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The Inverness Medical™ Early Pregnancy Test is intended for use by lay consumers for the qualitative detection of hCG in urine as an aid in the detection of early pregnancy.

The modified device is substantially equivalent to the previously cleared Selfcare Predicate Device Early Pregnancy Test (K960436). The primary modifications are changes to the strip width and specimen sample size. Corresponding dimensional changes have been made to the housing to accommodate the narrower strip design. The modified device has the same technological characteristics as the legally marketed predicate device employing monoclonal antibodies for immunochromatographic assay.

The provided text describes the 510(k) summary for the Inverness Medical™ Early Pregnancy Test. However, it does not provide specific acceptance criteria or detailed study results in quantifiable metrics. Instead, it makes general statements about testing and substantial equivalence.

Here's an attempt to answer your questions based only on the provided text, acknowledging where information is missing:

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

Specific quantitative acceptance criteria (e.g., sensitivity >X%, specificity >Y%) and their corresponding measured performance values are not provided in the document.

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

• Sample Size: The document does not specify the sample size used for the test set in any of the described evaluations (repeatability, sensitivity, interferences, professional and consumer accuracy).
• Data Provenance: The document does not specify the country of origin of the data or whether the studies were retrospective or prospective.

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)

The document does not provide information on the number of experts used or their qualifications for establishing ground truth. The device is for "lay consumers for the qualitative detection of hCG in urine," suggesting the "consumer accuracy" evaluations might have involved real users, but the specifics of ground truth determination (e.g., confirmation by a clinical lab) are absent.

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

The document does not describe any specific adjudication method for the test 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

This section is not applicable as the device described is an "Early Pregnancy Test Device" employing "immunochromatographic assay," which is a diagnostic test kit and not an AI-assisted diagnostic tool that would typically involve human readers interpreting images or complex data with or without AI assistance.

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

This concept is not applicable in the context of an immunochromatographic assay for pregnancy. The device itself is "standalone" in the sense that it provides a result, but it doesn't involve an "algorithm" in the way an AI medical device would. "Human-in-the-loop performance" would be similar to "consumer accuracy" and "professional accuracy," which were evaluated.

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

The document implies that the ground truth for hCG detection would be established by reference laboratory methods or clinical confirmation of pregnancy, but it does not explicitly state the specific type of ground truth. For a pregnancy test, the ground truth for hCG levels is typically determined by quantitative blood tests or confirmed clinical pregnancy.

8. The sample size for the training set

The document does not describe a "training set" in the context of machine learning or AI. The "testing verification and validation testing activities" relate to traditional device performance evaluation, not AI model development.

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

Not applicable, as no training set (in the AI/ML sense) is mentioned.

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