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Oncentra Simulation is an accessory to a radiation therapy simulation system which is intended to prepare patients for radiation therapy. The Simulator emulates the geometrical positions of radiation therapy treatment machines. Using conventional radiographic and fluorographic system, patients are positioned, filmed and marked to prepare them for treatment.

Oncentra Simulation 2.3 is a revision of the image handling software of the Simulix Evolution. This software has been adapted such that images can be acquired and processed from an Image Intensifier such as used on the Nucletron Radiotherapy simulators Simulix MC, Simulix HP and Simulix HQ. This makes it a replacement for the predicate device DTI (K954055). The PC based simulator workstation comes with functionality to support simulation procedures: Image acquisition, Image display, Image enhancement and multiple views, Database and DICOM Import / Export functionality, Simulator controls. The modification to the previously cleared device K033470 is: Added support for Image Intensifiers. The software runs on a PC on a Windows XP platform.

This 510(k) submission for Oncentra Simulation 2.3 is a special 510(k) for a software revision and focuses on demonstrating substantial equivalence to a predicate device (Simulix Evolution, K033470). It does not contain the detailed study information typically found in an original 510(k) or PMA submission regarding acceptance criteria, performance studies, or ground truth establishment.

Here's an analysis based on the provided text, highlighting what is not available in this document:

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not explicitly state specific acceptance criteria in terms of diagnostic performance metrics (e.g., sensitivity, specificity, AUC). Instead, it focuses on demonstrating that the revised software, Oncentra Simulation 2.3, performs its intended functions (image acquisition, display, enhancement, database functionality, simulator controls, and support for Image Intensifiers) adequately and is substantially equivalent to the predicate device.

The "performance" described is functional equivalence, not diagnostic accuracy. The key performance aspect mentioned is "Added support for Image Intensifiers." This is a functional addition, and its 'performance' is implicitly met if the system successfully interfaces with and processes images from Image Intensifiers as intended.

2. Sample Size Used for the Test Set and Data Provenance:

Not specified. This document describes a software revision and its functional equivalence to a predicate device. It does not detail a study involving a test set of patient data with specific sample sizes. The evaluation would have been more about verifying the software's functionality and compatibility rather than analyzing a clinical dataset.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts:

Not applicable/Not specified. Since there's no mention of a clinical test set requiring ground truth for diagnostic accuracy, there's no information on experts or their qualifications.

4. Adjudication Method for the Test Set:

Not applicable/Not specified. As there is no clinical test set described, no adjudication method is mentioned.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done:

No. This type of study is not mentioned or implied by the document. The submission focuses on substantial equivalence based on technological considerations and functional performance, not a comparative effectiveness study involving human readers.

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

Not applicable/Not specified. The device is a simulation system, an accessory to radiation therapy. It's an imaging and planning tool used by humans, not an AI algorithm making independent diagnostic decisions. The "algorithm" in this context refers to the software's functional logic, not a standalone diagnostic AI.

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

Not applicable/Not specified. As no clinical performance study involving diagnostic accuracy is described, no ground truth types are mentioned. The ground truth for a software revision like this would likely be engineering specifications and functional testing results.

8. The sample size for the training set:

Not applicable/Not specified. This is a software revision, not a machine learning model that requires a training set in the conventional sense. The "training" for the software would involve development, testing against specifications, and verification/validation activities.

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

Not applicable/Not specified. Again, this is not a machine learning context. The "ground truth" for software development typically refers to design specifications, requirements, and expected functional behavior, which are verified through various software testing methodologies.

In summary:

This 510(k) submission pertains to a software revision for a radiation therapy simulation system. It demonstrates substantial equivalence primarily by showing that the updated software (Oncentra Simulation 2.3) has the same intended use and similar technological characteristics to a legally marketed predicate device, with the key modification being "Added support for Image Intensifiers." The document does not contain information about clinical performance studies, diagnostic accuracy metrics, test/training sets, or expert evaluations in the context of ground truth establishment. Such detailed clinical performance data is typically found in submissions for novel devices or devices with significant changes affecting clinical outcomes, not usually for a functional software update like this one.

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Simulix Evolution is a radiation therapy simulation system is intended to prepare patients for radiation therapy. The Simulator emulates the geometrical positions of radiation therapy treatment machines. Using conventional radiographic and fluorographic system, patients are positioned, filmed and marked to prepare them for treatment.

Simulix Evolution is a Flat Panel detector option to the Nucletron Simulix HP simulator system. The Simulator emulates the geometrical positions of radiation therapy treatment machines. Using a conventional radiographic and fluorographic system, patients are positioned, filmed and marked to prepare them for treatment. The Simulix Evolution option consists of a digital Flat Panel detector and a PC based simulator workstation. The Flat Panel detector option replaces the current Image Intensifiers. The Flat Panel is a Amorphous silicon, digital detector, with a square image area of 41 by 41 cm. The PC based simulator workstation is the current DTI workstation, but ported to a Windows platform. The PC based simulator workstation comes with functionality to support simulation procedures: Image acquisition, Image display, Image enhancement and multiple views, Database and DICOM Import / Export functionality, Simulator controls.

The provided text describes a 510(k) premarket notification for a medical device called "Simulix Evolution." However, it does not include information about acceptance criteria, device performance studies, sample sizes, ground truth establishment, or expert qualifications in the comprehensive manner requested. The document focuses on establishing substantial equivalence to a predicate device and outlines the device's intended use and technological considerations.

Therefore, many of the requested details cannot be extracted from the given text.

In lieu of the specific details, here's what can be inferred or stated that is missing:

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

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

• Sample Size for Test Set: Not specified.
• Data Provenance (e.g., country of origin of the data, retrospective or prospective): Not specified.
  • Implied: Given it's a submission for a new device, any testing would likely be prospective in a controlled environment, but this is not explicitly stated.

3. 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.
  • Implied: For a radiation therapy simulation system, experts would typically be radiation oncologists, medical physicists, and/or radiation therapists.

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

• Adjudication Method: 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

• MRMC Comparative Effectiveness Study: Not mentioned.
  • Note: The device described is a simulation system, not an AI diagnostic tool, so an MRMC study comparing human readers with and without AI assistance is not directly relevant to the information provided. The "Simulix Evolution" is described as replacing an Image Intensifier with a Flat Panel detector and porting a workstation to Windows, focusing on hardware and software updates for simulation, not AI-driven interpretation.

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

• Standalone Performance Study: Not explicitly mentioned in terms of quantitative data or metrics. The submission focuses on demonstrating substantial equivalence of the system (hardware and software) to its predicate.

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

• Type of Ground Truth: Not specified.
  • Implied: For a simulation system, ground truth would likely involve established geometrical accuracy measurements, phantom studies, and possibly comparisons to existing simulation data from the predicate device.

8. The sample size for the training set

• Sample Size for Training Set: Not specified.
  • Note: The device is not described as having a machine learning component that would require a 'training set' in the traditional sense of AI algorithm development. The software capabilities mentioned (image acquisition, display, enhancement, database, DICOM) are standard functionalities, not indicative of a learning algorithm.

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

• How Ground Truth for Training Set was Established: Not applicable, as a training set, as typically understood for machine learning, is not indicated by the provided description. If 'training set' refers to data used for software validation or testing, the method of establishing ground truth for such data is not specified.

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