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The cannulas/ needles for anesthesia and analgesia enhanced for ultrasound visibility -Tuohy Sono, Sono TAP, Quincke Sono, Chiba Sono and Crawford Sono - are intended for the transient delivery of anesthetics to provide regional anesthesia and analgesia or to facilitate placement of a catheter.

The cannulas/ needles for anesthesia and analgesia enhanced for ultrasound visibility are single use sterile and non-pyrogenic devices used to gain entry or puncture the tissue and inject anesthetics to induce single shot anesthesia and analgesia. The needles/ cannulas may be used during all anaesthetic and analgetic procedures according to the physicians indication. Cannulas/ needles for anesthesia and analgesia enhanced for ultrasound visibility are standard cannulas/ needles equipped with CornerStone reflectors (cleared in K111374 - SonoPlex STIM) in order to significantly enhance ultrasound visibility. The cannulas basically consist of stainless steel tubing and an epoxy glued polycarbonate hub. The tubing is optionally coated for better insertion properties with NanoLine, a copolymeric coating cleared for market in K053283. In order to enhance ultrasound visibility the cannulas are equipped with a special reflector pattern named CornerStone imprinted to the cannulas surface. These reflectors are designed to optimally reflect ultrasound waves.

The provided document is a 510(k) summary for a medical device (cannulas/needles enhanced for ultrasound visibility). It describes the device, its intended use, technological characteristics, and comparison to predicate devices, ultimately concluding substantial equivalence. However, it does not contain any information about acceptance criteria or a study proving the device meets those criteria, especially in the context of an AI/algorithm-based device as implied by the structure of the request.

The request's format (e.g., "effect size of how much human readers improve with AI vs without AI assistance," "standalone (i.e. algorithm only without human-in-the-loop performance)") strongly suggests it's looking for information related to the performance validation of an AI/Machine Learning device, where accuracy metrics, ground truth establishment, and test set details are critical.

The provided document describes a physical medical device (needles), and its validation revolves around:

• Bench Testing: Bending stability, breaking resistance, hub-to-needle-bondage.
• Biocompatibility: Compliance with ISO 10993-1.
• Sterilization: Validation of cleaning and sterilization process, shelf life.
• Technological Equivalence: Comparing dimensions, materials, and manufacturing processes to previously cleared predicate devices.

Therefore, I cannot fulfill the request as phrased because the provided document does not contain the type of AI/algorithm performance data, acceptance criteria, or study details that the prompt is asking for.

If the intent was to understand the acceptance criteria for a physical medical device as described in the document, here's a conceptual answer based on the document's content, reinterpreting "acceptance criteria" as the grounds for demonstrating "substantial equivalence":

Based on the provided document, the "acceptance criteria" and "study" are interpreted in the context of demonstrating substantial equivalence for a physical medical device (ultrasound-enhanced needles) rather than an AI/algorithm. No specific quantitative performance acceptance criteria in terms of accuracy, sensitivity, or specificity are mentioned, as this is not an AI/diagnostic device.

Here's an attempt to map the request's structure to the available information, noting significant gaps:

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

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

• Sample Size: Not explicitly stated for any of the "standard testing" (bending, breaking, bonding). The document implies routine internal testing for "each cannula" or "all cannulas," but not a specific study sample size for validation.
• Data Provenance: The testing is implied to be internal testing by PAJUNK® GmbH Medizintechnologie in Germany. No mention of retrospective or prospective study types in the context of device performance validation beyond "bench testing" and "biocompatibility testing."

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

• N/A. This question is relevant for AI/diagnostic devices where expert labeling establishes ground truth. For this physical device, "ground truth" refers to engineering specifications, material properties, and physical performance against established standards (e.g., ISO, internal company standards). No human "experts" are cited for establishing ground truth in the document's validation section.

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

• N/A. This is relevant for AI-assisted image interpretation or similar diagnostic tasks. Not applicable to the physical validation of a needle.

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:

• N/A. This is specific to AI/diagnostic devices. The device described here is a physical medical needle.

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

• N/A. Not an algorithm-only device.

7. The type of ground truth used:

• For Mechanical Performance: Engineering specifications, physical measurements, and compliance with internal (PAJUNK®) and potentially industry standards for bending, breaking, and bonding.
• For Biocompatibility: Compliance with ISO 10993-1 standards and material specifications.
• For Sterilization: Measured sterility assurance level (SAL), residual limits, and validated shelf life.
• For Ultrasound Visibility: Implied to be assessed in "clinical evaluation" (details not provided) and implicitly by the design ("CornerStone reflectors designed to optimally reflect ultrasound waves"). No explicit 'ground truth' definition like pathology or outcomes data is mentioned; it's a functional claim validated via unspecified means.

8. The sample size for the training set:

• N/A. No training set is involved as this is not an AI/Machine Learning device.

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

• N/A. No training set for an AI model.

In summary, the document details the substantial equivalence of a physical medical device (ultrasound-enhanced needles) to predicate devices through bench testing, biocompatibility, and sterilization validation, rather than the performance validation of an AI/algorithm.

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