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Oncentra-VISIR is designed to be a tool for supporting the process of scheduling, preparing, setting up, delivering and recording radiation therapy.

Oncentra-VISIR is a verification system designed to be a tool for supporting the process of preparing, setting up, delivering and recording radiation therapy. It will retrieve data from doseplan systems and simulators automatically or by manual input from the user. It will help administer patient sessions through the booking functionality. At each individual treatment Oncentra-VISIR will present setup data to the operator and also to the linac through a specific interface. Actual setup will be verified. During treatment Oncentra-VISIR can monitor the progress of both Monitor Unit values and optional in vivo dosimetry. All data relevant for the given treatment can be recorded.

The main areas of modification to the previously cleared device K972617 are:

• Image Based Verification (IBV) .
• This module allows the user to schedule image acquisitions on the treatment machine. Once the images are acquired thev are compared to a reference image such as a DRR or digital simulator image. Automated edge detection will mark both the reference and control image and perform a comparison. A resultant displacement is translated into the treatment couch shifts required to reposition the patient correctly for the desired beam placement within the patient.
• Improved user interface improved layout of data making it easier for the user to navigate . and display the data they require. Also improved workflow requiring less clicking and password entry than before.
• Optional double signature for treatment lets 2 therapists sign for treatment delivery .
• Dynamic MLC IMRT support .
• Live MLC display in treatment screens .
• More flexible port filming options such as film only and orthogonal film modes .
• Image acquisition (port film) scheduling .
• Detailed audit trail tracks all changes to patient data with time/date/signature stamps of anv . edit

The software runs on a PC on a Windows NT, 2000, or XP platform.

This submission K041719 for Oncentra-VISIR is a Special 510(k), meaning it's a modification to a previously cleared device (K972617). Special 510(k)s often do not include extensive new performance studies if the modifications are considered minor and do not introduce new risks or significantly alter the fundamental operating principles. The provided text primarily focuses on describing the modifications and affirming substantial equivalence to the predicate device.

Therefore, the document does not explicitly describe acceptance criteria or a dedicated study proving the device meets new performance criteria in the way a traditional 510(k) for a novel device would. The core of this submission is demonstrating that the modifications to the Oncentra-VISIR (previously Helax-VISIR) do not negatively impact its existing safety and effectiveness and that the new features function as intended without introducing new risks.

Based on the provided text, here's an analysis of what can be inferred or is explicitly stated, with a strong caveat that the detailed information requested regarding specific performance testing and statistical analysis is largely absent.

Summary of Device Performance (Based on Affirmation of Substantial Equivalence and Feature Descriptions):

The document focuses on the functionality of the modifications, implying their intended performance is met through internal verification and validation rather than a comparative clinical study against specific acceptance criteria. The key modifications are:

• Image Based Verification (IBV):

  • Functionality: Allows scheduling of image acquisitions on the treatment machine.
  • Performance (Implied): Images acquired are compared to a reference (DRR or digital simulator image). Automated edge detection marks reference and control images. Performs a comparison. A resultant displacement is translated into treatment couch shifts.
  • Acceptance (Implied): The system accurately identifies edges, calculates displacement, and translates it into appropriate couch shifts for correct patient repositioning for desired beam placement. Precision and accuracy of this process would have been verified internally during development, but no specific metrics are provided.

• Improved User Interface/Workflow:

  • Functionality: Easier navigation, reduced clicking, fewer password entries.
  • Performance (Implied): Enhanced usability.
  • Acceptance (Implied): User testing or internal evaluation would have confirmed the improved user experience and workflow efficiency.

• Optional Double Signature for Treatment:

  • Functionality: Allows two therapists to sign for treatment delivery.
  • Performance (Implied): Secure and auditable verification.
  • Acceptance (Implied): Confirmed functionality as a security/verification step.

• Dynamic MLC IMRT support:

  • Functionality: Supports Inverse Modulated Radiation Therapy with dynamic Multi-Leaf Collimators.
  • Performance (Implied): Compatibility and proper functioning with IMRT.
  • Acceptance (Implied): System correctly integrates and controls MLC movements for IMRT.

• Live MLC display in treatment screens:

  • Functionality: Real-time display of MLC positions during treatment.
  • Performance (Implied): Accurate and timely display.
  • Acceptance (Implied): Visual confirmation of MLC operation.

• More flexible port filming options:

  • Functionality: Options like film only and orthogonal film modes.
  • Performance (Implied): Enhanced imaging flexibility.
  • Acceptance (Implied): Confirmed ability to operate in new modes.

• Image acquisition (port film) scheduling:

  • Functionality: Ability to schedule port film acquisitions.
  • Performance (Implied): Reliable scheduling and execution.
  • Acceptance (Implied): Confirmed scheduling capabilities.

