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The TRUCLEAR™ Morcellator System is intended for use in gynecological procedures by trained professional gynecologists to resect and remove endometrial tissue for the following indications, submucous myomas and endometrial polyps.

The TRUCLEAR Morcellator System was first introduced in K031787. The method of operation is mechanical resection and suction to cut and remove submucous myomas and endometrial polyps from the uterine lining. The system consists of a control unit, handpiece and footswitch. The system is used in conjunction with disposable hysteroscopic blades.

The provided text describes a 510(k) submission for the TRUCLEAR Morcellator System, which is a medical device for gynecological procedures. The submission focuses on demonstrating substantial equivalence to a predicate device rather than presenting a de novo study with acceptance criteria and a detailed analysis of a new device's performance against those criteria.

Therefore, the specific information requested cannot be fully extracted as it pertains to a different type of submission (e.g., a study proving a device meets specific performance criteria, often seen in AI/CADe submissions).

Here's a breakdown of what can be extracted and what cannot:

1. Table of Acceptance Criteria and Reported Device Performance:

• Cannot be provided directly. The document states: "The TRUCLEAR Morcellator System performance testing has demonstrated that the proposed device is substantially equivalent to the predicate device and the proposed modifications to increase the blade speed range does not raise new questions of safety and efficacy."
• This implies that the "acceptance criteria" were primarily demonstrating equivalence and ensuring that the modification (increased blade speed range) did not negatively impact safety or efficacy, rather than meeting specific quantifiable performance metrics in a new clinical study.
• The "reported device performance" is essentially that it performs equivalently to the predicate and the modification is safe.

2. Sample Size Used for the Test Set and Data Provenance:

• Cannot be provided. The document does not describe a clinical study with a test set of patient samples. Performance testing was likely mechanical/bench testing to demonstrate the safety and equivalence of the increased blade speed.

3. Number of Experts Used to Establish Ground Truth and Qualifications:

• Cannot be provided. This information is relevant for studies involving human interpretation or annotation (e.g., image-based AI studies), which is not described here.

4. Adjudication Method for the Test Set:

• Cannot be provided. Not applicable due to the nature of the submission.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, Effect Size:

• No, an MRMC study was not done. This type of study is common in AI/CADe for comparing human performance with and without AI assistance. This submission is for a mechanical tissue resection system.

6. If a Standalone Performance Study Was Done:

• No, a standalone performance study as typically understood for AI algorithms was not done. The performance testing focused on demonstrating substantial equivalence to the predicate device and the safety/efficacy of the increased blade speed, not a standalone clinical validation against a specific disease outcome in a patient cohort.

7. The Type of Ground Truth Used:

• Not explicitly stated as "ground truth" in the AI sense. For this device, "ground truth" would be related to the functional performance and safety parameters of the device (e.g., cutting efficiency, tissue removal, maintaining sterility, structural integrity). This would likely be assessed through engineering tests, material testing, and potentially simulated tissue models, rather than clinical patient outcomes.

8. The Sample Size for the Training Set:

• Cannot be provided. This concept is not applicable. There is no AI component or machine learning model that would require a training set.

9. How the Ground Truth for the Training Set Was Established:

• Cannot be provided. Not applicable.

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