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RayStation is a software system designed for treatment planning and analysis of radiation therapy. The treatment plans provide treatment unit set-up parameters and estimates of dose distributions expected during the proposed treatment, and may be used to administer treatments after review and approval by the intended user.

The system functionality can be configured based on user needs.

The intended users of RayStation shall be clinically qualified radiation therapy staff trained in using the system.

RayStation 5 is a radiation therapy treatment planning system, i.e. a software program for planning and analysis of radiation therapy treatment plans. Functionality includes fusion capabilities (CT, PET and MRI), contouring, collapsed cone convolution dose computation and 4D data compatibility, as well as unique features such as multi-criteria optimization, dose tracking, treatment adaptation and deformable registration, all available on one platform.

The device in question is RayStation 5, a radiation therapy treatment planning system. The provided document is a 510(k) premarket notification, which focuses on demonstrating substantial equivalence to a predicate device (RayStation 4.5), rather than providing detailed acceptance criteria and study results for a new, de novo device.

Therefore, the document does not contain the detailed information required for your request, such as specific acceptance criteria for performance metrics, detailed study designs with sample sizes, expert qualifications, or ground truth establishment. Instead, it states that "The test specification of RayStation 5 is a further developed version of the test specification of RayStation 4.5" and that "The successful verification and validation of RayStation 5 therefore support the substantial equivalence of the above RayStation versions."

However, I can extract what is provided and explain what is missing:

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

The document does not provide a table of specific acceptance criteria (e.g., accuracy thresholds for dose calculation, contouring precision) or detailed reported device performance against such criteria. It generally states that verification and validation were successful.

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

This information is not provided in the document.

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)

This information is not provided in the document. The document refers to "clinically qualified radiation therapy staff" as intended users, but not as experts establishing ground truth for testing.

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

This information is not provided in the document.

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

This information is not provided in the document. RayStation 5 is described as a "software system designed for treatment planning and analysis of radiation therapy" and does not explicitly mention "AI assistance" in the context of improving human reader performance. It is a tool used by qualified staff, not necessarily an AI-assisted diagnostic tool in the typical sense of an MRMC study.

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

This information is not provided in the document. The device is a "treatment planning system," implying a human-in-the-loop workflow where the plan is reviewed and approved by an "intended user."

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

This information is not provided in the document. For a treatment planning system, ground truth would typically relate to the accuracy of dose calculations, contouring agreement, and the ability to generate deliverable plans.

8. The sample size for the training set

Given that this is an upgrade to an existing system (RayStation 4.5) and the submission focuses on substantial equivalence for a software system, it's unlikely a "training set" in the context of a machine learning model is directly applicable or discussed in typical 510(k) submissions for such devices. The document implies a traditional software development and testing cycle rather than a machine learning model training. Thus, this information is not provided and likely not relevant in the way you're asking.

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

As with point 8, this information is not provided and likely not applicable in the context of the type of device and submission described.

Summary of what is provided regarding acceptance criteria and study:

The document states:

• "The test specification of RayStation 5 is a further developed version of the test specification of RayStation 4.5."
• "This is supported by the requirements specification, for which the same is true."
• "The successful verification and validation of RayStation 5 therefore support the substantial equivalence of the above RayStation versions."
• "The summary of the performed non-clinical tests shows that RayStation 5 is as safe and effective, and performs as well as the predicate device."

Essentially, the acceptance criteria and study methodology for RayStation 5 are implied to be consistent with, or improved upon, those used for its predicate device, RayStation 4.5, to demonstrate substantial equivalence. However, the explicit details of those criteria and studies (such as specific performance metrics, sample sizes, expert qualifications, or ground truth methods) are not included in this 510(k) summary.

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