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The AlignRT Plus system is indicated for use to position and monitor patients relative to the prescribed treatment isocentre, and to withhold the beam automatically during radiation delivery.

For cranial treatments, a manual head adjuster is included which can be used in concert with AlignRT Plus to provide fine corrections for pitch, roll and yaw rotations.

AlignRT Plus is also used to track the patient's respiratory pattern for respiratory synchronized image acquisition, and radiation therapy treatment.

Patient contour data can be extracted and exported from the data acquired for the purpose of treatment planning.

AlignRT Plus can be calibrated directly to the treatment beam isocentre and in turn assists in performing quality assurance on MV, kV imagers, room lasers and the treatment couch.

AlignRT Plus is indicated for use during simulation, setup and stereotactic radiosurgery and precision radiotherapy for lesions, tumors and conditions anywhere in the body where radiation is indicated.

The AlignRT Plus system (K193431) is a combination of the devices AlignRT, GateCT and GateRT. A special 510(K) K193431 was submitted by Vision RT Ltd. in December 2019 and this was cleared by FDA in January 2020 for the changes and improvements made in AlignRT.

AlignRT Plus is a video-based three-dimensional (3D) surface imaging system, which is used to image the skin surface of a patient in 3D before and during radiotherapy treatment. The system consists of advanced software, a computer workstation, and one, two or three 3D camera units (each camera unit comprising a stereo pair of sensors to allow 3D surface reconstruction). The system is non-invasive, does not require the use of body markers and produces no ionizing irradiation during the imaging process.

AlignRT Plus is also able to perform both respiratory synchronised CT imaging and treatment delivery. In both instances, the system acquires a gated 3D surface model of the patient. User selected points are then tracked in real time in order to provide gating and position monitoring signals.

Real-time imaging and surface matching of the patient is possible during both setup and the treatment delivery to determine any patient movement. During treatment delivery, AlignRT Plus is also able to withhold the beam automatically, should the patient move outside user-defined tolerances.

Patient contour data may be extracted from surface data acquired by the system and exported for the purpose of treatment planning by radiotherapy professionals.

AlignRT Plus may be calibrated directly to the treatment beam isocentre using an optional custom designed calibration phantom and image processing software. It can analyse MV and kV digital imaging data acquired by other cleared devices. This in turn assists the user in performing quality assurance on MV, kV imagers, room lasers and the treatment couch.

The AlignRT Plus system includes the optional Head Adjuster for cranial treatments to allow for the manual, fine correction of pitch, roll and yaw in the patient's head position.

Precise isocenter calibration and the optional Head Adjuster provide improved frameless Stereotactic Radiosurgery (SRS). This is provided with the brand name "AlignRT SRS module".

The AlignRT Plus system is also provided under the brand OSMS (Optical Surface Monitoring System). This product is identical to AlignRT.

This 510(k) is requested for a new optional InBore camera and a change in memory size for an existing FDA 510(k) cleared product "AlignRT Plus" (K193431).

The InBore camera is optional hardware for use with bore-based linear accelerators.

The increase in memory size from 32 to 64bit increases the system's processing speed, enabling a frame rate improvement.

The provided text is a 510(k) Premarket Notification for the AlignRT Plus device K203387, which describes modifications to an existing cleared device K193431. The document does not contain a detailed study that proves the device meets specific acceptance criteria with supporting data, an approach often seen for substantial equivalence claims where modifications are minor and performance remains unchanged from a previously cleared predicate. Instead, it relies on demonstrating that the modified device's performance and characteristics are the same as its predicate device.

Therefore, the following information is based on what is stated and what can be inferred from the provided text, primarily focusing on the comparison to the predicate device.

Acceptance Criteria and Reported Device Performance

The "Acceptance Criteria" for this submission are fundamentally defined by the performance of the predicate device (K193431). The "Reported Device Performance" for the modified device (K203387) is stated to be unchanged from that of the predicate.

Study Details (Based on provided text)

Since the document asserts that the system performance and accuracy have not changed since previous clearances, a new comprehensive performance study against acceptance criteria is not explicitly provided. The submission focuses on demonstrating substantial equivalence through a comparison of the modified device to its predicate, stating that the "changes made in subject device were test method and acceptance criteria as the predicate device and the subject device is substantially equivalent to the predication and/or validation demonstrate that the device is as safe, as effective, and performs as well as or better than the predicate device."

1. Sample size used for the test set and the data provenance: Not explicitly detailed in this document. The document states that "The system performance and accuracy have not changed since previous clearances, including with the additional optional hardware (InBore camera)." This implies that the performance characteristics were established in previous submissions (e.g., K193431) and are considered to apply to the current modified device. No new test set or data provenance is detailed for K203387 for these performance metrics.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not explicitly detailed. The ground truth for the performance metrics (positioning accuracy, respiratory tracking RMS errors) would likely have been established using physical phantoms and measurement references rather than human expert consensus for the quantitative metrics stated.

3. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable, as no clinical test set requiring human adjudication for diagnostic outcomes is described for this submission. The performance metrics are technical measurements against a known reference.

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: Not applicable. This device is a surface-guided radiation therapy system that primarily assists in patient positioning and monitoring, not in diagnostic interpretation by human readers. It does not involve AI in the context of improving human reader diagnostic performance.

5. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: The "System Performance and Accuracy" section indirectly refers to standalone performance:

   • "Positioning accuracy: Target registration errors (as measured using calibration phantom) < 1mm (0.5mm) for all couch angles." This is a measurement of the system's inherent accuracy using a physical phantom, which is a standalone assessment.
   • "Respiratory tracking: Tracks respiratory signal from imaged surface data... Surface displacements can be tracked with RMS errors < 0.5mm over 10 or more breathing cycles." This also describes the device's intrinsic capability to track surface displacements, tested against a measurable reference.
     The document states these performances "have not changed since previous clearances."

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): For the performance characteristics mentioned, the ground truth is based on physical reference measurements using a calibration phantom. For instance, "Target registration errors (as measured using calibration phantom)" is directly specified.

7. The sample size for the training set: Not applicable based on the information provided. This device is a surface imaging and tracking system where performance is validated through precision measurements, not a machine learning model that requires a "training set" in the conventional sense for image classification or prediction tasks. The software changes mentioned are minor and related to functionality improvements (e.g., frame rate due to memory increase, new camera for bore-based linacs, user interface features), not a re-training of a core algorithmic performance.

8. How the ground truth for the training set was established: Not applicable, as there is no mention of a training set for machine learning.

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