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The radial compression device is designed to apply compression in order to achieve temporary hemostasis of the radial artery after a percutaneous transradial procedure.

The Radial Compression Device (RC-001) is designed to promote hemostasis of the radial artery post percutaneous puncture. The radial compression device is fixed on the arm and the square pad of the device is pressed against the puncture site. The square cushion is adjusted to apply pressure and temporarily compress the puncture site of the radial artery to achieve rapid hemostasis. The product is primarily composed of a plate, turn cap, screw rod, hook and loop band, tapered cushion, and square cushion. The Radial Compression Device is available in one model, RC-001.

This document describes a 510(k) premarket notification for the Ningbo DIZENGENS Medical Science Co., Ltd. Radial Compression Device (K232577). The document presents a summary of non-clinical performance testing to support the claim of substantial equivalence to a predicate device. However, it does not contain the detailed acceptance criteria and study results in the format requested.

Specifically, the document does not provide:

• A table of acceptance criteria and reported device performance with specific metrics.
• Sample sizes used for a "test set" in the context of an algorithm's performance.
• Data provenance, number of experts, adjudication method, or ground truth details for an AI/algorithm study.
• Information on Multi-Reader Multi-Case (MRMC) comparative effectiveness studies or standalone algorithm performance.
• Sample size or ground truth establishment for a "training set."

The document primarily focuses on non-clinical performance testing for a physical medical device (a radial compression device), not an AI/software algorithm. The tests conducted are typical for a physical medical device.

Here's a breakdown of what is provided and what elements are missing or not applicable based on the provided text, in the context of your request for AI/algorithm study details:

1. Table of Acceptance Criteria and Reported Device Performance:

• Provided: A list of performance tests conducted. For each test, it states "All testing met the requirements and passed."
• Missing from provided text: Specific numerical acceptance criteria for each test and the corresponding numerical results obtained by the Radial Compression Device. For example, for "Bond strength testing," it doesn't state "Acceptance criteria: > X N, Result: Y N."

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

• Not Applicable in the provided text: This question is relevant for AI/algorithm performance evaluation. The document describes tests for a physical device (e.g., visual inspections, bond strength, biocompatibility), not an AI model. Therefore, there's no "test set" of data in the context of AI.

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

• Not Applicable in the provided text: This question is relevant for AI/algorithm performance. Ground truth for a physical device's non-clinical performance tests is typically established through established engineering standards, laboratory procedures, and physicochemical measurements, not through expert consensus on images or clinical data.

4. Adjudication method for the test set:

• Not Applicable in the provided text: This is relevant for AI/algorithm performance.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done:

• No: The document does not mention any MRMC study, as it's for a physical device and not an AI assistant.

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

• No: The device is a physical Radial Compression Device, not an algorithm.

7. The type of ground truth used:

• Implicitly: For the physical device tests, the "ground truth" is based on engineering specifications, standardized test methods (e.g., ISO standards for biocompatibility and sterilization), and validated laboratory measurements. For instance, biocompatibility is evaluated per ISO 10993, and sterilization per ISO 11135.
• Missing from provided text: No explicit mention of ground truth in the context of expert consensus, pathology, or outcomes data, as these are typically for diagnostic AI/software.

8. The sample size for the training set:

• Not Applicable: There is no AI/algorithm training set.

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

• Not Applicable: There is no AI/algorithm training set.

Summary of Provided Non-Clinical Performance Testing:

The provided document lists the following non-clinical performance tests conducted for the Radial Compression Device:

• Visual inspections
• Dimensional inspections
• Bond strength testing
• Turn Cap and Screw Function
• Distribution and Packaging Tests
• Device and Packaging Aging Evaluation
• Pressure applied at puncture site (to demonstrate adequate compression)
• Biocompatibility evaluation per ISO 10993 (Cytotoxicity, Sensitization, Irritation, Acute Systemic Toxicity, Pyrogenicity, Hemolysis)
• Sterilization (EO gas, SAL of 10-6 per ISO 11135, EO/ECH residuals assessment per 10993-7)

For all of these tests, the document states: "All testing met the requirements and passed."

