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The Olympus AU5400 Clinical Chemistry Analyzer is a fully automated photometric analyzer intended for clinical laboratory use. Applications include colorimetric, turbidimetric, latex agglutination, and homogeneous enzyme immunoassay.

The Olympus AU5400 Clinical Chemistry Analyzer is a fully automated photometric analyzer.

While the provided document is a 510(k) clearance letter for the Olympus AU5400 Clinical Chemistry Analyzer, it does not contain the detailed performance study results, acceptance criteria, or ground truth information typically found in the actual 510(k) submission or a scientific publication.

The letter confirms that the device has been found substantially equivalent to predicate devices, meaning it is considered safe and effective for its indicated use. However, it does not explicitly state the specific performance metrics (like sensitivity, specificity, accuracy), the thresholds for acceptance of those metrics, or the specifics of the validation study.

Therefore, I cannot populate all the requested fields from the given text. I can only infer some information based on the nature of a 510(k) submission for a clinical chemistry analyzer.

Here's what I can convey based on the provided document and general understanding of 510(k) submissions for similar devices:

1. Table of Acceptance Criteria and Reported Device Performance

• Acceptance Criteria: Not explicitly stated in the provided letter. For a clinical chemistry analyzer, acceptance criteria would typically involve demonstrating analytical performance similar to or better than a predicate device across various parameters, including:

  • Accuracy: Agreement with a reference method.
  • Precision (Reproducibility & Repeatability): Consistency of results.
  • Linearity: Accuracy across the analytical measurement range.
  • Detection Limits: Lowest concentration that can be reliably measured.
  • Interference: Lack of significant impact from common interfering substances.
  • Carry-over: Minimal contamination between samples.
  • Stability: Reagent and calibration stability.
  • Correlation: Strong correlation with predicate device or reference method.

• Reported Device Performance: Not explicitly stated in the provided letter. The 510(k) submission would have contained data supporting these performance characteristics, demonstrating that the device meets the established acceptance criteria. The FDA's clearance implies that this evidence was found satisfactory.

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

• Sample Size: Not specified in the provided letter. For a clinical chemistry analyzer, test sets would include a variety of patient samples (normal, abnormal) and spiked samples to assess different analytical aspects.
• Data Provenance: Not specified in the provided letter. Typically, clinical chemistry analyzer validation involves prospective collection of patient samples, often from multiple sites to ensure representativeness, as well as characterization of control materials.

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

• Experts and Qualifications: Not specified in the provided letter. For clinical chemistry analyzers, "ground truth" for analytical performance is typically established through:
  • Reference interval studies: Involving a statistically significant number of healthy individuals.
  • Comparison studies: Against a recognized reference method or a legally marketed predicate device, where the predicate device's results serve as the comparison standard.
  • Control materials and calibrators: With known, certified values.
  • Analytical experts (e.g., clinical chemists, laboratory directors) would be involved in designing and overseeing these studies, and interpreting the results.

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable in the traditional sense for analytical performance of a clinical chemistry analyzer. Adjudication methods (like 2+1, 3+1) are typically used for subjective interpretations, such as image analysis or pathology review, where expert opinion is directly establishing "ground truth." For an automated analyzer, the output is quantitative, and performance is assessed against established analytical standards or comparison methods.

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

• MRMC Study: Not applicable. MRMC studies are used to evaluate human reader performance, often with AI assistance, for tasks involving interpretation (e.g., radiology). The Olympus AU5400 is an automated clinical chemistry analyzer that produces quantitative results, not an AI-assisted diagnostic imaging tool with human interpretation.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

• Standalone Performance: As an automated analyzer, the device's performance is inherently "standalone" in generating the quantitative results. The entire 510(k) submission would be focused on demonstrating this standalone analytical performance. However, there's no "algorithm only without human-in-the-loop" contrast needed, as the device's function is to perform the chemical analysis automatically.

7. The Type of Ground Truth Used

• Ground Truth Type: For a clinical chemistry analyzer, the "ground truth" is typically established through:
  • Reference methods: Highly accurate and validated analytical methods.
  • Certified reference materials/calibrators: Materials with known, traceable analyte concentrations.
  • Comparison to a legally marketed predicate device: Demonstrating equivalent performance to a device already on the market.
  • Pathology/Outcomes data: Would generally not be the primary "ground truth" for the analytical performance of the analyzer itself, though the results generated by the analyzer would be used in conjunction with such data for clinical decision-making.

