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Dissection tools are intended for cutting and shaping bone including spine and cranium.

The Anspach Dissection Tools are the actual cutting devices designed exclusively for use with Anspach pneumatic or electric motor systems. These Dissection Tools are designed for surgical bone cutting and shaping procedures by trained medical/surgical personnel. Dissection Tools have a standard attachment mechanism, designed specifically for the type(s) of motors and attachments with which they will be used. The base materials of the Dissection Tools are tool steel, stainless steel, or carbide construction with some containing a coated layer of diamond chips. Dissection Tools are components to existing Anspach electric and pneumatic systems. The purpose of this submission is to describe a new design of the locking mechanism of the Dissection Tools. The locking mechanism provides a means to secure a Dissection Tool to the handpiece of the power system. Dissection Tools are sterilized, individually packaged, are for single use and disposable.

The provided text is a 510(k) Premarket Notification for "Dissection Tools" by The Anspach Effort, Inc. It describes a medical device, its intended use, and the performance testing conducted. However, it does not include detailed acceptance criteria or the specific study data that would allow a comprehensive answer to the requested questions regarding AI/ML device evaluation.

The device described is a physical surgical tool, not an AI/ML software device. Therefore, many of the questions related to AI/ML specific evaluation metrics, such as sample size for test sets and training sets, ground truth establishment for AI models, multi-reader multi-case studies, and human-in-the-loop performance, are not applicable to this submission.

Here's what can be extracted and what cannot based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

Not explicitly provided in the document. The document states:

• "Design verification was conducted on the proposed design change of the locking mechanism of the Dissection Tools."
• "These tests include a functional approach that challenged the design output against the design requirements. The tests verified established physical characteristics, functional requirements and performance standards."

While it mentions that tests verified "established physical characteristics, functional requirements and performance standards," no specific quantitative acceptance criteria or corresponding device performance results are listed.

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

Not applicable/Not provided. This question is relevant for AI/ML device validation, typically referring to a dataset of medical images or patient data. For this physical device, "test set" would refer to the number of units tested, but this information is not provided.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of those Experts

Not applicable/Not provided. "Ground truth" in this context typically refers to expert-labeled data for AI/ML models. For a physical device, testing its mechanical function would not involve "expert-established ground truth" in the same way.

4. Adjudication Method for the Test Set

Not applicable/Not provided. This is relevant for resolving discrepancies in expert labeling for AI/ML ground truth.

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

No, not done. MRMC studies are used to evaluate the diagnostic accuracy of AI/ML systems, often comparing human readers with and without AI assistance. This device is a physical dissection tool, so an MRMC study is not relevant.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

No, not applicable. A "standalone" study refers to the performance of an AI algorithm without human input. This is not an AI algorithm.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

Not applicable. As described above, the concept of "ground truth" for AI/ML model training/testing does not apply here. The device went through "design verification" tests to ensure it met "design requirements" and "performance standards," which are forms of ground truth for mechanical devices.

8. The Sample Size for the Training Set

Not applicable/Not provided. This question is for AI/ML models.

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

Not applicable/Not provided. This question is for AI/ML models.

In summary:

The provided document is a 510(k) for a physical medical device (dissection tools), not an AI/ML driven device. Therefore, the questions related to AI/ML specific evaluation methodologies are not addressed in this filing. The filing only states that "Design verification was conducted on the proposed design change of the locking mechanism...These tests include a functional approach that challenged the design output against the design requirements. The tests verified established physical characteristics, functional requirements and performance standards." No specific acceptance criteria or quantitative performance data are detailed in the publicly available summary.

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