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The BD FACSCanto System with BD FACSDiva software is intended for use as an In Vitro Diagnostic device for identification and enumeration of lymphocyte subsets in human cells in suspension using a lyse wash sample preparation method for flow cytometry.

Immunophenotyping in clinical laboratories, using previously cleared IVD assays for flow cytometry that utilize the lyse wash sample preparation method.
Immunophenotyping of lymphocyte subsets including CD3CD8, CD3 CD4, CD3 CD16* and/or CD56*, CD3 -CD19*, and CD3*.

The BD FACSCanto System with BD FACSDiva software is comprised of a flow cytometer, a wet cart, and a computer. The wet cart contains operational fluids, the flow cytometer acquires and analyzes the sample, and the computer displays and prints the analysis. The flow cytometer utilizes three sub-systems; fluidics, optics and electronics. It contains one software package for manual immunophenotyping and is compatible with the BD FACSLoader for automatic sample introduction.

Here's an analysis of the provided text regarding the BD FACSCanto System with BD FACSDiva software, focusing on the requested acceptance criteria and study information:

The provided document is a 510(k) summary for the BD FACSCanto System with BD FACSDiva software. It's important to note that a 510(k) submission generally aims to demonstrate substantial equivalence to a predicate device, rather than proving that a device meets specific, pre-defined acceptance criteria in the same way a novel device might with a comprehensive clinical trial. The performance data presented here are primarily to support this claim of substantial equivalence.

Based on the document, here's a breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state numerical "acceptance criteria" for each performance metric in the way a clinical study protocol might. Instead, it refers to industry guidelines and a comparative assessment against a predicate device. The "acceptance criteria" are implied by the statement that the new device "demonstrated comparable accuracy relative to the predicate" or "demonstrated acceptable system precision/carryover/linearity."

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

• Sample Size: The document does not specify the sample size used for any of the performance studies (Accuracy, Precision, Carryover, Linearity).
• Data Provenance: The document does not specify the country of origin of the data or whether the data was retrospective or prospective. The studies are cited as being based on NCCLS (now CLSI) documents, which are general guidelines for laboratory methods, not specific to a data set.

3. Number of Experts Used to Establish Ground Truth and Qualifications

The document does not mention the use of experts or the establishment of a ground truth through expert review for any of the performance studies. These types of studies (accuracy, precision, carryover, linearity for a flow cytometer) typically rely on quantitative measurements against known standards or reference methods rather than expert consensus on individual cases.

4. Adjudication Method for the Test Set

Not applicable, as no expert review or adjudication process is mentioned or implied by the type of studies conducted.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

No, an MRMC comparative effectiveness study was not done. The studies listed are technical performance evaluations (accuracy, precision, carryover, linearity) of the instrument itself, not studies involving human readers or AI assistance. The document is for a flow cytometer system, not an image interpretation or diagnostic AI device in the context of human readers.

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

This question is not directly applicable in the context of this device. The BD FACSCanto System with BD FACSDiva software is an automated differential cell counter (flow cytometer) that generates quantitative data. While it contains software ("algorithm"), its performance is evaluated in terms of instrument accuracy, precision, linearity, and carryover, rather than a standalone diagnostic "algorithm" that interprets images or makes diagnoses without human interaction in the clinical workflow. Its function is to count and identify cells, which is then used by a human operator for diagnostic purposes.

7. The Type of Ground Truth Used

For the studies mentioned:

• Accuracy: Implied ground truth would be a reference method or predicate device performance, adhering to NCCLS guidance.
• Precision, Carryover, Linearity: Implied ground truth would be quantitative measurements against internal controls, reference materials, or known dilutions, assessed according to NCCLS and FDA guidance documents for these specific performance characteristics of an automated counter. The device's output (cell counts, percentages) is compared against expected values or its own repeatability/reproducibility.

8. The Sample Size for the Training Set

The document does not mention a training set or its sample size. This is a 510(k) submission for a medical device (flow cytometer system) with associated software, not a machine learning model that typically goes through a distinct training phase on a data set. The software likely contains algorithms and predefined parameters, but these are developed through engineering and design processes, not typically "trained" on a large dataset in the way a modern AI model would be before its performance is evaluated.

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

Not applicable, as no training set (in the context of machine learning) is mentioned or implied. The functional specifications and performance characteristics are likely established through engineering specifications and validation against reference methods during device development.

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