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The FluChip-8G Influenza A+B Assay is a multiplex RT-PCR in vitro diagnostic test intended for the qualitative detection and differentiation of seasonal influenza A/H3N2, seasonal influenza A/H1N1pdm09, and "non-seasonal" influenza A subtypes other than seasonal H1N1pdm09 or H3N2. The assay is also intended for the qualitative detection and differentiation of the genetic lineage of human influenza B viruses as B/Victoria or B/Yamagata. The assay is designed for use on influenza nucleic acids isolated and purified from nasopharyngeal swab and nasal swab specimens from human patients with signs and symptoms of respiratory infection with clinical and epidemiological risk factors.

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The FluChip-8G Influenza A+B Assay is a multiplex RT-PCR in vitro diagnostic test for the qualitative detection and differentiation of influenza A and B viruses.

Here's a breakdown of the requested information based on the provided text:

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

The provided text describes the indications for use of the FluChip-8G Influenza A+B Assay and mentions how performance characteristics were established. However, it does not explicitly state specific acceptance criteria (e.g., sensitivity, specificity thresholds) or a detailed table of reported device performance metrics against those criteria. Instead, it describes how performance was assessed.

For "non-seasonal" influenza A viruses, the document states:
"Due to low prevalence of 'non-seasonal' influenza A viruses, performance characteristics of the FluChip-8G Influenza A +B Assay for detecting 'non-seasonal' influenza A viruses and distinguishing 'non-seasonal' influenza A from seasonal influenza A H1N1pdm09 and H3N2 were assessed exclusively by conducting cross-validation on a total of 759 microarray images generated from bench testing contrived samples consisting of 352 unique 'non-seasonal' influenza A strains representing 62 subtypes, and by bench testing contrived samples and surrogate clinical specimens consisting of 133 unique non-seasonal influenza A strains representing 46 subtypes."

This indicates that evaluation was done, but specific performance metrics (e.g., sensitivity, specificity, accuracy) are not provided in this excerpt. The text implies the performance met the criteria for clearance, but the criteria themselves are not listed.

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

• Test set sample size:
  • For "non-seasonal" influenza A:
    • 759 microarray images from contrived samples (352 unique strains, 62 subtypes).
    • Contrived samples and surrogate clinical specimens (133 unique strains, 46 subtypes).
  • The document implies that seasonal influenza A/H3N2 and influenza B/Victoria data were used, but specific numbers for these are not provided.
• Data provenance: Not explicitly stated in terms of country of origin. The document mentions "the United States" in the context of predominant circulating strains ("seasonal influenza A/H3N2 was the predominant influenza A virus circulating in the United States" and "influenza B/Victoria was the predominant influenza B virus circulating in the United States"), suggesting the data is relevant to the US.
• Retrospective or prospective: The assessment for "non-seasonal" influenza A involved "bench testing contrived samples" and "surrogate clinical specimens," which sounds like a laboratory-controlled study rather than a prospective clinical trial on real patients. The nature of data used for seasonal influenza A and B is not specified.

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 given text.

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

This information is not provided in the given text.

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 is an in vitro diagnostic (IVD) device (a molecular assay) and not an AI-powered diagnostic imaging device or an AI assistant for human readers. Therefore, an MRMC comparative effectiveness study comparing human readers with and without AI assistance is not applicable and was not performed.

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

This is an in vitro diagnostic assay, which intrinsically operates in a "standalone" manner in terms of its analytical performance. While a human interprets the results generated by the assay, the "algorithm" (the RT-PCR and microarray detection process) performs its function independently of real-time human interpretation during the detection phase. The performance assessment described for "non-seasonal" influenza A via "cross-validation on a total of 759 microarray images generated from bench testing" and "bench testing contrived samples and surrogate clinical specimens" reflects the standalone performance of the assay.

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

The ground truth for the "non-seasonal" influenza A assessment was based on contrived samples and surrogate clinical specimens. This implies that the 'true' presence and subtype of the virus in these samples were known implicitly through their creation or characterization. For seasonal influenza A and B, the nature of the ground truth is not explicitly stated, but it would typically involve confirmed reference methods for viral identification and subtyping.

8. The sample size for the training set

The provided text describes performance assessment but does not mention a separate "training set" or its sample size. This assay is a molecular diagnostic test, not a machine learning algorithm in the typical sense that requires explicit training on a dataset to learn patterns. While internal development and optimization would have involved extensive testing, the public FDA document focuses on the validation of the final product.

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

As no specific "training set" is mentioned in the context of a machine learning algorithm, the establishment of ground truth for such a set is not applicable based on the provided text.

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