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Light Diagnostics™ Human Metapneumovirus DFA Kit is intended for the detection and identification of human metapneumovirus (hMPV) in direct respiratory specimen cell preparations from nasopharyngeal swabs from patients with febrile respiratory illness. This assay detects but is not intended to differentiate the four recognized genetic sub-lineages of hMPV.

Negative results do not preclude hMPV infection and should not be used as the sole basis for diagnosis, treatment or other management decisions. It is recommended that specimens found to be negative after examination of the direct specimen results be confirmed by FDA cleared hMPV molecular assay.

For In Vitro Diagnostic Use.

Light Diagnostics Human Metapneumovirus DFA Kit utilizes a single reagent for the detection and identification of human metapneumovirus. The fluorescein labeled monoclonal antibodies, specific for human metapneumovirus will bind to viral antigen in human metapneumovirus infected cells. Unbound reagent is removed by rinsing with phosphate-buffered saline (PBS/Tween 20). Illumination allows visualization of the antigenantibody complex by fluorescence microscopy. When a FITC filter set is used, the human metapneumovirus antibody complex will exhibit an apple-green fluorescence. Uninfected cells stain a dull red due to the presence of Evans blue in the reagent.

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

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

• Sample Size for Test Set: 411 nasopharyngeal swab specimens.
• Data Provenance: Retrospective clinical specimens were collected from two sites:
  • Site One: A regional medical center in southeastern Canada (208 specimens).
  • Site Two: A hospital laboratory in the northeastern United States (199 specimens).
  • Site Three: Additional 200 specimens were submitted for testing, of which four were nasopharyngeal swabs.

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

The document does not specify the number or qualifications of experts used to establish the ground truth for the test set.

4. Adjudication method for the test set

The ground truth for the test set was established using a composite algorithm based on cell culture results and a validated RT-PCR method followed by bidirectional sequencing for confirmation and identification of human metapneumovirus. This implies a hierarchical or multi-modal adjudication process rather than an expert-based one.

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

A multi-reader multi-case (MRMC) comparative effectiveness study was not done. This study focuses on the performance of a direct immunofluorescence assay (DFA) device against a composite reference method, not human reader performance with or without AI assistance.

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

This study evaluates a diagnostic kit which is inherently intended for use by a human operator (reading fluorescence microscopy slides). Therefore, a purely standalone "algorithm only" performance (without human-in-the-loop) was not conducted in the context of an automated AI system. The performance presented for the Light Diagnostics™ hMPV DFA Kit represents its performance when used as intended by an operator.

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

The type of ground truth used was a composite algorithm based on cell culture results and a validated RT-PCR method followed by bidirectional sequencing for confirmation and identification of human metapneumovirus.

8. The sample size for the training set

The document does not explicitly mention a separate "training set" sample size for an AI algorithm. This submission describes the validation of a diagnostic kit (DFA) which relies on antibody-antigen binding observed via microscopy by a human, not a machine-learning based algorithm that would typically require a training set. If the "training set" refers to the data used to develop the assay's reagents, it is not specified.

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

As there is no explicit mention of a training set for an AI algorithm, the method for establishing its ground truth is not applicable/not provided in this document. The assay's development (e.g., selection of monoclonal antibodies) would have been guided by traditional laboratory methods for identifying hMPV, but this is not akin to a machine learning training set ground truth establishment.

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