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The AlignRT Plus system is indicated for:

• · Tracking respiratory motion throughout the simulation process to facilitate subsequent 4DCT reconstruction and coaching the patient in breathing techniques required for Deep-Inspiration Breath Hold (DIBH).
• · Verification of patient identity for their radiation treatment session.
• · Positioning and monitoring of patients during radiation delivery, relative to the setup isocenter and/or the prescribed treatment isocenter.
• · Withholding the beam automatically during radiation delivery, as well as gating the patient's respiratory motion.
• · Performing quality assurance on MV, kV imagers, room lasers, and the treatment couch.
• · Visualizing the Cherenkov signal associated with the radiation beam on entry and exit from the patient.
• · Passing and receiving information to/from other systems associated with the radiotherapy treatment.

The AlignRT Plus system (K212583) is a combination of the devices AlignRT InBore, AlignRT Offline, DoseRT, GateCT, GateRT and SimRT.

AlignRT Plus is a video-based three-dimensional (3D) surface imaging system used to monitor the patient's position in 3D before and during radiotherapy treatment. During each treatment session the patient's position is compared to the reference surface and offsets are displayed to the user. The system can be used to track pation during tumour localization in the CT scanner in order to facilitate subsequent 4D CT reconstruction. The system can be used to track the breath hold level consistency throughout the simulation process. The system can also be used to track patient motion during treatment delivery for radiation therapy procedures and hold the beam when the patient is not in position. Use of optional accessories allows the user to:

• monitor the patient's position inside bore-based linacs; and/or —
• to view the Cherenkov light emitted by the radiation beam as it enters and exits the patient's skin; and/or
• verify the patient's identity.

The system is non-invasive, does not require the use of body markers and produces no irradiation during the imaging process. The system mainly consists of advanced software. 3D cameras (≤6 cameras) and calibration tools. Each camera pod monitoring the pation will, however, project a pattern on the patient to acquire a 3D image of the patient.

This 510(k) notification is to obtain clearance for the following changes to the cleared device:

• 1. Introduction of new software, version 7.3. This is an updated version of the existing cleared software, to support the:
  • a. New Cherenkov imaging feature, called DoseRT.
  • b. Updated respiratory gating feature, called Respiratory Module.
• 2. Introduction of new hardware:
  • a. DoseRT camera to enable Cherenkov imaging.
• 3. Update to indications for use to include the new Cherenkov imaging feature and clarify existing indications.

The provided document describes the AlignRT Plus 7.3 system, a surface-guided radiation therapy system. However, it does not contain detailed acceptance criteria and a study dedicated to proving the device meets those criteria in the format requested.

The document focuses on demonstrating substantial equivalence to a predicate device (AlignRT Plus K212583) and a reference device (BeamSite K212606) for new features, primarily Cherenkov imaging and an updated respiratory module. It mentions "Verification tests were performed to ensure that the module works as intended and pass/fail criteria were used to verify requirements. Validation testing was performed using summative evaluation techniques per IEC 62366-1:2015. Verification and validation testing passed in all test cases." but does not provide details of these tests, specific acceptance criteria, or the results.

Therefore, many of the requested fields cannot be filled from the provided text.

Here's a summary of the information that can be extracted or inferred, and what is missing:

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Sample Size: Not specified for any of the performance claims. The document states "no clinical investigations were performed."
• Data Provenance: Not specified. It's likely internal testing data given the lack of clinical studies. Retrospective or prospective nature is not mentioned, nor is country of origin.

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

• Not specified. The document does not describe the use of experts to establish ground truth for testing.

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

• Not specified.

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. The device is a patient positioning and monitoring system, not an AI-assisted diagnostic tool for human readers.

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

• The document implies that the device's technical performance characteristics (e.g., positioning accuracy, respiratory tracking) were evaluated as a standalone system against predefined specifications, as it mentions "Verification tests were performed to ensure that the module works as intended and pass/fail criteria were used to verify requirements." However, no specific details like algorithms, metrics, or detailed results are provided.

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

• For the positioning accuracy, the ground truth appears to be based on measurements using a "calibration phantom."
• For respiratory tracking, the ground truth for "relative respiratory position" is implied to be a measured value for comparison.
• For Cherenkov visualization, the ground truth is the actual presence and characteristics of the radiation beam.
• No expert consensus, pathology, or outcomes data is explicitly mentioned as ground truth for these technical performance tests.

8. The sample size for the training set:

• Not applicable/Not specified. The document describes a medical device system, not an AI model requiring a distinct training set in the typical sense of machine learning. The "advanced software" may incorporate algorithms, but details about training data, if any, are not present.

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

• Not applicable/Not specified (refer to point 8).

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