Conclusion:

The provided text serves as a 510(k) summary for a physical medical device. It details non-clinical performance testing for that physical device to establish substantial equivalence. It does not provide the information requested regarding acceptance criteria and studies for an AI/algorithm device, as the product is not an AI/algorithm.

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High Pressure Tubing is indicated for use in PTCA surgery to connect the angiographic catheter and the angiographic syringe to deliver contrast media.

High Pressure Tubing is designed as a connecting line for injection of contrast media during coronary angiographic procedures. The device has a pressure resistance of 1200psi (8.3MPa); It is divided into two series according to the luer connector combination. ETM series consists of tubing and rotating male luer connecters, and the ETF series consists of tubing and rotating male luer connecter; The rotating male luer connector and female luer connector are made of polycarbonate and silica gel; The tubing is made of polyurethane, and the middle of the tube wall also contains nylon braided wire, which does not contact with outside. The High is for single use only, non-pyrogenic, sterilized by EO gas and the sterilization process is validated.

The question asks to describe the acceptance criteria and the study that proves the device meets the acceptance criteria based on the provided text. However, the provided text is a 510(k) Pre-market Notification for a "High Pressure Tubing" device, which describes its comparison to a predicate device and includes information about performance testing, but it does not contain information typically found in a study proving an AI/ML device meets acceptance criteria.

Specifically, the document discusses a medical device that is a physical product (High Pressure Tubing) used in PTCA surgery. It does not mention any AI or machine learning components. Therefore, information regarding "sample sizes used for the test set," "data provenance," "number of experts used to establish ground truth," "adjudication method," "MRMC comparative effectiveness study," "standalone performance," "type of ground truth," "sample size for training set," or "how ground truth for training set was established" is completely absent and not applicable to this document.

The document primarily focuses on establishing substantial equivalence to a predicate device by comparing technological characteristics and presenting results of biocompatibility and bench testing.

Based on the provided text, the following information can be extracted regarding the physical device's performance testing, which is analogous to "acceptance criteria" and "device performance" for a non-AI medical device:

1. Table of Acceptance Criteria and Reported Device Performance (as inferred from the bench testing section):

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

• Not Applicable. The document describes performance and bench testing of a physical medical device, not a software or AI/ML device. Specific sample sizes for each test are not provided, nor is data provenance in the context of clinical studies (e.g., country of origin, retrospective/prospective). The tests were conducted to assure reliable design and performance.

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. This information is relevant for AI/ML device validation where human experts establish ground truth. For this physical device, the "ground truth" is established by adherence to recognized international standards (e.g., ISO 10993, ISO 8536-4, ISO 80369-7, ISO 80369-20).

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

• Not Applicable. Adjudication methods are used in clinical studies or expert reviews for AI/ML devices to resolve discrepancies in ground truth establishment. This document pertains to physical device 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:

• Not Applicable. MRMC studies are specific to AI/ML devices involving human readers. This document is about a physical high-pressure tubing.

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

• Not Applicable. This concept is only relevant for AI/ML algorithms.

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

• Not Applicable. For this physical device, "ground truth" is adherence to established engineering specifications and international standards for medical device safety and performance.

8. The sample size for the training set:

• Not Applicable. This device is a physical product and does not involve AI/ML models that require training data.

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

• Not Applicable. This device is a physical product and does not involve AI/ML models that require training data.

In summary, the provided document is a 510(k) Pre-market Notification for a conventional (non-AI/ML) medical device. Therefore, most of the questions related to AI/ML device validation are not applicable. The "acceptance criteria" and "device performance" for this specific device are demonstrated through adherence to relevant international standards and successful completion of biocompatibility and bench testing as listed above.

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