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The ViewRay (MRIdian) Linac System, with magnetic resonance imaqinq capabilities, is intended to provide stereotactic radiosurgery and precision radiotherapy for lesions, tumors, and conditions anywhere in the body where radiation treatment is indicated.

The MRIdian Linac system delivers ionizing radiation using a magnetic resonance imaging system (MRIS) unit for image quidance and a 6MV linear accelerator to deliver radiation therapy. The system is designed so that the imaging and radiotherapy fields of view coincide permitting imaging of the patient at the radiotherapy isocenter before and during treatment. The MRIdian Linac system is used with the ViewRay Treatment Planning and Delivery System (TPDS) (K102915). As with the predicate MRIdian System (K111862), the MRIdian Linac System consists of three primary subsystems: The Treatment Planning and Delivery System (TPDS), The Magnetic Resonance Imaging System (MRIS), and The Radiation Therapy Delivery System (RDS). These three subsystems are designed to operate concurrently for accurate targeted administration of radiation therapy.

The provided document is a 510(k) summary for the ViewRay (MRIdian) Linac System (K162393), which is a medical device for radiation therapy. The purpose of this document is to demonstrate "substantial equivalence" to a predicate device, not necessarily to detail comprehensive acceptance criteria and a study to prove meeting them in the way a clinical trial or performance study for a novel device would.

Based on the provided text, the device itself is a radiation therapy system, not an AI/ML algorithm. Therefore, many of the typical AI/ML-specific questions (like sample size for test set, number of experts for ground truth, adjudication method, MRMC study, standalone performance) are not directly applicable or explicitly stated in this type of regulatory submission for this device. The "performance" being described is primarily related to the physical and functional characteristics of the radiation delivery system.

However, I can extract information related to demonstrating substantial equivalence and the performance characteristics presented.

Here's an interpretation based on the document:

1. Table of Acceptance Criteria and Reported Device Performance

The document compares the "Device with Change" (K162393, the MRIdian Linac System) to the "Cleared Device" (K111862, the predicate MRIdian System). The "acceptance criteria" can be inferred from the predicate device's performance, as the goal is to show the new device is "substantially equivalent." Where specific quantitative metrics are provided, those can be considered the performance reported for each device.

2. Sample size used for the test set and the data provenance:

• Sample Size: Not explicitly stated as a number of patients or cases. The "testing" referred to appears to be engineering and physics verification tests on the device itself, rather than studies involving patient data or a "test set" of images/cases in the AI/ML sense. The testing verified conformance to design requirements and safety standards.
• Data Provenance: Not applicable in the context of this device's validation as described (i.e., not a data-driven AI/ML device in the document). The testing is on the physical system's performance.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Not applicable/mentioned. "Ground truth" in this context refers to established engineering/physics standards (e.g., AAPM TG 119) and design specifications, not expert interpretations of medical images for an AI/ML diagnostic or therapeutic algorithm.

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

• Not applicable/mentioned. This refers to consensus methods for expert labeling, which is not relevant for the type of device validation presented here.

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. The device is a radiation therapy system, not an AI-assisted diagnostic or therapeutic interpretation tool requiring human-in-the-loop performance measurement.

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

• Not applicable as an "algorithm-only" performance in the AI/ML sense. The "standalone" performance is the device's operational characteristics as detailed in the table (e.g., dose accuracy, geometric accuracy). The document states, "Testing executed on the System verified conformance to design requirements and ensured all identified risks and hazards were mitigated, and demonstrated conformance to relevant safety standards. The MRIdian Linac system described in this premarket notification passed all verification testing..."

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

• The "ground truth" for the performance characteristics are established engineering and physics standards, such as:
  • AAPM TG 119 for Static Dose Accuracy.
  • IEC 60601-1, IEC 60601-1-2, IEC 60601-2-33, IEC/EN 60601-2-1, IEC/EN 60976, IEC 60601-1-6, IEC 61217, IEC 62083, EN 62304, EN 62366, ISO 10993-1.
  • The design specifications of the ViewRay (MRIdian) System (K111862) serve as the benchmark for substantial equivalence.

8. The sample size for the training set:

• Not applicable. This is not an AI/ML device that uses a "training set" in the conventional sense. The "training" for the device's design implicitly comes from decades of medical linear accelerator and MRI technology development, and experience with the predicate device.

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

• Not applicable. As this is not an AI/ML device with a training set, the concept of establishing ground truth for it is not relevant here.

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