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The Beacon Fine Needle Fiducial System is intended to implant fiducial markers under endoscopic ultrasound to radiographically mark soft tissue for future therapeutic procedures. The needle is designed with a passive (i.e. automatic) safety shielding feature to aid in the prevention of needle stick injury.

The Beacon™ Fine Needle Fiducial System is a sterile, single-use, endoscopic ultrasound device which consists of the Beacon™ Endoscopic Ultrasound (EUS) Delivery System and a Beacon™ Fine Needle Fiducial (FNF) Needle preloaded with fiducial markers. The Beacon™ EUS Delivery System with Beacon™ FNF Needle is inserted through the accessory channel of an ultrasound endoscope. The Beacon™ FNF Needle houses two fiducial markers and a fiducial spacer enclosed with bone wax. A blunt stylet contained within the Beacon™ FNF Needle allows for manual deployment of the fiducial markers to the intended location. Once the fiducial markers have been implanted, the Beacon™ FNF Needle may be removed from the Beacon™ EUS Delivery System and replaced with a new Beacon™ FNF Needle for placing additional fiducial markers. The Beacon™ Fine Needle Fiducial System has an integrated needle protection shield that automatically encloses the distal end of the needle during removal to cover the needle sharp. The needle tip is covered to help protect against inadvertent needle sticks.

This document is a 510(k) summary for the Beacon Fine Needle Fiducial System, which is a medical device. It does not describe a study that uses AI or machine learning models to analyze medical images or data.

Instead, it's a submission to the FDA demonstrating that a new medical device (the Beacon Fine Needle Fiducial System) is "substantially equivalent" to an already legally marketed device (the BNX Fine Needle Aspiration System). This process primarily involves comparing the new device's indications for use, technological characteristics, and safety and performance testing to the predicate device.

Therefore, many of the requested details about acceptance criteria, device performance, sample sizes for test/training sets, expert ground truth, adjudication methods, and MRMC studies are not applicable to the content provided.

However, I can extract information related to the device's performance bench testing and its overall conclusion of meeting specifications.

Here's a breakdown of what can be inferred from the provided text, and where gaps exist due to the nature of the document:

1. Table of Acceptance Criteria and Reported Device Performance

Explanation: The document states, "Bench testing was performed for attributes affected by the modifications made to the subject device. Bench testing evaluated such characteristics as fiducial deployment force, fiducial echogenic performance, bond strengths, and deployment indicator functionality." It then adds, "Performance testing for the Beacon™ Fine Needle Fiducial System also consisted of user validation testing, biocompatibility assessment, sterilization validation, packaging validation, shelf life testing, Image Guided Radiotherapy Compatibility, and Magnetic Resonance testing." The conclusion clearly states, "Testing performed demonstrated that the Beacon™ Fine Needle Fiducial System met the required specifications for its intended use."

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

• Not Applicable. This document describes the substantial equivalence of a physical medical device, not the performance of an AI model on a test set of data. The "testing" mentioned refers to bench testing of the physical properties and functionality of the device itself.

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

• Not Applicable. This is not a study requiring expert-established ground truth for an AI model.

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. This document does not describe an AI-assisted diagnostic or interpretative device, nor an MRMC study.

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. The "ground truth" for this device's performance would be engineering specifications, material properties, and biological safety standards.

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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