Features are described as technical improvements and new imaging techniques integrated into an existing MRI platform. They are not described as machine learning or AI algorithms that require a specific training data set in the conventional sense.

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

Since there is no mention of a training set, the establishment of ground truth for a training set is not applicable to this document.

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