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The VeriSuite patient position verification system is used for verification and correction of the patient's position during a radiotherapy treatment with external beams or charged particles. It is based on stereoscopic X-ray images and DRRs calculated from a CT image series of the treatment region of the patient and information from the treatment planning.

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Here's a breakdown of the acceptance criteria and study information for the VeriSuite device, based on the provided text.

Note: The provided document is a 510(k) premarket notification summary, which primarily focuses on demonstrating substantial equivalence to a predicate device. It does not contain the detailed study protocols, results, or comprehensive acceptance criteria that would typically be found in a full clinical or performance study report. Therefore, much of the requested information (like specific sample sizes for test sets, expert qualifications, adjudication methods, MRMC studies, or detailed ground truth establishment for training data) is not present in this document. The information provided below is extracted directly from the available text.

Acceptance Criteria and Device Performance (Limited Information)

The document states: "We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices..." This indicates that the primary acceptance criterion for the 510(k) submission was demonstrating substantial equivalence to existing predicate devices (specifically, EXACTRAC 5.5 / ExacTrac X-RAY 6D K072506).

Table of Acceptance Criteria and Reported Device Performance

Study Information (Based on Available Text)

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

   • Sample Size: Not specified in the provided document.
   • Data Provenance: Not specified in the provided document. The document primarily focuses on the regulatory review rather than detailed study methodology. It is likely that testing data would have been generated internally by the manufacturer during development and validation, but specific details are absent here.

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

   • Number of Experts: Not specified.
   • Qualifications of Experts: Not specified.

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

   • Not specified.

4. 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 evidence of an MRMC study related to human readers or AI assistance in this document. The VeriSuite system is described as a "patient position verification system" based on X-ray images, implying an automated or semi-automated system for patient positioning rather than an AI-assisted diagnostic tool that would typically involve human readers and diagnostic accuracy studies.

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

   • The description "patient position verification system... It is based on stereoscopic X-ray images and DRRs calculated from a CT image series..." suggests that the system operates in a standalone capacity to perform its verification function. However, the document does not explicitly present a "standalone performance study" with specific metrics or results. The FDA's determination of substantial equivalence implies they reviewed performance data, but the details are not included in this summary.

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

   • The document implies that the "ground truth" for patient position verification would be the actual patient position or a carefully measured, validated reference position. This would likely be established through precise physical phantoms or highly accurate imaging modalities. However, the exact method for establishing ground truth for any testing is not specified in this regulatory filing summary.

7. The sample size for the training set:

   • Not specified. The document does not describe a training set as would be relevant for machine learning models. If the system uses algorithms that were "trained," the details are not in this summary.

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

   • Not applicable/Not specified, as no training set is described in the provided text.

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