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Remote Afterloading Brachytherapy Unit for interstitial, intracavitary, intraluminal, including bronchial, endovascular (PARIS IDE), intraoperative and surface applicator treatments.

The Treatment Control Station (TCS) described in this submission is a software and hardware package which replaces the current Treatment Control Unit (TCU) for the microSelectron-HDR classic. The TCS is windows based software and runs on a PC based computer system. TCS will allow the user to program a treatment and monitor a treatment in progress. TCS will come together with a Treatment Control Panel, which takes care of the secondary timing and providing hardware independent "Start", "Interrupt" button and source location indicators. The TCS user obtains authorization for parts of the functionality depending on the user name and password. Treatment data can either be entered manually, based on a standard plan, based on a previously fraction or Imported from the Nucletron Treatment Planning System (PLATO). Prior and after treatment completion an extensive report is generated providing full details of how the patient will be and is treated.

The provided text describes a Premarket Notification (510(k)) for the Nucletron microSelectron-HDR classic with TCS (Treatment Control Station). This submission focuses on software and hardware upgrades to an existing remote afterloading brachytherapy system.

Based on the provided document, the submission is a 510(k) for device approval, which primarily demonstrates substantial equivalence to predicate devices. It does not contain information about a dedicated clinical study with specific acceptance criteria, performance metrics, ground truth establishment, or human reader effectiveness as would typically be found for novel diagnostic AI devices.

The document states that "Our Device is substantially equivalent to the legally marketed predicate devices cited in the table below," implying that performance is assumed to be similar to legally marketed devices rather than being proven through a separate study with specific acceptance criteria.

Therefore, many of the requested details are not present in this type of regulatory submission. I will answer based on the information available and note where information is not provided.

Here's a breakdown of the requested information:

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

No explicit acceptance criteria or reported device performance metrics (e.g., accuracy, sensitivity, specificity for diagnostic devices) are provided in this regulatory submission. The submission is focused on demonstrating "substantial equivalence" to existing, legally marketed devices for a software and hardware upgrade. The "performance" being assessed here is the device's ability to fulfill its intended use in a similar manner to its predicate.

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

This information is not provided. This type of 510(k) submission for a software/hardware upgrade to a therapeutic device often relies on engineering verification and validation, rather than a clinical "test set" with patient data in the way a diagnostic AI device would.

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 provided. As there's no mention of a clinical "test set" involving patient data and diagnostic outcomes, the concept of establishing ground truth by medical experts in this context is not applicable from the provided text.

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

This information is not provided, as there is no described test set or ground truth establishment by experts in the document.

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

There is no mention of a Multi-Reader Multi-Case (MRMC) comparative effectiveness study. This device is a treatment control station for brachytherapy, not a diagnostic AI device intended to assist human readers in image interpretation. Therefore, this type of study would not be relevant in this context.

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

This information is not provided, and the concept of "standalone performance" for a treatment control station (which is inherently a human-in-the-loop system) would not directly apply. The TCS is an interface and control system for a medical device, requiring operator input and monitoring.

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

This information is not provided. The regulatory submission focuses on the technical specifications and safety/effectiveness equivalence to predicate devices, rather than clinical performance against a medical ground truth (like pathology or outcomes data). The "ground truth" in this context would likely relate to the correct and safe execution of treatment plans and control of the brachytherapy unit, which would be assessed through engineering and software validation.

8. The sample size for the training set

This information is not provided. The device is a software and hardware upgrade to control a therapeutic device. It is not an AI/ML model that would have a traditional "training set" of data in the sense of learning from patient images or diagnostic outcomes.

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

This information is not provided, as there is no traditional "training set" described for a machine learning model.

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