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The ADVIA® 2120 Nucleated Red Blood Cell (nRBC) method is intended to provide an in vitro diagnostic, quantitative determination of nucleated red blood cells in peripheral whole blood.

The technological and methodological principles of the ADVIA 2120 with Autoslide used for the quantitative measurement of blood cells in whole blood specimens will be used without change except for the addition of a new methodology to quantitate nucleated red blood cells and correct for white blood cells in peripheral whole blood.

The ADVIA 2120 Nucleated Red Blood Cell (nRBC) method is described below:
a) The ADVIA 2120 nRBC Analysis method uses histogram analysis routines to analyze the unstained region of the Peroxidase channel as well as an arithmetic algorithm that combines counts from the Peroxidase and Basophil/Lobularity channels to enumerate nRBCs.
b) The analysis corrects the White Blood Cell Count for the presence of nRBCs, as well as the WBC Differential. It reports the corrected WBC count and the corrected differential.

The new nRBC method will report the following Red Blood Cell Parameters-

1. % nucleated Red Blood Cells (% nRBC)
2. absolute nRBC (# of nRBC per microliter)

The provided text describes the ADVIA® 2120 with Autoslide and NRBC Method. However, it does not include detailed acceptance criteria or a study proving the device meets specific performance metrics in the format requested.

The text focuses on explaining the new functionality (nRBC analysis) and its technological characteristics compared to the predicate device. It also includes the FDA's 510(k) clearance letter, which confirms substantial equivalence but does not detail the underlying performance studies or acceptance criteria that led to that determination.

Therefore, I cannot provide the requested table or answer most of the questions because the information is not present in the provided document.

Here's what can be extracted based on the limited information:

1. Table of Acceptance Criteria and Reported Device Performance:

• Acceptance Criteria: Not explicitly stated in the document.
• Reported Device Performance:
  • Reports "% nucleated Red Blood Cells (% nRBC)"
  • Reports "absolute nRBC (# of nRBC per microliter)"
  • Reports nRBC counts for whole blood samples with either 200 or more nRBC/uL, or with at least 2% nRBCs with a WBC count of at least 3,000/ uL.
  • Corrects the White Blood Cell Count for the presence of nRBCs.
  • Recalculates the WBC Differential, and recalculates %MN and %PMN.

2. Sample size used for the test set and the data provenance: Not provided.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not provided.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not provided.

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 a standalone automated analyzer, not an AI-assisted reader system.

6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done: Yes, the device is described as an "Automated Hematology Analyzer," indicating standalone performance. The document states, "The ADVIA 2120 nRBC method uses histogram analysis routines... as well as an arithmetic algorithm that combines counts... to enumerate nRBCs." This describes the algorithm's standalone operation.

7. The type of ground truth used: The text mentions "nRBC counts are performed by manual methods that are well established as reference methods in clinical laboratories and consistent with NCCLS H-20A." This suggests that manual microscopy (per NCCLS H-20A guidelines) would have been used as the ground truth for validation, likely expert consensus through manual review.

8. The sample size for the training set: Not provided.

9. How the ground truth for the training set was established: Not explicitly stated, but likely through manual methods consistent with NCCLS H-20A, as referenced for "reference methods."

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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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