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The K-ASSAY Haptoglobin Assay is intended to be used for the quantitative determination of Haptoglobin (a protein that binds hemoglobin, the oxygen-carrying pigment in red blood cells) in human serum by immunoturbidimetric assay. Measurement of haptoglobin may aid in the diagnosis of hemolytic diseases (diseases in which the red blood cells rupture and release hemoglobin) relating to the formation of hemoglobin complexes and certain kidney diseases. FOR IN VITRO DIAGNOSTIC USE.

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AI/ML Overview

Here's an analysis based on the provided FDA 510(k) clearance letter and Indications for Use statement for the K-ASSAY Haptoglobin device.

It's important to note that this document is an FDA clearance letter, which means it confirms substantial equivalence to a predicate device. It typically does not contain the full details of the study that established the device's performance against specific acceptance criteria in the way a clinical trial report or a comprehensive validation study might. Many of the requested details (like sample size, ground truth establishment, expert qualifications, etc., for AI performance) are not applicable or not present here because this is for an In Vitro Diagnostic (IVD) assay, not an AI/ML-based diagnostic device.

However, I will extract what is relevant and indicate where information is not available from the provided text.

Device: K-ASSAY Haptoglobin

Device Type: In Vitro Diagnostic (IVD) assay (immunoturbidimetric assay)

Intended Use: Quantitative determination of Haptoglobin in human serum to aid in the diagnosis of hemolytic diseases and certain kidney diseases.

1. Table of Acceptance Criteria and Reported Device Performance

The provided document does not explicitly state specific acceptance criteria or detailed performance metrics from a validation study. For IVD devices like this one, acceptance criteria would typically involve analytical performance characteristics (e.g., precision, accuracy, linearity, reportable range, interference) and potentially clinical performance (e.g., correlation with clinical status or another accepted method).

The FDA's review for a 510(k) involves determining substantial equivalence to a legally marketed predicate device. This implies that the sponsor demonstrated the new device performs "as safely and effectively as" a predicate, but the specific numerical benchmarks of that comparison are not in this letter.

Performance Metric (Hypothetical for IVD)	Acceptance Criteria (Not specified in document)	Reported Device Performance (Not specified in document)
Analytical Performance
Precision (e.g., CV%)	e.g., ≤ X%	Not stated
Accuracy (e.g., % bias)	e.g., within Y% of reference method	Not stated
Linearity Range	e.g., A - B mg/dL	Not stated
Limit of Detection (LoD)	e.g., ≤ C mg/dL	Not stated
Clinical Performance
Correlation with predicate device	e.g., r ≥ 0.95	Implied to be sufficient for substantial equivalence

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

Not explicitly stated in the provided document.
For IVD assays, test sets would include patient samples (serum in this case) covering the expected analytical range and potentially clinical conditions.
Data Provenance: Not specified. Typically, clinical samples might be from various clinical sites.
Retrospective or Prospective: Not specified. Could be either or a combination.

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

Not applicable in the context of this being an IVD assay clearance.
Ground truth for an IVD assay usually refers to the true concentration of the analyte, often established by a gold standard reference method or laboratory. It doesn't typically involve multiple human experts interpreting data for a diagnostic decision.

4. Adjudication method for the test set

Not applicable. As above, this is for an IVD assay, not a device requiring human interpretation 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 an IVD assay, not an AI-assisted diagnostic device. Therefore, no human reader improvement with AI assistance would be studied or reported here.

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

Not applicable. This is an IVD assay that quantifies a biomarker. Its "performance" is its analytical accuracy and precision in measuring haptoglobin, not an algorithmic interpretation of an image or other complex data.

7. The type of ground truth used

Implied ground truth: For an IVD assay, the ground truth would be the true concentration of Haptoglobin in a sample. This is typically established by:
- Reference materials/calibrators: Samples with precisely known concentrations.
- Comparison methods: Results from a well-established, often predicate or gold-standard, analytical method.
- Clinical correlation: Demonstrating that the measured Haptoglobin levels correlate with the presence or absence of the intended clinical conditions (hemolytic diseases, kidney diseases).
Not explicitly detailed in the document.

8. The sample size for the training set

Not applicable in the AI/ML sense. For an IVD assay, there isn't a "training set" in the context of machine learning. There would be samples used during assay development and optimization (which might be analogous to "training data" in a very broad sense) and then independent validation samples. The size of these development samples is not specified.

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

Not applicable in the AI/ML sense. As above, the concept of "ground truth for a training set" as it pertains to AI/ML models does not directly apply to the development of this type of immunoturbidimetric IVD assay. The accuracy of the assay is established through analytical validation against known concentrations and comparison with established methods.

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