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Model 3991A Series Leads are indicated for use for epidural spinal cord stimulation (SCS) as an aid in the treatment of chronic intractable pain of the trunk and/ or limbs. All other uses are considered investigational.

The Medtronic Model 3991a series of Transverse Tripolar™ Leads, or TTL™ leads are indicated for use for SCS for chronic intractable pain of truck or limbs, as for the Model 3982 Symmix®, Model 3883, Model 3586 and Model 3487A leads. There are three modifications in the Model 3991a series of leads from the original Model 3991C (K952459). These modifications are discussed as follows: A. Spiral-wound wires in lead body. B. Crimps and ferrules in the paddle. C. Radiopaque markers in the tips of the paddle.

The provided document is a 510(k) premarket notification letter from the FDA regarding a medical device, the Medtronic Model 3991a series of Transverse Tripolar™ Leads. This document outlines the FDA's determination of substantial equivalence and does not contain detailed information about acceptance criteria or specific study results that would typically be found in a clinical study report or a pre-market approval (PMA) submission.

Therefore, many of the requested details cannot be extracted from this document.

Here's what can be inferred and what is missing:

1. Table of Acceptance Criteria and Reported Device Performance:

• Acceptance Criteria: Not explicitly stated in this document. The submission focuses on demonstrating "substantial equivalence" to predicate devices. Substantial equivalence generally means that the new device is as safe and effective as a legally marketed device and does not raise different questions of safety and effectiveness.
• Reported Device Performance: The document mentions "bench tests on similar Model 3991C leads" and "Returned Product Analysis of one explanted lead" which led to design changes. However, specific quantitative performance metrics (e.g., failure rates, durability under specific conditions) are not provided in this summary.

2. Sample size used for the test set and the data provenance:

• Not specified. The document mentions a "single device failure" from a returned product and "bench tests on similar Model 3991C leads," but no details on the sample size or provenance of these tests are given.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Not applicable. This document does not describe a study involving expert assessment or ground truth establishment in the context of device performance. The modifications were based on engineering analysis and physician recommendations for improved usability (radiopaque markers), not on expert consensus for a test set.

4. Adjudication method for the test set:

• Not applicable. There is no mention of a test set requiring adjudication in this document.

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. This device is a spinal cord stimulation lead, an implantable medical device, not an AI-powered diagnostic or interpretive tool that would involve human readers or AI assistance.

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

• Not applicable. This is not an AI algorithm.

7. The type of ground truth used:

• Not applicable in the typical sense of a diagnostic or predictive device. The "ground truth" for the device modifications appears to be:
  • Engineering analysis of device failure: For the spiral-wound wires and crimps/ferrules.
  • Physician feedback/clinical utility: For the radiopaque markers.

8. The sample size for the training set:

• Not applicable. This is not an AI algorithm requiring a training set.

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

• Not applicable.

In summary, the provided document is a regulatory communication from 1997 for a medical device that predates the common use of AI in medical devices and the detailed reporting requirements for AI-enabled devices. It focuses on demonstrating substantial equivalence of design modifications to an existing product, rather than providing extensive clinical study data with acceptance criteria or performance metrics as would be expected for a novel device or AI algorithm today.

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