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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.

• RBC (10⁶/μL): 0.0 to 7.0 (Max Deviation: 0.1)
• HGB (g/dL): 0 to 22.5 (Max Deviation: 0.2 or 2.0%)
• PLT (10³/μL): 5.0 to 3500 (Max Deviation: 5.0 or 5.0%)
• %RETIC: 0.2 to 24.5 (Max Deviation: 5.0%)
• CN-free HGB (g/dL): 1 to 22.5 (Max Deviation: 0.3 or 3.0%)
• CSF WBC (cells/µL): 0 to 50 (Max Deviation: 5)
• CSF RBC (cells/µL): 50 to 5000 (Max Deviation: 10%)
• BF TNC (10³/µL): 0 to 50 (Max Deviation: 5)
• BF RBC (10⁶/µL): 50 to 1500 (Max Deviation: 10%) | Same |
  | Within-Run Precision| (See detailed table in original text; e.g., WBC: Nominal 7.5, SD 0.2, CV 2.66%) | Same |
  | Carryover | Less than or equal to 1% | Same |
  | Physical/Electrical | (Various detailed specifications for Temperature, Humidity, Noise, Weight, Dimensions, Vacuum/Pressure, Reaction Chamber Temp, Power Pack Temp, Light Intensities, Power Supply Voltages, Sample Mode Volumes, Throughput, Sample Capacity, Tube Sizes/Types, Barcode Reader functionality) | Same |
  | Data Management | TDC version 9 or higher, Database storage, Review/edit, User-defined reports/ranges, Bi-directional communication, QC features, ILQC programs, User assistance | Same |
  | Consumables/Reagents| CBC TIMEPAC Baso HGB RBC/PLT Defoamer CN-Free CBC TIMEPAC; DIFF TIMEPAC, Perox 1, 2, 3, Perox Sheath, autoRetic, EZ KLEEN, Sheath/Rinse, CSF | Same |
  | Calibrators | ADVIA OPTIpoint, ADVIA SETpoint | Same |
  | Controls | ADVIA TESTpoint Low/Normal/High, Retic Low/High, 3-in-1 Abnormal1/Normal/Abnormal2 | Same |

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 Bayer ADVIA 2120 Hematology System with Autoslide is a quantitative, automated hematology analyzer that provides a complete blood cell count, leukocyte differential count and reticulocyte analysis for in vitro diagnostic use in clinical laboratories. In addition, the system provides the added capability to automatically prepare and stain high-quality blood smears on a microscope slide.

The ADVIA 2120 Hematology system with Autoslide is an integrated option of a Hematology analyzer with complete blood cell count, leukocyte differential cell count, reticulocyte analysis capability and a slide stainer designed to provide reflexive slide making/staining without user intervention based upon pre-selected, user-definable criteria.

The ADVIA 2120 Hematology system with Autoslide consists of the following: an analytical module that aspirates, dilutes, and analyzes whole blood samples; an autosampler 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 autoslide 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-Grunwald Giemsa Staining techniques.

The provided text does not contain specific acceptance criteria or a detailed study description for the ADVIA® 2120 with Autoslide. Instead, it focuses on demonstrating that the device is substantially equivalent to a predicate device (ADVIA 120 Hematology Analyzer) through a 510(k) submission.

The core argument for substantial equivalence is that the ADVIA 2120 with Autoslide performs the same functions (hematological analysis and automated slide preparation/staining) using similar technological characteristics as the predicate device, with the added functionality of the Autoslide module.

Therefore, many of the requested details about acceptance criteria, performance metrics, sample sizes, and expert adjudication are not present in this document. The 510(k) process for substantial equivalence often relies on demonstrating that the new device performs similarly to a legally marketed predicate device, rather than requiring extensive new performance studies against predefined acceptance criteria for every parameter.

However, I can extract information related to the device's capabilities and the nature of the submission.

Here's a breakdown of what can be inferred and what is not available from the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

Not explicitly provided. The document states that the new device is "substantially equivalent" to the predicate device. This implies that its performance is considered comparable to the predicate, which would have had its own performance data established. The exact acceptance criteria used to deem it substantially equivalent are not detailed.

Sufficient information is not provided to complete this table.

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

Not explicitly provided. The document does not describe specific test sets, sample sizes, or data provenance used for performance evaluation of the ADVIA 2120 with Autoslide itself. The focus is on demonstrating technological similarity to the predicate device.

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

Not applicable/provided. Since no specific performance study against a defined ground truth is detailed for this 510(k) submission, there is no mention of experts or their qualifications for establishing ground truth.

4. Adjudication Method for the Test Set

Not applicable/provided. As no detailed test set or ground truth establishment is described, an adjudication method is not mentioned.

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

No. The document does not describe an MRMC comparative effectiveness study where human readers' performance with and without AI assistance is evaluated. This device is an automated hematology analyzer and slide maker/stainer, not an AI-assisted diagnostic tool for human readers in the typical sense of an MRMC study.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

Implicitly yes, in the context of the predicate device. The ADVIA 2120 with Autoslide is an automated system. Its performance, like its predicate (ADVIA 120), is inherent to the algorithms and hardware that perform the blood analysis and slide preparation. While not explicitly called a "standalone algorithm performance study" in modern AI terms, the device's measurements and slide quality are its standalone outputs. The 510(k) relies on the predicate device's established standalone performance and demonstrates that the new device performs similarly.

7. The Type of Ground Truth Used

Not explicitly described for this submission. For diagnostic devices like hematology analyzers, typical ground truth for analytical performance includes:

• Reference Methods: Highly accurate and validated laboratory methods for measuring specific analytes (e.g., manual differential counts, reference hemoglobinometry).
• Patient Samples: Using samples with known clinical conditions.
• Control Materials: Calibrated reference materials and controls.

Given the nature of a 510(k) for substantial equivalence, the ground truth would have been established for the predicate device, and the current submission argues the new device operates on the same (or similar) principles to achieve comparable results.

8. The Sample Size for the Training Set

Not applicable/provided. The document does not discuss a training set in the context of machine learning or AI algorithm development. This submission is for a medical device (hematology analyzer and automated slide stainer) that uses established analytical principles (laser light scatter, absorption, impedance, chemical reactions), not a machine learning model that requires a "training set."

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

Not applicable/provided. As there is no "training set" described for an AI algorithm, this information is not relevant to the provided document.

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