• Detailed audit trail:

  • Functionality: Tracks all changes to patient data with time/date/signature stamps.
  • Performance (Implied): Comprehensive and accurate logging.
  • Acceptance (Implied): Confirmed logging of all relevant details.

Detailed Response to Requested Information:

Since this is a Special 510(k) for modifications, the typical structure for a new device's performance study is not present in the provided text.

1. Table of Acceptance Criteria and Reported Device Performance:

   Acceptance Criteria (Implied for IBV)	Reported Device Performance (Implied from Description)
   Accurate Image Comparison & Displacement Calculation:	"Automated edge detection will mark both the reference and control image and perform a comparison. A resultant displacement is translated into the treatment couch shifts..."
   - Precision of edge detection	(Not specified)
   - Accuracy of displacement calculation	(Not specified)
   - Correct translation to couch shifts	(Not specified)
   Effective Patient Repositioning:	"...reposition the patient correctly for the desired beam placement within the patient."
   - Reduction in setup errors	(Not specified)
   Improved User Interface/Workflow:	Improved layout, easier navigation, less clicking and password entry.
   - Enhanced usability	(Not specified, but implied by 'improved user interface')
   - Workflow efficiency	(Not specified, but implied by 'improved workflow requiring less clicking')
   Functional Dynamic MLC IMRT Support:	Supports Dynamic MLC IMRT.
   - Compatibility with IMRT	(Not specified, but implied by 'supports')
   Accurate Live MLC Display:	Live MLC display in treatment screens.
   - Real-time accuracy of display	(Not specified)
   Robust Audit Trail:	Detailed audit trail tracks all changes to patient data with time/date/signature stamps.
   - Comprehensive and tamper-proof logging of data changes/signatures	(Not specified, but implied by 'detailed audit trail tracks all changes... with time/date/signature stamps')

   Note: The provided text describes functionalities of the modifications, implying their intended performance is met, but does not provide quantitative acceptance criteria or detailed results from specific performance tests for these new features. It relies on the substantial equivalence argument for the overall device's safety and effectiveness.

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

   • Not explicitly stated. As this is a Special 510(k) for software and feature modifications to an existing device, it's highly likely that testing was performed internally by the manufacturer (Nucletron B.V.) using a combination of simulated data, phantom studies, and potentially internal clinical review, but no specific sample sizes or data provenance (country of origin, retrospective/prospective) for formal test sets against acceptance criteria are provided in the public summary.

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

   • Not applicable / Not explicitly stated. The document does not describe the establishment of ground truth by external experts for a test set in the context of clinical performance evaluation for the modified features. Ground truth for the underlying radiation therapy planning and delivery would be based on established medical physics principles and clinical standards. The "Image Based Verification" feature would rely on algorithms to establish "ground truth" through image registration and edge detection, not human experts for that specific functional test.

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

   • Not applicable / Not explicitly stated. There is no mention of an adjudication process for a test set, as no formal clinical performance study with human readers assessing diagnostic outcomes is described.

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. An MRMC comparative effectiveness study was not performed or referenced in this 510(k) summary. This device is a verification system for radiation therapy, not a diagnostic AI system intended to improve human reader performance in interpreting medical images.

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

   • Implied for specific features, but not explicitly detailed. The "Automated edge detection" within the Image Based Verification (IBV) module would operate in a standalone manner to identify edges and calculate displacement. However, the overall Oncentra-VISIR system is a "human-in-the-loop" device as it provides tools for operators to prepare, set up, deliver, and record radiation therapy, and the operator ultimately makes decisions based on the system's output. The document doesn't provide specific standalone performance metrics for the automated functions.

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

   • Not explicitly stated for a clinical test set. For the Image Based Verification, the "ground truth" for image comparison is a "reference image such as a DRR or digital simulator image." Accuracy would be measured against the known geometry of phantoms or the established reference image, rather than a clinical ground truth like pathology for diagnostic purposes.

8. The sample size for the training set:

   • Not applicable / Not explicitly stated. The modifications described are primarily software feature enhancements and integration rather than a machine learning/AI model that would require a distinct training set in the typical sense for image interpretation. The "automated edge detection" is likely based on established image processing algorithms rather than a deep learning model requiring a large training dataset as understood today for AI.

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

   • Not applicable / Not explicitly stated. (See point 8).

Conclusion:

The K041719 submission for Oncentra-VISIR is a Special 510(k) focusing on software and functional modifications to an already cleared device. As such, it primarily asserts substantial equivalence and describes the added functionalities. It does not contain the detailed, quantitative performance studies against specific acceptance criteria, sample sizes, expert ground truth establishment, or multi-reader studies that would be expected for a novel device or a device introducing entirely new diagnostic capabilities leveraging artificial intelligence. The performance and safety of the modified device are substantiated by verifying the internal functionality of the new features and their integration within the existing, cleared system, rather than through a public summary of a clinical trial.

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