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The Sterile syringes for single use with/without needle is intended to be used for medical purposes to inject fluid into or withdraw fluid from body.

The Sterile Syringes for Single Use with/without Needle is a three-piece, sterile, single use hypodermic syringe with a 6% (Luer) male connector/lock fitting in various sizes. The syringe assembly consists of a lubricated polypropylene barrel with a graduated scale, a lubricated synthetic rubber stopper and a polypropylene plunger rod. The plunger rod is pulled back to aspirate fluids or depressed to inject or expel fluids.

This document is a 510(k) premarket notification for a medical device (Sterile syringes for single use with/without needle). It outlines the performance data provided to support the claim of substantial equivalence to a predicate device, rather than defining novel acceptance criteria for an AI/ML powered device and proving those are met.

Therefore, many of the requested details about acceptance criteria, ground truth establishment, sample sizes for AI/ML training/test sets, expert adjudication, and MRMC studies are not applicable or extractable from this specific document.

The document focuses on demonstrating that the new syringe device performs equivalently to existing legally marketed predicate devices by complying with established international and FDA-recognized standards for mechanical, physical, chemical, and biological properties.

Here's a breakdown of what can be extracted and what cannot:

What can be extracted:

• Device Name: Sterile syringes for single use with/without needle
• Intended Use: "intended to be used for medical purposes to inject fluid into or withdraw fluid from body."
• Study Type: This is a premarket notification (510(k)) which relies on demonstrating substantial equivalence to a predicate device, not necessarily a de novo clinical study proving novel performance criteria for an AI/ML algorithm. The performance data is primarily for physical, chemical, and biological characteristics of the syringe, not AI/ML performance.
• Acceptance Criteria & Reported Performance Table: While not in the typical "AI/ML performance" format, the document lists specific standards and states that "All evaluation acceptance criteria were met."

• Ground Truth Type: For this device, the "ground truth" is defined by the established international standards and testing methodologies (e.g., ISO, ASTM, USP) for physical, chemical, and biological properties of syringes, rather than expert labels on medical images or outcomes data.
• Sample Size:
  • Test set: The document doesn't explicitly state sample sizes for each test, but implies that sufficient samples were tested to demonstrate compliance with the referenced standards. For example, "The shelf life of 5 year is determined based on stability studies which include ageing test according to FDA recognized standard ASTM F1980-16," which would involve a specific number of samples over time.
  • Training set: Not applicable, as this is not an AI/ML device requiring a training set.
• Data Provenance: The manufacturing and testing entities are based in China (Wepon Medical Technology CO., Ltd. in Zhejiang, China, and Shanghai Ling Fu Technology Co., Ltd. in Shanghai, China). The data would be retrospective in the sense that it was collected during product development and validation for submission, rather than a prospective clinical trial.

What cannot be extracted (because it's not an AI/ML device):

• Number of experts used to establish ground truth & qualifications: Not applicable; ground truth is standard compliance.
• Adjudication method for test set: Not applicable.
• MRMC comparative effectiveness study: Not applicable.
• Effect size of human readers improvement with AI vs. without AI assistance: Not applicable.
• Standalone (algorithm only) performance: Not applicable.
• How the ground truth for the training set was established: Not applicable.

In summary, this 510(k) pertains to a conventional medical device (syringes) and demonstrates substantial equivalence by meeting recognized performance standards, not by evaluating an AI/ML algorithm's diagnostic or predictive capabilities.

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