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The QUANTA Lite™ OMP Plus kit is an enzyme-linked immunosorbent assay (ELISA) for the semi-quantitative detection of anti-OMP antibodies of the IgA class in human serum. It is intended to be used in conjunction with anti-Saccharomyces cerevisiae (S. cerevisiae) antibody (ASCA) IgG and/or IgA test systems. The presence of OMP (outer membrane proteins) IgA antibodies, used in conjunction with clinical findings and other laboratory tests, may aid in the diagnosis of patients with Crohn's disease.

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This is an FDA 510(k) clearance letter for the QUANTA Lite™ OMP Plus IgA ELISA device. This type of document typically focuses on establishing substantial equivalence to a predicate device and does not contain detailed study results or acceptance criteria in the format you've requested for AI/Software as a Medical Device (SaMD).

The information provided describes a laboratory diagnostic test kit for detecting IgA antibodies, not an AI or SaMD product. Therefore, many of the requested categories, such as sample sizes for test sets and training sets, number and qualifications of experts, adjudication methods, MRMC studies, and standalone AI performance, are not applicable to this type of medical device submission and are not present in the provided text.

Based on the document, here's what can be extracted and inferred:

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

This document does not specify formal acceptance criteria in a table or directly report performance metrics like sensitivity, specificity, or AUC for the device itself. Instead, the FDA's clearance is based on the device being substantially equivalent to a legally marketed predicate device. This implies that the performance characteristics (e.g., precision, accuracy, linearity) would have been assessed during the validation studies submitted to the FDA, and deemed comparable to the predicate. However, these specific metrics and acceptance criteria are not detailed in the clearance letter itself.

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

Not explicitly stated in this clearance letter. Clinical study details (sample sizes, provenance, study design) are part of the full 510(k) submission, not typically in the clearance letter.

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):

Not applicable. This is a laboratory diagnostic test. Ground truth would be established through clinical diagnosis of Crohn's disease, potentially with a panel of clinical experts, but this information is not in the clearance letter.

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

Not applicable. This is a laboratory diagnostic test, not an imaging device requiring expert 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 a laboratory diagnostic test, not an AI-assisted diagnostic tool.

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

Not applicable. This is a laboratory diagnostic test. The "standalone performance" is the performance of the assay kit itself, which would be measured in a lab setting. The clearance letter doesn't detail these study specifics.

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

For this type of device (an assay for IgA antibodies against OMP), the ground truth for evaluating its clinical utility would typically be the clinical diagnosis of Crohn's disease, established through a combination of clinical findings, endoscopy, imaging, and potentially other laboratory tests or pathology. However, the specific methodology for establishing this ground truth in the studies supporting the 510(k) is not contained in this letter.

8. The sample size for the training set:

Not applicable. This is a laboratory diagnostic test, not a machine learning model that requires a "training set" in the conventional AI sense. The assay works based on biochemical reactions.

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

Not applicable for the same reason as above. The "ground truth" for developing and validating such an assay would involve known positive and negative samples for the target antibodies, often derived from clinically characterized patient populations.

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