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When used with a fluoroscope, the EBI® TargetCath™ Fluoro-Guided Steerable Catheter System can be used in the lumbar and sacral spine for delivery of drugs approved for epidural indications. The System may also be used for the purpose of assisting in the diagnosis and treatment of disease utilizing a caudal approach via the sacral hiatus.

The EBI® TargetCath™ Fluoro-Guided Steerable Catheter System consists of several components and different accessories for delivery of approved epidural drugs. This system includes a disposable catheter, and various accessories.

The provided 510(k) summary for the EBI® TargetCath™ Fluoro-Guided Steerable Catheter System is for a medical device, not an AI/ML-driven diagnostic or assistive technology. Therefore, many of the typical acceptance criteria and study components related to AI performance metrics (like sensitivity, specificity, MRMC studies, ground truth establishment for AI, and training/test set details) are not applicable to this submission.

This submission focuses on demonstrating substantial equivalence to a predicate device based on design, materials, intended use, and mechanical testing. The information provided does not contain details about acceptance criteria in the context of an AI/ML algorithm's diagnostic performance, nor does it describe a study designed to prove such performance.

Here's an analysis based on the information provided, highlighting why certain AI-specific questions cannot be answered:

1. Table of Acceptance Criteria and Reported Device Performance:

The document broadly states that "mechanical testing demonstrates that the device meets its functional requirements." However, it does not provide a table with specific acceptance criteria (e.g., tensile strength, steerability force, drug delivery rate consistency) or quantitative performance data to show the device meets those criteria. The comparison is made against a predicate device, focusing on substantial equivalence rather than novel performance claims requiring specific quantitative acceptance thresholds.

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

• Not Applicable in the AI/ML context. For a mechanical device, a "test set" would refer to the number of devices subjected to mechanical or bench testing. The document does not specify the sample size for this mechanical testing.
• Data Provenance: Not applicable in the context of clinical data for AI.

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

• Not Applicable in the AI/ML context. The ground truth for this device would be established by engineering specifications and objective measurements from mechanical tests, not by expert interpretation of data relevant to an AI algorithm.

4. Adjudication method for the test set:

• Not Applicable in the AI/ML context. Adjudication methods like "2+1" are used for resolving disagreements among human readers in a diagnostic setting, which is not relevant here.

5. If a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done:

• No, an MRMC study was not done. This type of study is specifically designed to evaluate the diagnostic performance of an AI system, often comparing it with human readers or human readers assisted by AI. This is not relevant for a steerable catheter system demonstrating substantial equivalence through mechanical testing.

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

• Not Applicable. There is no AI algorithm involved in this device.

7. The type of ground truth used:

• Mechanical Specifications/Engineering Standards: For a physical device like a catheter, the "ground truth" would be established by predefined engineering specifications, material properties, and functional requirements verified through physical and mechanical testing. The document states "mechanical testing demonstrates that the device meets its functional requirements," implying such a ground truth.

8. The sample size for the training set:

• Not Applicable. There is no AI model or training set involved.

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

• Not Applicable. There is no AI model or training set involved.

In summary, both the acceptance criteria and the study described in this 510(k) summary are for a conventional medical device (a catheter) and not for an AI/ML-powered system. Therefore, the questions posed, which are tailored for AI/ML device evaluations, are largely not applicable. The core of this submission is demonstrating "substantial equivalence" to a predicate device through comparison of design, materials, intended use, and general statement of meeting functional requirements via mechanical testing.

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