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Para-Pro™ fc50 is a unique, patent pending device for the separation of fecal debris from the appropriate preserved specimen (10% buffered formalin; SAF sodium acetate acetic acid formaldehyde; and Proto-fix™ CLR) for the concentration of eggs, larvae, protozoa and juvenile nematodes associated with intestinal infections.

Para-Pro™ fc50 is a unique, patent pending device for the separation of fecal debris from the appropriate preserved specimen (10% buffered formalin; SAF sodium acetate acetic acid formaldehyde; and Proto-fix™ CLR) for the concentration of eggs, larvae, protozoa and juvenile nematodes associated with intestinal infections.

This is a 510(k) premarket notification for a Class I microbiology specimen collection and transport device, not an AI/ML device. Therefore, the requested information about acceptance criteria, study data, ground truth, and training sets for an AI/ML algorithm is not present in this document.

The document states that the device is "substantially equivalent" to legally marketed predicate devices. This means that a direct comparison against a predicate device was performed, rather than an independent study with acceptance criteria and statistical analysis of performance as would be expected for an AI/ML device.

Here's what can be extracted from the document relevant to a traditional medical device submission:

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

The document does not explicitly present a table of acceptance criteria and reported device performance in the manner of an AI/ML device study. Instead, the FDA determined the device is "substantially equivalent" to legally marketed predicate devices. This typically implies that the device performs comparably to the predicate device for its intended use. For
a microbiology specimen collection and transport device, acceptance criteria would likely relate to its ability to:

• Effectively separate fecal debris.
• Preserve eggs, larvae, protozoa, and juvenile nematodes for subsequent analysis.
• Be compatible with specified preservatives (10% buffered formalin; SAF sodium acetate acetic acid formaldehyde; and Proto-fix™ CLR).
• Maintain the viability or integrity of the parasites within the specified timeframes.

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

This information is not provided in the document as it's a 510(k) summary focused on substantial equivalence. A detailed study protocol with sample sizes, data provenance, and study design (retrospective/prospective) would be part of the supporting documentation submitted to the FDA, but is not included in this 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)

This concept of "ground truth" established by experts is typically relevant for diagnostic devices that produce an output requiring expert interpretation (e.g., imaging devices, AI algorithms). For a specimen collection and transport device, the "ground truth" would be established by laboratory methods assessing the recovery and preservation of parasites, which is distinct from expert consensus on an interpretation. The document does not provide details on such a study or expert involvement in establishing performance benchmarks.

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

Not applicable in the context of this device and the information provided. Adjudication methods are typically for resolving discrepancies in expert interpretations, which is not the primary evaluation method for a specimen collection and transport device.

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 traditional medical device, not an AI/ML driven diagnostic.

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

Not applicable. This is a physical device, not an algorithm.

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

The "ground truth" for this type of device would likely be based on laboratory-confirmed presence and condition of parasites. This would typically involve microscopic examination by trained laboratory personnel after processing the samples and comparing results from the device against a gold standard method. The document does not specify this type of detail.

8. The sample size for the training set

Not applicable. This is a traditional medical device, not an AI/ML device that requires a training set.

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

Not applicable. This is a traditional medical device, not an AI/ML device.

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