(137 days)
The Cell-Dyn 4000 System with IRF is a multi-parameter, hematology analyzer designed for In-Vitro diagnostic use in clinical laboratories.
The Cell-Dyn 4000 System with Immature Reticulocyte Fraction is a fully automated hematology analyzer, including reporting of the Immature Reticulocyte Fraction, 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 Cell-Dyn 4000 System with IRF is designed to analyze EDTA-anticoagulated whole blood specimen and report the hematological parameters shown in the table on the following page.
The provided text is a 510(k) summary for the CELL-DYN®4000 Multi-Parameter Automated Hematology Analyzer with Immature Reticulocyte Fraction (IRF). It outlines the device's intended use, description, principles of operation, and a comparison to similar predicate devices to establish substantial equivalence.
Based on the information provided, here's a breakdown of the acceptance criteria and the study that proves the device meets them:
1. Table of Acceptance Criteria and Reported Device Performance:
The document states: "The accuracy, precision, and linearity data shows performance to manufacturer's specifications." However, specific numerical acceptance criteria or detailed performance data (e.g., accuracy percentages, precision coefficients of variation, linearity ranges) are not provided in the text. The document broadly asserts that the device meets "manufacturer's specifications" without detailing what those specifications are.
Therefore, a table of specific acceptance criteria and reported performance cannot be fully constructed from the provided text. The text only makes a general statement of compliance.
2. Sample Size Used for the Test Set and Data Provenance:
The document does not explicitly state the sample size used for the test set or the data provenance (e.g., country of origin, retrospective or prospective nature of the samples). It generally refers to "data compiled to support the claim," but lacks these specific details.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications:
The document mentions a "manual reticulocyte method" and "manual microscopic differential" as comparison methods. However, it does not specify the number of experts used to establish ground truth for the test set or their qualifications.
4. Adjudication Method for the Test Set:
The document mentions that the Cell-Dyn® 4000 System with IRF "counts RBCs and PLTs by both the optical and impedance methods and compares the data as an internal quality check." This suggests an internal comparison mechanism for these parameters. For other parameters, it refers to comparison with methods from predicate devices. However, it does not describe an explicit adjudication method (e.g., 2+1, 3+1) involving human experts for establishing ground truth for the test set.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:
No MRMC comparative effectiveness study involving human readers with and without AI assistance is described or implied in the document. The device is an automated hematology analyzer, not an AI-assisted diagnostic tool designed to integrate with human reader interpretation in the context of the studies described. The "AI" would be the algorithms within the analyzer itself.
6. Standalone (Algorithm Only Without Human-in-the-Loop Performance):
Yes, the studies described are essentially standalone performance studies. The document describes the device's ability to analyze blood specimens and report various hematological parameters automatically. The "equivalency data" (accuracy, precision, linearity, carryover) is intrinsic to the device's performance as an automated system without human intervention in the interpretation flow. The comparisons are against other automated or manual laboratory methods, not against human readers making diagnoses with or without AI assistance.
7. Type of Ground Truth Used:
The ground truth for the studies described appears to be established by comparison with established laboratory methods and predicate devices. This includes:
- Existing Automated Hematology Analyzers: Coulter® ZBI, Coulter, Hemoglobinometer, Coulter Model S, Coulter Model S Plus Series, Coulter STKS, Cell-Dyn®4000 System, Cell-Dyn®3500R, Technicon H*1™ Series; Becton Dickinson FACScan™™, Sysmex™ R-3000.
- Manual Methods: The "manual reticulocyte method" and "manual microscopic differential."
Therefore, the ground truth is primarily based on comparative analytics against existing, accepted laboratory methods and devices.
8. Sample Size for the Training Set:
The document does not specify a sample size for the training set. It describes the "equivalency data" compiled to support the substantial equivalence claim, which is typically the testing phase rather than the training phase of an algorithm's development. Given the technology used (flow cytometry, impedance, spectrophotometry), it's more about calibration and validation against known standards and methods rather than a machine learning "training set" in the modern sense.
9. How the Ground Truth for the Training Set Was Established:
As mentioned, the concept of a "training set" in the context of modern machine learning isn't directly applicable here. The device uses established physical and chemical principles (laser optical scatter, fluorescence, impedance, absorption spectrophotometry) to measure and classify cells. The "ground truth" for the development and calibration of such a device would historically come from:
- Known control materials: Samples with established values for parameters.
- Reference methods: Performing analyses using gold-standard manual or highly accurate methods on a subset of samples to calibrate the automated device.
- Clinical correlation: Ensuring that the measurable parameters correlate with known clinical conditions.
The document states the device performs "to manufacturer's specifications," implying that the device was developed and calibrated against internal or industry-standard benchmarks. Again, specific details on how ground truth was established during the development/training (calibration) phase are not provided.
§ 864.5220 Automated differential cell counter.
(a)
Identification. An automated differential cell counter is a device used to identify one or more of the formed elements of the blood. The device may also have the capability to flag, count, or classify immature or abnormal hematopoietic cells of the blood, bone marrow, or other body fluids. These devices may combine an electronic particle counting method, optical method, or a flow cytometric method utilizing monoclonal CD (cluster designation) markers. The device includes accessory CD markers.(b)
Classification. Class II (special controls). The special control for this device is the FDA document entitled “Class II Special Controls Guidance Document: Premarket Notifications for Automated Differential Cell Counters for Immature or Abnormal Blood Cells; Final Guidance for Industry and FDA.”