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The CELL-DYN® 4000 System is a fully automated hematology analyzer intended for in-vitro diagnostic use in the clinical hematology laboratory of a hospital, medical clinic, or reference laboratory.

The CELL-DYN® 4000 System has five main modules: the Analyzer, which aspirates, dilutes and analyzes each whole blood specimen; the Autoloader, which automatically identifies, mixes, and presents specimens for processing; the Pneumatic Unit, which controls fluid movement in the Analyzer and tube movement in the Autoloader; the Data Station, which controls all system processing and provides the primary operator interface with the system; and the Color Printer, which generates reports automatically or on demand.

The provided text describes the CELL-DYN® 4000 System and its substantial equivalence to other existing hematology analyzers. However, it does not contain a specific study proving the device meets acceptance criteria in the format typically expected. Instead, it makes claims about "accuracy, precision, and linearity" demonstrating performance to "manufacturer's specifications" and supports substantial equivalence to predicate devices.

Therefore, many of the requested fields cannot be directly extracted from the provided text. I will fill in what can be inferred or explicitly stated.

Acceptance Criteria and Reported Device Performance

The document states that the "accuracy, precision, and linearity data shows performance to manufacturer's specifications." However, the specific manufacturer's specifications (acceptance criteria) for these parameters are not explicitly provided in the text. The text primarily focuses on demonstrating substantial equivalence to predicate devices rather than listing detailed performance metrics against predefined acceptance thresholds.

What is explicit is the claim of substantial equivalence to:

• Abbott CELL-DYN 3500 System for hemogram and white cell (WBC) differential parameters.
• Becton Dickinson FACScan™ Flow Cytometer ReticCOUNT™ Reticulocyte Enumeration Software for reticulocytes.
• Manual microscopic WBC differential count for enumeration of nucleated red blood cells (NRBCs).

Without the specific manufacturer's specifications, a table of acceptance criteria and reported performance cannot be fully constructed.

Study Details

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

   • The document mentions "The data complied to support the claim... includes accuracy, precision, linearity, and carryover." However, specific sample sizes for these tests are not provided.
   • The data provenance (country of origin, retrospective/prospective) is not specified in the provided text.

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

   • For NRBC enumeration, equivalence is demonstrated by comparison to the "manual microscopic WBC differential count." This usually implies human expert review, but the number of experts and their qualifications are not specified.
   • For other parameters, the ground truth seems to be established by comparison to predicate devices, not typically by human experts as a primary ground truth.

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

   • Not specified.

4. 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:

   • No MRMC study was mentioned. This device is an automated hematology analyzer, not an AI-assisted diagnostic tool for human readers.

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

   • Yes, the entire submission describes the standalone performance of the CELL-DYN® 4000 System, which is a fully automated hematology analyzer. Its performance is measured against predicate devices and manufacturer's specifications.

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

   • The ground truth for demonstrating substantial equivalence is primarily based on:
     • Measurements from predicate devices: Abbott CELL-DYN 3500 System and Becton Dickinson FACScan.
     • Manual microscopic WBC differential count for NRBCs.
     • Manufacturer's internal specifications for accuracy, precision, and linearity.

7. The sample size for the training set:

   • This being a 510(k) submission for a medical device (analyzer), rather than a machine learning algorithm, the concept of a "training set" in the context of AI is not (and would not be) explicitly discussed. The device's operation is based on established principles of flow cytometry rather than a trained AI model. No training set information is provided.

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

   • As there is no mention of a "training set" in the context of an AI/ML algorithm, this question is not applicable based on the provided text.

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