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The VariSource 200 System is a High Dose Rate Brachytherapy Afterloader used to treat lesions, tumors and conditions in the body where radiation is indicated.

The VariSource 200 Series High Dose Rate Afterloader system is a computer controlled remote electro/mechanical system used for medical purposes, for placing a NiTiNol wire incorporating an irradiated iridium seed internally or close by, a malignant tumor or tumor bed in a practice known as brachytherapy.

The provided document is a 510(k) summary for the VariSource 200 HDR Brachytherapy Afterloader, which is a medical device used for radiation therapy, specifically brachytherapy. It is not an AI/ML device, and thus the acceptance criteria and study information typically associated with AI/ML device performance (like sensitivity, specificity, MRMC studies, ground truth establishment by experts, training/test set sizes, etc.) are not applicable or provided in this document.

Instead, the document focuses on demonstrating substantial equivalence to a predicate device (VariSource K - K945383) by comparing technological characteristics and meeting relevant safety standards.

Therefore, I cannot provide the requested information regarding AI/ML device performance criteria.

However, I can extract the comparison chart that outlines the device's characteristics and how it compares to its predicate, essentially serving as a form of acceptance criteria for a non-AI medical device.

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

For a traditional medical device like the VariSource 200 HDR Brachytherapy Afterloader, "acceptance criteria" are typically defined by regulatory standards (like IEC 601-2-17) and by demonstrating substantial equivalence to a predicate device. The performance is assessed against these established characteristics and safety thresholds.

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

This information is not applicable to this type of device and is not provided in the document. The submission focuses on hardware and functional equivalence, not data-driven performance.

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)

This information is not applicable to this type of device and is not provided. Ground truth establishment by experts is relevant for diagnostic or AI/ML devices, not for a brachytherapy afterloader.

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

This information is not applicable to this type of device and is not provided. Adjudication methods are typically used in clinical studies or for establishing ground truth for AI/ML algorithms.

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

This information is not applicable, as the VariSource 200 is a hardware device for delivering radiation therapy, not an AI-assisted diagnostic or treatment planning system. Therefore, no human readers or AI assistance are involved in its primary function as described in this submission.

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

This information is not applicable, as the VariSource 200 is a hardware device.

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

This information is not applicable. The "ground truth" for this device relates to its engineering specifications, safety performance (e.g., maximum surface dose, emergency retraction functionality), and its ability to accurately deliver radiation as intended, rather than diagnostic accuracy against a clinical ground truth. Compliance with regulatory standards (e.g., IEC 601-2-17) serves as a primary form of "ground truth" for device safety and performance.

8. The sample size for the training set

This information is not applicable. This is a hardware device, not an AI/ML algorithm that requires a training set.

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

This information is not applicable.

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