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RayStation is a software system designed for treatment planning and analysis of radiation therapy. The treatment plans provide treatment unit set-up parameters and estimates of dose distributions expected during the proposed treatment, and may be used to administer treatments after review and approval by the intended user.

The system functionality can be configured based on user needs.

The intended users of RayStation shall be clinically qualified radiation therapy staff trained in using the system.

RayStation 4.0.2 is a treatment planning system, i.e. a software program for planning and analysis of radiation therapy plans. Typically, a treatment plan is created by importing patient images obtained from a CT scanner, defining regions of interest either manually or semi-automatically, deciding on a treatment setup and objectives, optimizing the treatment parameters, comparing rival plans to find the best compromise, computing the clinical dose distribution, approving the plan and exporting it.

The provided text is a 510(k) Summary for RayStation 4.0.2, a radiation treatment planning system. However, it does not contain specific acceptance criteria or a detailed study description with performance metrics, sample sizes, ground truth establishment, or expert qualifications necessary to fully answer your request.

The document primarily focuses on demonstrating substantial equivalence to predicate devices (RayStation 3.5 and XiO RTP System) through a general description of functionalities, technological characteristics, and a summary of non-clinical performance data.

Here's a breakdown of what can be extracted and what information is missing based only on the provided text:

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

• Missing Information: The document does not explicitly state quantitative "acceptance criteria" or provide "reported device performance" metrics in the way you've requested (e.g., sensitivity, specificity, accuracy, dice scores, etc.).
• What is reported: The document states that "The summary of the performed non-clinical tests shows that RayStation 4.0.2 is as safe and effective, and performs as well as the predicate devices." This is a qualitative statement of equivalence rather than a quantitative performance metric against specific criteria.

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

• Missing Information: The document does not specify any sample sizes for test sets (e.g., number of patient cases, images).
• Data Provenance: Not mentioned (e.g., country of origin, retrospective or prospective).

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

• Missing Information: This information is not provided. The document refers to "verification and validation" and "test results" but does not describe the methodology of establishing ground truth or the involvement of experts in this process for a test set.

4. Adjudication method for the test set

• Missing Information: No adjudication method (e.g., 2+1, 3+1, none) is mentioned.

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

• Missing Information: The document makes no mention of an MRMC study or any AI components, nor does it discuss human reader improvement with or without assistance. The device described is a treatment planning system, not an AI-assisted diagnostic tool in the sense implied by this question.

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

• Missing Information: No information is provided about standalone performance studies for an "algorithm only" where the context suggests AI. The device is a "treatment planning system," implying human interaction is integral.

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

• Missing Information: The document does not specify the type of ground truth used for any testing. It mentions "verification performed for proton planning verifies the functionality for - Proton planning treatment plan calculation, - Proton energy range estimation, - Proton dose calculation, - Pencil Beam Scanning (PBS)." This suggests a ground truth related to physical accuracy of calculations, but how this ground truth was established (e.g., comparison to gold standard physics measurements, theoretical models) is not detailed.

8. The sample size for the training set

• Missing Information: As the document describes a traditional software system rather than a machine learning/AI model, the concept of a "training set" in the context of deep learning is not applicable here, and therefore, no sample size for training is provided.

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

• Missing Information: Not applicable as per point 8.

Summary of Study Information Provided:

The document describes a non-clinical performance data assessment to support substantial equivalence. The "study" appears to be a verification and validation (V&V) process against internal requirements and the functionalities of predicate devices.

• "General Technology" V&V: The test specification for RayStation 4.0.2 is a "further developed version of the test specification of RayStation 3.5," supported by similar requirements specifications. The "successful verification and validation" of 4.0.2 supports its substantial equivalence to previous RayStation versions.
• "Proton Planning" V&V: Verification was performed for specific proton planning functionalities: treatment plan calculation, energy range estimation, dose calculation, and Pencil Beam Scanning (PBS). These are stated to be the "same functionality as included in the predicate device XiO RTP System."

Conclusion based on provided text:

The 510(k) submission for RayStation 4.0.2 relies on demonstrating substantial equivalence to its predicate devices through internal verification and validation of its expanded functionalities (especially PBS for proton planning). It asserts that the tests performed show the device is "as safe and effective, and performs as well as the predicate devices." However, it does not provide the detailed study characteristics, quantitative acceptance criteria, or performance metrics typically associated with studies for AI/ML-based medical devices. The information supplied is typical for a traditional software update where V&V against existing requirements and functionalities is the primary means of demonstrating safety and effectiveness.

