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These sets are indicated for the infusion of fluids into the body below the surface of the skin when attached to a fluid reservoir.

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The provided text is a 510(k) summary for the Unomedical A/S Intuition Infusion Sets. It discusses the device's substantial equivalence to existing products and verifies that it meets specifications. However, the document does not contain the detailed information necessary to answer many of the specific questions asked about acceptance criteria and a study design, especially concerning AI/ML aspects.

Here's what can be extracted and what cannot:

1. Table of Acceptance Criteria and Reported Device Performance

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

This information is not provided in the document. The document only states "Verification testing confirmed the product meets their specifications" but gives no details about the size or nature of the test set, nor its provenance.

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)

This information is not provided in the document. This type of device (infusion set) does not typically involve expert review for ground truth in the way medical imaging AI does. The "ground truth" for an infusion set would likely be functional performance metrics, material properties, and manufacturing tolerances, all assessed through engineering and quality control tests, not expert consensus on medical images or diagnoses.

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

This information is not provided in the document. Adjudication methods are typically relevant for studies involving human interpretation or subjective assessments, which are not described here for an infusion set.

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

No, an MRMC comparative effectiveness study was not done. This document describes an infusion set, which is a physical medical device, not an AI/ML algorithm used to assist human readers (e.g., in medical image interpretation). Therefore, the concept of "human readers improve with AI vs without AI assistance" is not applicable to this submission.

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

Not applicable. This device is a physical infusion set, not an algorithm. Therefore, "standalone algorithm performance" is not relevant.

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

The document implies that the ground truth for performance was product specifications. For an infusion set, this would typically involve various engineering, material science, and functional tests (e.g., fluid flow rates, strength of materials, sterility, biocompatibility, force required for insertion, leakage rates). It would not be expert consensus, pathology, or outcomes data in the way these terms are used for diagnostic or AI devices.

8. The sample size for the training set

This information is not provided and is not applicable. This is a physical device, and the concept of a "training set" is relevant for AI/ML algorithms, not for the verification of a medical infusion set.

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

This information is not provided and is not applicable for the same reasons as point 8.

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