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The Expect™ EUS-FNA device is intended for sampling targeted submucosal and extramural gastrointestinal lesions through the accessory channel of a curvilinear echoendoscope.

The Expect™ Endoscopic Ultrasound Aspiration Needle (EUS-FNA) is an endoscopic ultrasound aspiration needle that can be coupled to the biopsy channel of a Curvilinear Array (CLA) Echoendoscope with a standard luer connection and delivered into the digestive tract. The needle is used to acquire aspiration samples from lesions within and adjacent to the digestive system's major lumens that can be identified and targeted using the echoendoscope. An aspiration sample is obtained by penetrating the lesion with the needle while applying suction.

The provided document describes a 510(k) premarket notification for a medical device, the Expect™ Endoscopic Aspiration Needle, and not a study assessing the device's diagnostic performance for an AI/ML product. The document focuses on demonstrating substantial equivalence to a predicate device through bench testing rather than clinical performance data typical of AI/ML software.

Therefore, many of the requested categories in your prompt related to clinical study design, AI/ML performance metrics, ground truth, and expert evaluation cannot be extracted from this document.

Here's an analysis of what can be extracted based on the provided text, and where information is not available:

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

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

• Sample Size for Test Set: Not applicable. This was a bench test of a physical device component, not an AI/ML diagnostic test with a "test set" of clinical data.
• Data Provenance: Not applicable.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• Not applicable. Ground truth as typically defined for AI/ML diagnostic studies is not relevant here as it's a bench test of mechanical and material properties.

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

• Not applicable. No expert adjudication process was described for the bench testing.

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. An MRMC study was not conducted as this is a physical medical device, not an AI/ML diagnostic software.

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

• Not applicable. No algorithm is involved.

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

• Not applicable. The "ground truth" for this device's performance relies on engineering specifications and validated test methods for mechanical and material properties (design verification) and biocompatibility, not clinical data or expert consensus on diagnosis.

8. The sample size for the training set

• Not applicable. There is no training set mentioned, as this is not an AI/ML device.

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

• Not applicable. No training set was used.

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