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The MDA is a multipurpose system for in vitro coagulation studies and is capable of running various clot based, chromogenic and immunoassays.

The Multi-Channel Discrete Analyzer (MDA) system is a fully automated, random access analyzer used to perform clinical analyses related to hemostasis and thrombosis. The instrument determines results or reaction rates by detecting changes in the light transmitted through a reaction mixture. Flexibility in optics, fluidics, and software allow the MDA to perform many different assays including traditional clotting assays, chromogenic assays, and immunoassays. The MDA B.30 software version Q08.00 is an update to the MDA B.23 software version Q05.00 and was developed with the same intended use. The modifications to the MDA B.30 software consists of the following: 1. add new methods in order to accommodate new OEM supplied chromogenic reagents; 2. new endpoint algorithms to reduce the erroneous error rate without increasing the erroneous result rate; 3. new wash macro for Simplastin HTF to minimize precipitate build up in Probe D; 4. add a new B4 latex method to allow customers to validate a Free Protein S assay; 5. add Italian and Spanish languages; and 6. new and enhanced waveform analysis features

Here's an analysis of the provided 510(k) summary regarding the MDA® B.30 device, structured to address your specific questions.

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

The document does not explicitly state the specific sample size used for the test set in terms of number of patient samples. It mentions "12 representative assays" for precision testing and "samples that had increased levels of interfering substances" for robustness testing.

• Sample Size: Not explicitly stated for each test, but "12 representative assays" are mentioned for precision.
• Data Provenance: Not specified. The document does not indicate the country of origin of the data or whether it was retrospective or prospective.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

This information is not provided in the given 510(k) summary. The summary focuses on internal performance metrics and comparison to a predicate device, not on expert-adjudicated ground truth for a clinical test set.

4. Adjudication Method for the Test Set

This information is not applicable and not provided because the study described is a performance comparison of software versions on an in vitro diagnostic device, not a study involving human reader interpretation requiring adjudication.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. This type of study is relevant for imaging or diagnostic systems where human readers interpret output with and without AI assistance to assess human performance improvement. The MDA B.30 is an automated coagulation instrument, and the assessment focuses on its automated performance.

6. If a Standalone Study (i.e., algorithm only without human-in-the-loop performance) Was Done

Yes, a standalone study was done. The entire submission and the described tests refer to the performance of the MDA B.30 instrument's software and its associated algorithms directly comparing its output to that of the predicate device (MDA B.23) and general performance criteria (precision, robustness). There is no human-in-the-loop component discussed for the performance evaluation.

7. The Type of Ground Truth Used

The ground truth implicitly used for this device performance study appears to be:

• Predicate Device Performance: The MDA B.23 software served as the primary reference for comparison, with "100% agreement" being a key metric.
• Established Analytical Performance Criteria: For precision, there were "criteria stated" against which the 12 representative assays were measured.
• Known Reference Values: For samples with interfering substances, there was an implied "correct clotting time" that the device was expected to determine.

Essentially, the "ground truth" was based on the expected analytical performance of a clinical laboratory instrument and its consistency with a previously cleared version.

8. The Sample Size for the Training Set

The document does not provide information regarding a "training set" for the MDA B.30 software. This is because the MDA B.30 is an update to existing software (B.23), focusing on adding new methods, refining algorithms for existing methods, and improving error handling. It's not described as a machine learning system that undergoes explicit "training" with a distinct dataset in the common sense of AI development. The software development likely involved internal testing and refinement based on existing data and domain knowledge, rather than a separate "training set" as might be documented for a novel AI algorithm.

9. How the Ground Truth for the Training Set Was Established

Since no "training set" is explicitly mentioned or suggested in the context of typical AI algorithm training, this question is not applicable based on the provided document. The changes described are primarily software updates and algorithm refinements rather than training a new model from scratch.

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