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The ADVIA 2120 and ADVIA 2120i with autoslide are quantitative, automated hematology analyzers that provide the following information for in vitro diagnostic use in clinical laboratories:

• A complete blood count (CBC) consisting of WBC, RBC, Hgb, CN-Free Hgb, Calculated Hgb, MCV, Hct, MCH, MCHC, CHCM, RDW, HDW, CH, Plt, MPV.
• A leukocyte differential count consisting of Neut (%/#), Lymph (%/#), Mono (%/#), Eos (%/#) Baso (%/#), LUC (%/#).
• A reticulocyte analysis consisting of Retic (%/#), MCVg, MCVr, CHCMg, CHCMr, CHg, CHr.
• A nucleated red blood cell count consisting of NRBC(%/#).
• Enumeration of the total nucleated cell (TNC) count and RBC count for pleural, peritoneal, and peritoneal dialysis (PD) specimens.
  Note: Above measurands are determined (in whole blood, pleural, peritoneal, or peritoneal dialysis specimens with K2 and/or K3 EDTA anti-coagulants).
• Quantitative determination of blood cells in Cerebrospinal Fluid (CSF) consisting of WBC, RBC, Neut (%/#), Lymph (%/#), Mono (%/#), MN (%/#),PMN (%/#).
  In addition, the system provides the added capability to automatically prepare and stain high quality blood smears on a microscope slide.

The ADVIA 2120/210i Hematology systems with Auto slide are an integrated option of a Hematology analyzers with complete blood cell count, leukocyte differential cell count, reticulocyte analysis capability, nucleated red blood cell count, quantitative determination of blood cells in Cerebrospinal Fluid (CSF), enumeration of the total nucleated cell (TNC) count and RBC count for pleural, peritoneal, and peritoneal dialysis (PD) specimens and a slide stainer designed to provide reflexive slide making/staining without user intervention based upon pre-selected, user-definable criteria.
The ADVIA 2120/210i Hematology systems with Auto slide consists of the following: an analytical module that aspirates, dilutes, and analyzes whole blood samples; an auto sampler that automatically mixes, identifies, and presents the samples for processing; a computer workstation that controls the instrument, provides primary user interface with the instrument and manages the data produced by the instrument; a printer that optionally generates reports based on the instrument results and an auto slide module that prepares a wedge smear from a drop of blood, places it on a microscope slide and stains the slide in accordance with Wright, Wright-Giemsa and May-Grnwald Giemsa Staining techniques.

The provided text is a 510(k) Summary for the ADVIA® 2120/2120i Hematology auto-analyzers. This document focuses on demonstrating substantial equivalence to a predicate device, which means the new device is as safe and effective as a legally marketed device. It does not describe a study that proves the device meets specific acceptance criteria in the way you might expect for a novel AI device with a defined set of performance metrics.

Instead, the submission shows the new device with an ARM9 CPU board performs similarly or identically to the predicate device (the ADVIA 2120/2120i with the current CPU board) across various specifications. The "acceptance criteria" here are essentially the established performance characteristics of the predicate device, and the "study" is the comparison data presented to support substantial equivalence.

Here's an attempt to answer your questions based on the provided text, acknowledging that some information you requested (like AI-specific details, ground truth establishment for a training set, and expert adjudication for a test set) are not relevant or present in this type of FDA submission for a hardware/firmware upgrade to an existing analyzer.

1. A table of acceptance criteria and the reported device performance

The acceptance criteria are implied to be the performance characteristics of the predicate device. The "reported device performance" is the expectation that the new device (AVIA 2120/2120i with ARM9 CPU) will exhibit identical performance.

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

The document lists performance specifications (linearity, precision) but does not specify the sample sizes or data provenance (country, retrospective/prospective) used to establish these predicate device performance characteristics. The context is that the new device's performance is expected to be identical to the established predicate performance, implying that these performance metrics have been previously validated and are being maintained.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g., radiologist with 10 years of experience)

This is not applicable to this submission. The "ground truth" for hematology analyzers is typically established through reference methods, calibrated controls, and comparison to established, validated manual methods, not through expert consensus in the way an imaging AI algorithm's ground truth might be. The document focuses on the technical specifications and equivalence of a hardware component change.

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

Not applicable. This type of submission does not involve adjudication of diagnostic decisions as would be relevant for an AI algorithm.

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 for a hematology auto-analyzer, not an AI-assisted diagnostic tool for human readers.

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

The device itself is a standalone automated hematology analyzer. The submission is not for a new algorithm, but for a hardware (CPU board) and associated software upgrade to an existing analyzer. The performance characteristics presented are those of the entire automated system.

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

Not explicitly stated for the predicate device's original validation. However, for hematology analyzers, "ground truth" for parameters like cell counts, hemoglobin, etc., is typically established using:

• Reference methods: Highly accurate and precise laboratory methods.
• Calibrated materials: Use of control materials with known values traceable to international standards.
• Manual microscopy or other established techniques: For differential counts or specific cell morphology, comparison to manual review by highly trained laboratorians.

8. The sample size for the training set

Not applicable. This device does not use a "training set" in the context of machine learning. It's a change to a pre-programmed analytical instrument.

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

Not applicable, as there is no "training set" in the AI sense.

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The CELL-DYN® 3200 System is a fully automated hematology analyzer intended for in-vitro diagnostic use in the clinical laboratory.

