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This device is used to sample targeted submucosal gastrointestinal lesions through the accessory channel of an ultrasound endoscope. The target population is adult use only.

The ECHO-3-22: Echotip Ultra and the EUSN-1, EUSN-3: Echotip Ultrasound Needles are sterile, single use devices. The Ultrasound Needles consist of an adjustable luer slip handle, an outer and an inner catheter with a needle, extension locks (ECHO-3-22) or spacers (EUSN-1, EUSN-3), a stylet, and a syringe with a stopcock. All devices are compatible with ultrasound endoscopes with a minimum accessory channel of 2.8 mm. The catheter is made of polyether ether ketone, PEEK (ECHO-3-22), or polytetrafluoroethylene, PTFE (EUSN-1, EUSN-3), while the needle of all devices is stainless steel, and the stylet is nitinol. The tips of the needles are dimpled for echogenicity.

This document describes a 510(k) premarket notification for a medical device and is a regulatory submission, not a study report. Therefore, it does not contain the detailed information required to answer your request about acceptance criteria and a study proving device performance as typically expected for an AI/ML medical device.

The document states that "Performance testing consisting of non-clinical bench testing demonstrates that the Ultrasound Needles meet the performance requirements to fulfill the intended use of the device." and lists the types of non-clinical testing conducted. However, it does not provide:

• Specific acceptance criteria values or the reported device performance against those criteria.
• Any information about sample sizes for test sets, data provenance, expert qualifications, adjudication methods, MRMC studies, standalone performance, or ground truth details because these are concepts typically associated with diagnostic or AI/ML device performance evaluations, not for a biopsy needle.
• Any training set information as this device is a physical medical instrument, not an AI/ML algorithm.

Therefore, I cannot populate the table or provide the requested details from the provided document. The document confirms that non-clinical bench testing was performed to demonstrate performance, but the specifics of that performance or the criteria used are not included in this summary.

Here's what can be extracted and what cannot:

1. Table of acceptance criteria and the reported device performance:
* Cannot be provided. The document states that "Performance testing ... demonstrates that the Ultrasound Needles meet the performance requirements" but does not define these requirements (acceptance criteria) or report specific performance metrics or values.

2. Sample size used for the test set and the data provenance:
* Cannot be provided. This document describes non-clinical bench testing of a physical device (a needle), not a diagnostic algorithm with a "test set" of patient data.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
* Not applicable. Ground truth, in the context of expert consensus, is relevant for diagnostic device performance evaluation using patient data, not for the physical performance testing of a biopsy needle itself.

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

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:
* No. This is a physical medical device (biopsy needle), not an AI/ML diagnostic system. Therefore, an MRMC study is not relevant here.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
* Not applicable. This is a physical medical device, not an algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
* Not applicable. Ground truth is not relevant in the context of the physical performance testing of a biopsy needle against engineering specifications.

8. The sample size for the training set:
* Not applicable. This device is a physical instrument, not an AI/ML algorithm that requires a "training set."

9. How the ground truth for the training set was established:
* Not applicable. See point 8.

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