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PatXfer RT provides capabilities for the acceptance, transfer, display, storage, and digital processing of medical images and objects according to the DICOM RT standard and according to the BrainLAB proprietary data format.

This includes functions for performing operations related to image manipulation, enhancement, compression, and quantification. The integrity of the original data is maintained.

PatXfer RT interfaces between medical devices and provides the patient data for treatment planning or verification.

The application transfers, displays, stores, and processes data received according to the ACR-NEMA standard (DICOM 3.0) or according to the BrainLAB proprietary data format. DICOM RT data are converted into the BrainLAB proprietary data format and vice-versa for further data processing.

PatXfer RT supports Dicom RT data and BrainLAB's proprietary data formats from digital storage media, such as network archive, optical disk or CD-ROM.

To keep the application as user-friendly as possible only the necessary information for the intended procedures are displayed. The patient, image series, and images will be displayed and can be selected and processed.

PatXfer RT creates a history log-file including all the software actions to import the data and can be controlled with the mouse or by touchscreen monitor.

The provided text does not contain detailed acceptance criteria or a study proving that the device meets specific performance metrics. Instead, it is a 510(k) summary for a medical device (PatXfer RT) that focuses on demonstrating substantial equivalence to a predicate device.

Here's a breakdown of what is and isn't available in the provided document, based on your requested information:

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

• Not available. The document states: "A risk analysis was conducted for PatXfer RT and all hazards were mitigated to as low as reasonable possible (ALARP) and found to be acceptable. The Verification showed that PatXfer RT is safe and effective for its intended use." This is a general statement about verification but does not provide specific performance metrics or acceptance criteria in a table format.

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

• Not available. There is no mention of a specific test set, its sample size, or data provenance. The document refers to "Verification and Validation" being performed according to BrainLAB's procedures.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g., radiologist with 10 years of experience)

• Not available. Ground truth establishment is not discussed, as there's no specific test set described with human expert evaluation.

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

• Not available. No information on adjudication is provided.

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

• Not available. This device (PatXfer RT) is a medical image processing and transfer system, not an AI-assisted diagnostic tool. Therefore, an MRMC study comparing human readers with and without AI assistance would not be applicable or relevant to its intended use as described.

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

• Not available. The device performs functions like transfer, display, storage, and processing, which are inherently "standalone" in their execution. However, the performance is in terms of data handling and integrity, not diagnostic accuracy that would typically be evaluated in a standalone study for an AI algorithm.

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

• Not available. Since specific performance metrics for diagnostic or analytical tasks are not mentioned, the concept of "ground truth" as it applies to such tasks is not discussed. The "ground truth" for this device would likely refer to the accuracy of data transfer, transformation, and integrity, which is assessed through technical verification methods rather than clinical ground truth types.

8. The sample size for the training set

• Not available. As this is not an AI/ML algorithm requiring a training set, this information is not applicable.

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

• Not available. Not applicable, as there's no mention of a training set.

Summary of Information Present in the Text:

The provided document describes a 510(k) submission for PatXfer RT, focusing on its intended use as a system for the acceptance, transfer, display, storage, and digital processing of medical images and objects according to DICOM RT and BrainLAB's proprietary format. The core of the submission revolves around demonstrating substantial equivalence to a predicate device (PatXfer 5.0, K021583).

The "study that proves the device meets the acceptance criteria" is broadly referred to as:

• "A risk analysis was conducted for PatXfer RT and all hazards were mitigated to as low as reasonable possible (ALARP) and found to be acceptable."
• "The Verification showed that PatXfer RT is safe and effective for its intended use."
• "PatXfer RT has been verified and validated according to BrainLAB's procedures for product design and development and found to be substantially equivalent with BrainLAB medical devices such as PatXfer 5.0 (K021583). The validation proves the safety and effectiveness of the system."

This indicates a process-based validation rather than a detailed performance study with specific acceptance criteria and outcome metrics typical for diagnostic or AI devices. The acceptance criteria would likely relate to functional requirements, DICOM compliance, data integrity, and system reliability established during the design and development verification/validation process, rather than clinical efficacy metrics.

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