The CELL-DYN® 3200 System has three main modules: the Analyzer, which identifies, mixes, and presents specimens for processing, aspirates, dilutes and analyzes each whole blood specimen. The Data Module, which controls all system processing; and the Display Station, which provides the primary operator interface with the system and generates reports automatically or on demand.

Here is an analysis of the provided information, structured to address your specific questions:

The provided documents do not contain a table of acceptance criteria or reported device performance in a format that directly maps to typical AI/software performance metrics (e.g., sensitivity, specificity, AUC). Instead, the studies described are focused on demonstrating "substantial equivalence" of the CELL-DYN® 3200 System to existing predicate devices for various hematological parameters. This typically involves accuracy, precision, and linearity data compared against manufacturer's specifications and predicate device performance.

Given the nature of the device (a hematology analyzer) and the era of the submission (1997), the evaluation methodology is characteristic of medical devices that provide quantitative measurements rather than AI-driven diagnostic interpretations. Therefore, many of your questions regarding AI-specific assessments (e.g., MRMC studies, human-in-the-loop, training set details) are not directly applicable or answerable from the provided text.

Here's what can be extracted and inferred:

1. Table of Acceptance Criteria and Reported Device Performance

As mentioned, a direct table of acceptance criteria and performance metrics (like sensitivity/specificity) is not present. Instead, the "acceptance criteria" are implied by the requirement to demonstrate substantial equivalence to predicate devices and adherence to "manufacturer's specifications" for accuracy, precision, and linearity for each parameter.

The following table summarizes the parameters for which substantial equivalence was demonstrated and the predicate devices used for comparison, which implicitly define the performance standard (acceptance criteria).

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

• Sample Size: The specific sample size used for the "test set" (i.e., the samples used in the equivalency studies) is not explicitly stated in the provided documents.
• Data Provenance: The documents do not specify the country of origin of the data or whether the studies were retrospective or prospective.

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

• Number of Experts: For WBC differential parameters, the NCCLS H20-A, "Reference Leukocyte Differential Count (Proportional) and Evaluation of Instrumental Methods," Approved Voluntary Standard 1992 was used "to arbitrate differences in the differential." This standard outlines the methodology for establishing a reference leukocyte differential count, which typically involves multiple trained technologists reviewing blood smears. The exact number of experts/technologists used in this specific study and their qualifications are not explicitly stated in the provided text.
• Qualifications of Experts: Not specified beyond the reference to the NCCLS H20-A standard.

4. Adjudication Method

For the white cell differential parameters, the NCCLS H20-A standard was used for arbitration. This standard details a process for establishing a reference differential count, which usually involves a consensus or weighted average from multiple expert reviews of stained blood smears (e.g., 500-cell or 200-cell manual counts). The specific "adjudication method" (e.g., 2+1, 3+1) within that standard, as applied to this study, is not explicitly detailed in the provided text. For other parameters, comparison was made against predicate devices or a manual method (microhematocrit for MCV), implying the predicate device's or manual method's result served as the "ground truth" or reference for comparison, rather than an independent expert adjudication.

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

• No, an MRMC comparative effectiveness study was not done. This type of study is typically associated with evaluating reader performance (e.g., radiologists, pathologists) with and without AI assistance, which is not applicable to a hematology analyzer that provides quantitative measurements. The studies focused on quantitative agreement with predicate devices and reference methods.

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

• Yes, the studies are effectively standalone performance evaluations. The CELL-DYN® 3200 System is an automated analyzer designed to provide results directly. Its performance was evaluated by comparing its outputs (WBC, RBC, HGB, etc.) against established methods (predicate devices, manual microhematocrit, NCCLS standard for differentials). There is no "human-in-the-loop" component described in this context for generating the primary analytical results. The "human-in-the-loop" would be the clinical laboratory professional operating the device and interpreting its output, but the performance testing here is of the device's measurement capabilities themselves.

7. The Type of Ground Truth Used

The ground truth used varied by parameter:

• WBC, RBC, HGB, PLT, MCH, RDW, MPV, all hemogram parameters: The results from the predicate devices (Abbott CELL-DYN® 3500R System, CELL-DYN® 4000 System, Technicon H 3 RTC) served as the primary reference or "ground truth" for demonstrating substantial equivalence. This is a form of comparative measurement against an accepted standard.
• WBC Differential parameters: The NCCLS H20-A "Reference Leukocyte Differential Count" standard was used for arbitration, which implies an expert consensus or reference method defined by the standard.
• MCV: The manual microhematocrit method (NCCLS H7-A2) was used as a reference for comparison, in addition to the CELL-DYN® 4000 System. This is a reference standard method.

8. The Sample Size for the Training Set

• Not Applicable / Not Provided. Hematology analyzers of this type are not typically "trained" in the machine learning sense with a distinct training set. They are designed based on physical principles (flow cytometry, laser optical scatter, colorimetry) and algorithms are developed to process these signals. Any "training" or calibration would be part of the device's engineering and internal validation, not a separate, disclosed "training set" like in modern AI models. The 510(k) summary focuses on the performance of the final, developed device.

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

• Not Applicable / Not Provided. As noted above, the concept of a "training set" with established ground truth as used in contemporary AI/ML is not relevant to this type of device and submission. The "ground truth" in development would likely refer to highly characterized reference materials and established analytical methods used during the engineering and validation phases of the device.

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