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The ELAN 4 Electro Motor System is intended for high speed cutting, sawing, and drilling of bone in the fields of Spine, ENT, Neuro, and Maxillofacial Surgery.

The ELAN 4 Software V3.00, Wireless Foot Control, Drill and Attachments are designed for use with the ELAN 4 Electro Motor System (K152960). The ELAN 4 Electro Motor System is an electrical motor system consisting of a control unit with different sizes and types of hand-pieces, each containing its own integrated motor, and attachments such as burrs, saw blades, drills, etc.

The Control Unit of the ELAN 4 Electro Motor System houses the system software. The ELAN 4 Software V3.00 provides support for the ELAN 4 Wireless Foot Control and ELAN 4 Drill. The ELAN 4 drill is a pistol type hand-piece designed specifically to accept various ELAN 4 attachments.

The provided text describes the regulatory clearance of a medical device, the ELAN 4 Electro Motor System, not an AI/ML powered device. As such, information regarding acceptance criteria, performance studies, sample sizes, ground truth establishment, expert adjudication, or comparative effectiveness studies (MRMC) that would typically apply to AI/ML devices is not applicable or present in this document.

The document discusses "performance data" in the context of:

• Bench testing: In-house design verification to ensure mechanical and functional requirements, including design specifications, were met.
• Biocompatibility: Evaluation according to ISO-10993.
• Electrical safety and electromagnetic compatibility (EMC): Compliance with IEC 60601-1 and UL 2601-1 standards for safety and IEC 60601-1-2 for EMC.
• Software Verification and Validation Testing: Conducted in accordance with FDA guidance for software in medical devices, with the software considered a "moderate" level of concern.

Summary of available information:

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

2. Sample size used for the test set and the data provenance: Not applicable. The tests performed are primarily engineering and regulatory compliance tests, not studies involving datasets for AI/ML performance.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable.

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: Not applicable.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Not applicable.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable. "Ground truth" in this context refers to established engineering specifications, regulatory standards, and functional requirements for the device.

8. The sample size for the training set: Not applicable.

9. How the ground truth for the training set was established: Not applicable.

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The ELAN 4 Tools are intended for high speed cutting, sawing, and drilling of bone in the fields of Spine, ENT, Neuro, and Maxillofacial Surgery.

The ELAN 4 Tools are cutting devices designed for use with the ELAN 4 Electro Motor System. The ELAN 4 Tools are designed for cutting, sawing and drilling of bone. The ELAN 4 Tools have an attachment mechanism designed specifically for the type of motors and attachments with which they will be used. The ELAN 4 Tools are manufactured from stainless steel. The ELAN 4 Tools are components of the ELAN 4 Electro Motor System.

The provided text describes a medical device, the Aesculap ELAN 4 Tools, and its regulatory submission to the FDA. The submission asserts substantial equivalence to predicate devices based on various tests and comparisons. However, the document does not contain information about the performance of a device based on AI/ML.

Here's an analysis of the provided text in relation to your request about acceptance criteria and study details for an AI/ML device:

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

• The document presents a table of bench tests and their results, which could be considered analogous to acceptance criteria and performance for a mechanical device.
• The acceptance criterion for each test is implicitly "Pass," meaning the device performed as intended or within acceptable design limits (e.g., "Design not suitable, suitable for speeds up to 100,000 rpm" for the rotating performance test).
• The reported performance for all listed bench tests (Rotating performance test for burrs and disc, Functional testing of the tool adaptation with small blade, Measurement of oscillation frequency and fatigue test (small blade), Measurement of oscillation frequency and fatigue test (large blade), Measurement of temperature) is "Pass."

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 (K172709) describes the testing of electromechanical surgical tools, not an AI/ML device. Therefore, the concepts of "test set" and "data provenance" in the context of AI/ML are not applicable here.
• For the bench tests, the sample sizes are not explicitly stated, but these would typically involve a small number of physical samples for mechanical testing, not a large dataset for algorithm evaluation.

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 applicable. The device is a surgical tool, and its performance is evaluated against engineering specifications and physical measurements, not against expert interpretation of data.

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

• This information is not applicable. Adjudication methods are relevant for human interpretation of data, often in clinical studies with expert reviewers, which is not the case for this mechanical device.

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

• This information is not applicable. MRMC studies are used for evaluating AI performance in clinical settings where human readers interpret medical images or data. The Aesculap ELAN 4 Tools are mechanical surgical devices.

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

• This information is not applicable. There is no algorithm mentioned for this device; it is a physical tool.

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

• For the bench tests described, the "ground truth" is based on engineering specifications, physical measurements, and established industry standards (e.g., ISO standards for biocompatibility and sterilization). The "Pass" results indicate adherence to these predefined criteria and expected performance.

8. The sample size for the training set

• This information is not applicable. The device is a mechanical tool, not an AI/ML model, so there is no "training set."

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

• This information is not applicable. As there is no training set, there is no ground truth for it.

In summary, the provided document describes the regulatory clearance for a mechanical surgical device and its supporting non-clinical (bench) testing, not an AI/ML-driven medical device. Therefore, most of the requested information pertaining to AI/ML device evaluation is not present in the text.

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