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The Digital X-ray Radiography System is indicated for use in generating radiographic image of human anatomy. It is intended to replace radiographic film/screen systems in all general purpose diagnostic procedures. This device is not intended for mammographic applications.

The Digital X-ray Radiography System is indicated for use in generating radiographic image of human anatomy. It is intended to replace radiographic film/screen systems in all general purpose diagnostic procedures. This device is not intended for mammographic applications. The Digital X-ray Radiography System is designed to perform rudiographic X-ray cvario 1. consists of a control cabinet, a high voltage (HV) generator, an X-ray source assembly including an X-ray tube and an collimator, an Overhead Tube Suspension, a amorphous silicon (a-Si) detector, an elevating radiographic Table, a radiographic wall stand unit and a workstation.

The provided text describes a 510(k) submission for a Digital X-ray Radiography System (model Eagle-DR2000A). The submission aims to demonstrate substantial equivalence to a predicate device, the Revolution XR/d Digital Radiographic Imaging System (K012389).

However, the provided document does not contain any information about the acceptance criteria or the study that proves the device meets those criteria, specifically concerning device performance in terms of image quality metrics, clinical outcomes, or comparative effectiveness studies (e.g., MRMC studies).

The document primarily focuses on:

• General device identification and classification.
• Intended use statement.
• Comparison to a predicate device.
• A high-level description of the device components.
• Non-clinical bench tests conducted to verify compliance with various safety and performance standards (e.g., IEC standards for electrical safety, radiation protection, and biological evaluation).
• The FDA's letter of clearance, stating substantial equivalence.

Therefore, I cannot populate the requested table and study information based on the provided text. The document states that "Bench tests were conducted to verify that the proposed device met all design specifications as was Substantially Equivalent (SE) to the predicate device. The test results demonstrated that the proposed device complies with the following standards." This indicates that the acceptance criteria for this 510(k) submission were primarily related to compliance with established safety and performance standards for X-ray equipment and demonstrating equivalence to a legally marketed predicate device, rather than detailed clinical performance metrics in a human study.

Based on the provided text, I can only provide the following, with many fields explicitly stated as "Not mentioned in the provided text":

Analysis of Acceptance Criteria and Study to Prove Device Performance

The provided 510(k) summary focuses on demonstrating substantial equivalence to a predicate device through compliance with recognized standards and general safety/performance. It does not detail specific performance criteria related to diagnostic accuracy, image quality metrics, or human reader performance, nor does it describe any associated clinical studies or reader studies. The "Non-Clinical Test Conclusion" section primarily lists compliance with various IEC and CFR standards related to electrical safety, radiation protection, and biological compatibility.

Given the nature of a 510(k) for a general radiography system as a replacement for film/screen systems, the primary "acceptance criteria" appear to be robust engineering and safety compliance, and equivalence to a predicate device, rather than a specific clinical performance threshold beyond what the predicate already demonstrated.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size (Test Set): Not mentioned in the provided text. The document refers to "bench tests" for compliance with standards, not a test set of patient data.
• Data Provenance (Country of Origin, Retrospective/Prospective): Not mentioned in the provided text.

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

• Experts: Not mentioned in the provided text.
• Qualifications: Not mentioned in the provided text.

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

• Adjudication Method: Not mentioned in the provided text. This type of method would typically be used in reader studies or clinical trials, which are not 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

• MRMC Study: Not mentioned in the provided text. The device is a digital X-ray radiography system, not specifically an AI-based diagnostic tool for assisting human readers. Its primary purpose, as stated, is to "replace radiographic film/screen systems," implying a direct imaging modality replacement.
• Effect Size of Improvement: Not applicable/not mentioned, as no such study is described.

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

• Standalone Performance: Not applicable/not mentioned. This device is an imaging system, not an algorithm being evaluated for standalone diagnostic performance. Its primary function is to generate images for human interpretation.

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

• Type of Ground Truth: Not mentioned in the provided text. This information would be relevant if clinical performance or diagnostic accuracy studies were conducted, which are not detailed here. The "ground truth" for this 510(k) appears to be compliance with engineering and safety standards, as well as functional equivalence to a predicate device.

8. The sample size for the training set

• Sample Size (Training Set): Not applicable/not mentioned. The provided document does not describe any machine learning or AI components that would require a "training set."

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

• Ground Truth Establishment (Training Set): Not applicable/not mentioned, as no training set is described.

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