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Heel Incision Safety Lancet is intended for the collection of capillary blood from the heel of newborn, preemie, and toddler. The lancet has equipped with safety protection features.

Heel Incision Safety Lancet is comprised of top upper cover, bottom cover, button, safety plug, slider, rod, holder, spring, blade. The spring provides an elastic force to puncture and ensure the blade can shrink back to the covers. The blade can be fired when the spring is under pressure. The safety plug is to protect the blade from triggering before use. Heel Incision Safety Lancet is single use, sterile medical devices designed to be used in collecting the blood sample. Heel Incision Safety Lancet is intended to be used by professionals. Heel Incision Safety Lancets are sterile and non-toxic. The product is intended for prescription (Rx) only.

The provided document is a 510(k) Premarket Notification for a medical device (Heel Incision Safety Lancet) and primarily focuses on demonstrating substantial equivalence to a predicate device. It is not a study proving a device meets acceptance criteria for an AI/ML-based medical device.

Therefore, many of the requested details regarding acceptance criteria, AI/ML study design, sample sizes, expert involvement, and ground truth establishment (especially for AI/ML models) are not applicable and not present in this document. This document describes a traditional medical device (a lancet) and its non-clinical performance testing.

However, I can extract the closest analogous information available from the document regarding acceptance criteria and performance for this non-AI/ML device:

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

The document provides a "Summary of non-clinical testing" with "Items" (which can be interpreted as the performance characteristics for which acceptance was sought) and "Results" (the performance shown). While explicit "acceptance criteria" values (e.g., "must be > X N Force") are not always stated, the "Results" implicitly demonstrate acceptance where they state "No failures observed", "Meet the requirement", or "Compared with equivalent devices, there was no statistical difference."

Biocompatibility Testing:

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

• Test Set Sample Sizes: For key functional tests like "Safety self-locking," "Safety plug pullout," "Shooting performance," and "Accidental access to sharp once in safe mode," a test run of 500 devices was used, with "no failures observed." For other physical property and material tests, specific sample sizes are not explicitly stated, but are implied to be sufficient for meeting the standard/requirement (e.g., "All models have been tested within tolerances").
• Data Provenance: The tests are "bench testing" and "biocompatibility evaluations" conducted by the manufacturer, SteriLance Medical (Suzhou) Inc., based in Suzhou, Jiangsu, P.R.China. The data is prospective, generated specifically for this 510(k) submission.

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

This is not applicable as this is not an AI/ML device requiring expert-established ground truth from medical images or clinical data. The "ground truth" for this device's performance is established by direct measurement of physical properties (e.g., dimensions, forces) and standard laboratory tests (e.g., material analysis, sterility, biocompatibility).

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

This is not applicable. Adjudication methods like 2+1 or 3+1 are used in clinical studies or AI/ML ground truth establishment where human readers might disagree. For bench testing of a physical device, results are typically objective measurements or pass/fail outcomes against defined specifications.

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 is not applicable as this is a physical medical device, not an AI/ML-based diagnostic or assistive technology. No MRMC study was performed.

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

This is not applicable as this is a physical medical device, not an AI/ML algorithm.

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

As explained, for this physical device, the "ground truth" for performance is established by:

• Direct physical measurements (e.g., dimensions, force values).
• Standardized analytical tests (e.g., material composition, sterility).
• Biocompatibility tests according to ISO standards.
• Pass/fail criteria based on safety and functional requirements.

8. The sample size for the training set

This is not applicable as this is a physical medical device, not an AI/ML algorithm that requires a training set.

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

This is not applicable as this is a physical medical device, not an AI/ML algorithm that requires a training set and associated ground truth establishment.

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