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The Nipro Safelet Cath is intended to be inserted into a patient's vascular system for short term use to sample blood, monitor blood pressure or administer fluids intravenously.

The devices we intend to market are intravascular catheters as described in 21 CFR 880.5200. The subject devices are similar to catheters currently marketed by Nipro Medical Corporation under K960051. Two types of catheters will be available: the container cases types L (long) and S (short). Type L catheters have needles 1 1/2 to 2 1/2 inches long and Type S have needles ¾ to 1 ¼ inches long. Six gauges of catheters are described here (14, 16, 18, 20, 22 and 24 gauge). The tip of the cannula is beveled. The proximal end of the catheter has a filter cap.

This is a premarket notification for a medical device (Nipro Safelet Cath), not an AI/ML medical device. Therefore, the information typically requested regarding acceptance criteria and studies proving performance for AI devices (such as sample size for test/training sets, expert qualifications, adjudication methods, MRMC studies, or standalone algorithm performance) is not applicable or available in this document.

The document is a 510(k) summary demonstrating substantial equivalence to a legally marketed predicate device (K960051), rather than presenting novel performance data against specific acceptance criteria for an AI algorithm.

However, I can extract information relevant to the device itself as presented in the document:

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

This document describes a medical device (intravascular catheter) and focuses on demonstrating its substantial equivalence to a previously marketed device (K960051). It does not present specific quantitative acceptance criteria or detailed performance results in the way an AI diagnostic device submission would. Instead, the "performance" here is implied by the similarity in technical characteristics and intended use to the predicate device.

2. Sample size used for the test set and the data provenance: Not applicable. This is not an AI/ML device, and no "test set" in the context of algorithm performance is mentioned. The submission relies on comparison to a predicate device.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for an AI algorithm is not relevant for this type of device submission.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable. No test set requiring expert adjudication for ground truth is mentioned.

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. This is not an AI-assisted device.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable.

8. The sample size for the training set: Not applicable.

9. How the ground truth for the training set was established: Not applicable.

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