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The intended use of the Advanced Gynecological Applicator is intracavitary for cancer treatment of the cervix and endometrium. Optional needles can be placed for interstitial brachytherapy.

The Advanced Gynecological Applicator is a gynecological applicator for intracavitary and interstitial brachytherapy treatment. It can be used it for treatment of the vagina, cervix and uterus. The applicator consists of tubes, caps and templates to place needles. Components can be combined to reach a wide range of target areas. The tubes and the needles, if used, guide the radioactive source of the afterloader to the target volume.

This document is a 510(k) premarket notification letter from the FDA to Nucletron B.V. regarding their Advanced Gynecological Applicator. It primarily addresses the device's substantial equivalence to existing legally marketed devices, rather than presenting a detailed clinical study with specific acceptance criteria, study methodologies, and performance metrics like those typically found for AI/ML-based devices or studies requiring statistically significant findings for efficacy.

Therefore, many of the requested details about acceptance criteria, performance tables, sample sizes, expert involvement, ground truth establishment, and MRMC studies are not present in this document. This letter is a regulatory approval notice, not a clinical study report.

Here's an analysis of the information that is present, addressing your points where possible:

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

This document does not provide a table of quantitative acceptance criteria or specific performance metrics as one would expect for an AI/ML device or a device requiring significant clinical outcome validation. The "acceptance" here is the FDA's determination of substantial equivalence based on a comparison to predicate devices.

The "performance" is generally described qualitatively:

• "The test results demonstrated suitability of the device to its intended uses and clinical acceptance of the device."
• "Usability testing demonstrates that the device can be used by the intended users, under simulated clinical conditions, without serious use errors or problems."
• "Bench testing... shows that the device meets its performance requirements, and that the modified device performance is equivalent to the marketed devices."
• "The results of the testing provided in this submission adequately demonstrate that the Advanced Gynecological Applicator performs as defined in the requirements, in accordance with the recognized standards, meets clinical expectations and is safe and effective for clinical use."

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

• The document states, "Bench testing was performed at a hospital site, under clinical conditions and with the involvement of clinical personnel but excluding the delivery of treatment of patients."
• It does not specify the sample size (e.g., number of tests, number of devices tested, or number of simulated cases).
• The data provenance is implied to be from "a hospital site" where "experienced users" reviewed the device, likely in The Netherlands, given the company's address. It was conducted under "simulated clinical conditions," suggesting it was prospective testing focused on usability and functional performance, not a retrospective clinical data study.

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)

• The document mentions "Experienced users reviewed the device design and executed validation tests."
• It does not specify the number of experts or their specific qualifications (e.g., number of years of experience, specific medical specialty).

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

• The document does not describe any specific adjudication method for performance evaluation, as the testing described is primarily functional and usability-focused, not diagnostic accuracy requiring ground truth establishment through expert consensus or adjudication. The "validation tests" and "usability testing" appear to be direct observations and assessments of the device's function.

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

• No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. This type of study is relevant for diagnostic devices, particularly AI-driven ones where human-AI interaction is assessed. This device is a physical medical instrument (brachytherapy applicator), not a diagnostic imaging or AI software.

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

• Not applicable. This is a physical medical device, not an algorithm.

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

• For this type of device (a physical applicator), "ground truth" would relate to its physical performance, safety, and functional capabilities (e.g., source path accuracy, sterility, material compatibility, ease of use). The "summary of testing" indicates that the device's performance was evaluated against "performance requirements" and "recognized standards." The testing included:
  • Bench testing for performance
  • Usability testing
  • Validation of sterilization processes
  • Biological evaluation (biocompatibility)
  • Testing for use in MR and CT environments.
• The "ground truth" for these tests would be the established engineering specifications, safety standards (e.g., ISO standards for sterility, biocompatibility), and user requirements (for usability). This is not "ground truth" in the sense of clinical diagnostic accuracy.

8. The sample size for the training set

• Not applicable. This is a physical medical device, not an AI/ML algorithm that requires a training set.

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

• Not applicable. This is a physical medical device, not an AI/ML algorithm.

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