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510(k) Data Aggregation
(90 days)
The NTERO HOTROD™ System is intended to coagulate tissue during surgical procedures.
Not Found
The provided text describes a 510(k) submission for the NTERO HOTROD™ System, an electrosurgical device. It outlines the intended use, classification, equivalent devices, and summary of the substantial equivalence determination by the FDA. However, the document does not contain the specific information required to answer your questions about acceptance criteria and a study proving those criteria are met for an AI/device performance study.
Here's why and what information is missing:
- No Acceptance Criteria or Stated Performance: The document states "Results of animal testing demonstrate that the NTERO HOTROD™ System is safe and effective for its intended function," but it does not specify any quantitative acceptance criteria (e.g., specific coagulation depth, time to achieve hemostasis, or a quantifiable measure of "effectiveness" or "safety") or report numerical device performance against such criteria.
- No AI Component: The NTERO HOTROD™ System is an electrosurgical device for tissue coagulation, not an AI-powered diagnostic or therapeutic device. Therefore, questions related to AI performance, such as sample sizes for test/training sets, data provenance, ground truth establishment by experts, adjudication methods, MRMC studies, or standalone algorithm performance, are not applicable to this device description.
- Study Details are Limited: The study mentioned is "animal testing" for safety and effectiveness and "biocompatibility" testing for materials. No detailed methodology, sample sizes, or specific results are provided beyond the general statement of safety and effectiveness.
Based on the provided text, I cannot complete the table or answer the specific questions related to acceptance criteria and an AI-related study. The document is a regulatory filing for a traditional medical device, not an AI-driven one, and lacks the detailed performance metrics you've requested.
However, I can extract and state what is available based on your prompt's structure:
Acceptance Criteria and Device Performance
Acceptance Criteria | Reported Device Performance |
---|---|
Safety and effectiveness for intended function (coagulation of tissue during surgical procedures) | Demonstrated through animal testing. |
Biocompatibility of materials in direct tissue and blood contact | Materials have been shown to be biocompatible. |
Detailed Study Information (Based on available text):
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Sample size used for the test set and the data provenance:
- Test Set Sample Size: Not specified. The document only mentions "animal testing."
- Data Provenance: Animal testing. No country of origin is specified. The study type is effectively "prospective experimental" as it involved conducting tests on animals.
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Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. This was animal testing for a non-AI device. Ground truth establishment by experts in the context of diagnostic image interpretation, for example, is not relevant here. The "ground truth" would be direct physiological outcomes in the animals.
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Adjudication method for the test set: Not applicable. This was animal testing for a non-AI device.
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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 applicable. The NTERO HOTROD™ System is an electrosurgical device, not a diagnostic AI system with human readers.
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If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Not applicable. The NTERO HOTROD™ System is an electrosurgical device, not an algorithm.
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The type of ground truth used: Direct physiological and histological observation in animal models and material property testing for biocompatibility.
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The sample size for the training set: Not applicable. No "training set" in the context of machine learning, as this is not an AI device.
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How the ground truth for the training set was established: Not applicable.
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