Search Results

intended for single use by trained physician to assist in vessel cannulation.

Vascular Access Kit - containing dilators, guide wire, scalpel, introducer needle, and syringe.

The provided text describes the 510(k) submission for the Avalon Elite Vascular Access Kit. This is a medical device, and the information presented primarily focuses on its substantial equivalence to a predicate device based on validation testing. It does not describe an AI/ML-based device, a comparative effectiveness study with human readers, or an algorithm-only standalone performance study. Therefore, several sections of your request are not applicable.

Here's an analysis of the provided text based on your request, focusing on what is available:

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

The document does not explicitly list "acceptance criteria" in a quantitative manner (e.g., "device must achieve X% accuracy"). Instead, it lists validation tests conducted to demonstrate safety and effectiveness compared to a predicate device. The "reported device performance" is a general conclusion that the device "is as safe and effective as the legally marketed predicate device" based on these tests.

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

The document does not specify sample sizes for any of the validation tests. It also does not provide information on data provenance (country of origin, retrospective/prospective). This type of detail is typically found in the full study reports, not the 510(k) summary.

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

This information is not applicable as the device is not an AI/ML-based diagnostic device that relies on expert interpretation for ground truth establishment. The validation tests are largely laboratory-based and engineering assessments.

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

Not applicable. There's no mention of an adjudication process, as the validation tests are not based on subjective interpretation requiring expert consensus.

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. The device is a physical vascular access kit, not an AI-assisted diagnostic tool, so an MRMC study is not relevant.

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

Not applicable. The device is a physical medical kit and does not involve an algorithm with standalone performance.

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

For the validation tests conducted (Biocompatibility, Packaging Integrity, Transportation Integrity, Sterilization Validation, Functional Testing), the "ground truth" would be established through defined scientific and engineering standards and methods relevant to each test. For example:

• Biocompatibility: In vitro and/or in vivo tests according to ISO standards (e.g., ISO 10993 series). Ground truth is meeting predefined toxicity/irritation thresholds.
• Packaging/Transportation Integrity: Physical tests to ensure sterility barrier and product integrity are maintained under simulated conditions. Ground truth is successful completion of tests without integrity compromise.
• Sterilization Validation: Microbiological testing and adherence to validated sterilization cycles (e.g., ISO 11135 or 11137). Ground truth is achieving a specified sterility assurance level (SAL).
• Functional Testing: Bench testing to evaluate component strength, fit, ease of use, etc. Ground truth is meeting specified performance parameters.

8. The sample size for the training set

Not applicable. The device is not an AI/ML-based device that requires a training set.

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

Not applicable. As there is no training set for an AI/ML model, the establishment of ground truth for such a set is irrelevant.

Ask a specific question about this device