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Emit® d.a.u. ™ Opiates Assay is a homogeneous enzyme immunoassay with a 300 ng/mL or 2000 ng/mL cutoff. The assay is intended for use in the qualitative or semiquantitative analysis of opiates in human urine. The assay is used to screen for potential drugs of abuse.

This 510(k) Summary of Safety and Effectiveness is being submitted in accordance with the requirements of SMDA 1990. Syva Company is submitting the Premarket Notification, 510(k) to convey our intention to commercially distribute the modified Emit® d.a.u. ™ Opiates Assay, an in vitro diagnostic reagent test kit for the qualitative or semiquantitative (Emit® d.a.u." ** Opiates Assay at 300 ng/mL only) analysis of Opiates in human urine. The modified Emit® d.a.u. ™ Opiates Assay is a homogenous enzyme assay with a 300 ng/mL cutoff. The modified Emit® d.a.u. ™ Opiates Assay has been found to be equivalent to the predicate device: Emit® II Opiates 300/2000 Assay (K971596) with regard to intended use, assay sample, and overall performance characteristics.

The provided text describes a 510(k) submission for the Emit® d.a.u.™ Opiates Assay. The study is a comparative analysis to demonstrate substantial equivalence to a predicate device, not a standalone study demonstrating novel performance. Therefore, many standard AI/ML study components, such as a training set, number of experts, and adjudication methods, are not applicable in this context.

Here's an analysis of the provided information:

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

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

The sample size for the comparative analysis and precision study is not explicitly stated. The text mentions "spiked samples" and "positive specimens" and "negative samples" but does not give specific numbers.
The data provenance (country of origin, retrospective/prospective) is not mentioned. However, because it's a submission to the FDA, it is likely that the testing was performed in the US.

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

Not applicable. This is not a study requiring expert interpretation of results, but rather a performance study of an in vitro diagnostic assay compared to a predicate method. The ground truth would be based on the known concentration of opiates in the spiked samples or the results from the predicate device for clinical samples.

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

Not applicable. This is a chemical assay, and the results are quantitative or semi-quantitative, not subjective interpretations requiring adjudication.

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 an in vitro diagnostic device, not an AI/ML-driven diagnostic system that assists human readers.

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

Yes, this is a standalone performance study of the Emit® d.a.u.™ Opiates Assay. The assay itself is the "algorithm" in this context, providing results without human intervention in the interpretation of the enzymatic reaction.

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

The ground truth for the comparative analysis against the predicate method would have been:

• Known concentrations for spiked samples: For samples that were "spiked" with opiates, the true concentration of opiates would be known, allowing classification as positive or negative relative to the cutoff.
• Predicate device results: For unspiked clinical specimens, the results obtained from the predicate device (Emit® II Opiates 300/2000 Assay) would serve as the comparator ground truth for agreement calculations.

8. The sample size for the training set

Not applicable. This is an immunoassay, not an AI/ML model that requires a training set. The assay's parameters are determined by its chemical reagents and design, not by machine learning from a dataset.

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

Not applicable. No training set was used.

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