8. The Sample Size for the Training Set

• Training Set Sample Size: Not applicable in the conventional machine learning sense. This device is a traditional analytical instrument, not a machine learning or AI model that requires a "training set" to learn its function. Its operational parameters are determined by its design, engineering tolerances, and chemical principles, not by training on a dataset.

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

• Ground Truth for Training Set: Not applicable, as there is no "training set" for a traditional clinical chemistry analyzer. The device's calibration involves using calibrator materials with known concentrations, but this is part of routine operation and quality control, not "training" in the ML sense.

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The IMMAGE Immunochemistry System Alpha - Acid Glycoprotein (AAG) Reagent, when used in conjunction with Beckman IMMAGE™ Immunochemistry Systems and Beckman Calibrator 1, is intended for the quantitative determination of human alpha-acid glycoprotein by rate nephelometry.

The IMMAGE Immunochemistry System AAG Reagent in conjunction with Beckman Calibrator 1, is intended for use in the quantitative determination of alpha-acid glycoprotein concentrations respectively in human serum samples on Beckman's IMMAGE Immunochemistry System.

Here's a breakdown of the acceptance criteria and study information for the IMMAGE™ Immunochemistry System Alpha-Acid Glycoprotein (AAG) Reagent, based on the provided text:

Acceptance Criteria and Device Performance

The document doesn't explicitly state "acceptance criteria" with specific thresholds for method comparison metrics (Slope, Intercept, r-value). However, the "Summary of Performance Data" presents the results of these studies, implying these values are deemed acceptable for demonstrating substantial equivalence. The predicate device's performance would serve as the implicit benchmark.

Since explicit acceptance criteria are not provided for the method comparison, I will present the reported performance as fulfilling the implied acceptance for substantial equivalence based on the comparison to the predicate.

Note: The section for "Estimated Within-Run Imprecision" contains garbled text. Therefore, specific acceptance criteria and performance data for this metric cannot be extracted from the provided document.

Study Details

This submission describes the development of a reagent for use on an existing system (IMMAGE™ Immunochemistry System), not a standalone diagnostic device in the sense of an AI algorithm or imaging system. Therefore, some of the requested categories (like number of experts for ground truth, adjudication method, MRMC studies) are not applicable to this type of device and study.

1. Sample Sizes and Data Provenance:

• Test Set Sample Size (Method Comparison): 141 samples
• Data Provenance: Human serum samples. Country of origin not specified. The study appears to be prospective in nature, comparing the new reagent's performance against the existing predicate on new samples.

2. Number of Experts and Qualifications for Ground Truth:

• Not Applicable. This is a quantitative chemical assay. The "ground truth" for the method comparison is the measurement obtained from the established predicate device (Beckman AAG Reagent on Array® 360). There are no "experts" in the sense of clinical decision-makers involved in establishing ground truth for individual samples in this context.

3. Adjudication Method for Test Set:

• Not Applicable. As a quantitative assay comparison, there is no need for adjudication by experts. The comparison is between two quantitative measurements.

4. Multi Reader Multi Case (MRMC) Comparative Effectiveness Study:

• No. This type of study is not applicable to a chemical assay reagent. MRMC studies are typically used for diagnostic imaging or other subjective interpretation tasks where human readers are involved.

5. Standalone Performance Study:

• Yes, in essence. The "Method Comparison Study Results" section directly shows the performance of the IMMAGE AAG Reagent (the "algorithm/device" in this context) in generating quantitative results compared to an established method. This demonstrates the reagent's performance on its own, albeit in comparison to a predicate, not necessarily against a "true" gold standard across all possible ranges.

6. Type of Ground Truth Used:

• Comparison to a Predicate Method. The "ground truth" for the method comparison study was the quantitative results obtained from the Beckman AAG Reagent on Array® 360, which is the well-established predicate device. This is a form of comparative truth rather than an absolute biological truth like pathology or outcomes data.

7. Sample Size for Training Set:

• Not specified. The document does not mention a "training set" in the context of an algorithm. This is a chemical reagent, likely optimized through laboratory development and testing rather than machine learning training.

8. How Ground Truth for Training Set was Established:

• Not Applicable. As there's no specified "training set" in the machine learning sense, this question is not relevant. The reagent development would have involved various analytical chemistry and formulation studies, but not "ground truth" establishment for training an algorithm.

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