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When used with a fiberoptic endoscope, the EBI VueCath™ Spinal Endoscopic System can be used in the lumbar and sacral spine for observing epidural anatomy, pathology and delivery of drugs approved for epidural indications.

In addition, the system may be used by physicians for the illumination and visualization of tissues of the epidural space in the lumbar and sacral spine for the purpose of assisting in the diagnosis of disease utilizing a caudal approach via the sacral hiatus.

The EBI VueCath™ Endoscopic Spinal System is an arthroscope consisting of several components and different accessories for viewing the lumbar and sacral spinal anatomy. This system includes a fiberscope, disposable catheter, and various accessories. The fiberscope is designed to connect to any compatible commercially available endoscopic video imaging system by using a camera coupler and light cord adapters.

This document is a 510(k) Summary of Safety and Effectiveness for a medical device called the EBI VueCath™ Spinal Endoscopic System. It is a submission to the FDA (Food and Drug Administration) to demonstrate that the device is substantially equivalent to legally marketed predicate devices.

The request asks for information related to acceptance criteria and a study proving the device meets these criteria. However, this 510(k) summary does not contain the level of detail typically found in a clinical study report for AI/software-as-a-medical-device (SaMD) products. This submission is for an endoscopic system, which is a physical medical device, not a diagnostic AI algorithm. Therefore, many of the requested points, such as "number of experts used to establish ground truth," "adjudication method," "MRMC comparative effectiveness study," or "sample size for training set," are not applicable to this type of device and submission.

The "study" referenced in this document is primarily a comparison to predicate devices and bench testing to confirm functional requirements.

Here's the closest possible answer based on the provided text, acknowledging the limitations:

Acceptance Criteria and Device Performance Study

As this is a submission for a physical medical device (an endoscopic system), not an AI/SaMD product, the concept of "acceptance criteria" and "study" differs significantly from what might be expected for an AI algorithm. The primary "acceptance criteria" for this device, as implied by the 510(k) process, is substantial equivalence to existing legally marketed predicate devices, along with meeting its functional requirements through bench testing.

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

• Illumination and visualization for diagnosis assistance |

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

This information is not provided in the 510(k) summary. For a physical device, "test set" and "data provenance" as described typically refer to clinical data. This document relies on bench testing and comparison to predicates, not a clinical trial with a "test set" in the context of diagnostic accuracy.

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

This information is not provided and is not applicable to this type of device submission. Ground truth for diagnostic accuracy is not the focus here.

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

This information is not provided and is 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

This information is not provided and is not applicable. This is a physical endoscopic system, not an AI-assisted diagnostic tool.

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

This information is not applicable. This describes a physical endoscopic system, not an algorithm.

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

For the "bench testing" mentioned, the "ground truth" would be the engineering specifications and functional requirements of the device. For example, if a criterion is "light output intensity," the ground truth is the specified intensity in lumens, and the device is tested against that. For safety, this would involve material compatibility and sterility testing, where "ground truth" is established through industry standards and regulatory requirements for biocompatibility and sterilization.

8. The sample size for the training set

This information is not provided and is not applicable. This is not an AI algorithm that requires a "training set."

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

This information is not applicable.

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