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The Diatron Abacus 3CP System is a quantitative multi-parameter automated hematology analyzer designed for in-vitro-diagnostic use in clinical laboratories for enumeration of the following parameters: WBC, LYM%, LYM#, MID%, MID#, GRA%, GRA#, RBC, HGB, HCT, MCV, MCH, MCHC, RDW, PLT, MPV in K3EDTA anti-coagulated venous whole blood samples. The Diatron Abacus 3CP is indicated for use to identify patients with hematologic parameters within and outside of established reference ranges.

The 'Abacus 3CP' is a fully automated, bench top hematology cell counter with a cap piercing function. It uses the impedance-method for counting cells passing through a small aperture, and measures the hemoglobin content of red blood cells using a photometric method. The analyzer features a color graphical LCD display module and a foil keypad of 29 keys including 6 software buttons, 6 function keys and a START button. The instrument allows printing reports to an external printer (USB port), or can have an optional built-in printer module. Its internal memory is capable of storing 1000 records with full histograms, and individual patient data. The QC measurements are stored in a separate database. The software operating the instrument can be updated by using a USB flash memory device. The instrument can be connected to a host computer for uploading records in its memory through a USB SLAVE port (USB B) or serial link (RS232). Archiving records to an USB flash memory device is also possible.

The provided text describes the 510(k) summary for the Diatron Abacus 3CP Automated Hematology Analyzer (K111534). It compares its performance to a predicate device, the Abbott CELL-DYN® 1800 (K030513), to demonstrate substantial equivalence.

Here's an analysis of the acceptance criteria and study information based on the provided document:

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

The document does not explicitly state quantitative acceptance criteria in a table format. Instead, it relies on demonstrating equivalence to the predicate device. The performance section states:

"All performance and accuracy data and data analysis in this submission support and substantiate equivalence to the selected predicate device (Abbott CELL-DYN® 1800 (K030513))."

This implies that the acceptance criteria are met if the device's performance is comparable to that of the legally marketed predicate device. The detailed performance data, which would show the "reported device performance" and how it compares to the predicate, is not included in this summary. It would likely be in the full submission. The key performance metrics mentioned are the ability to accurately enumerate the specified hematology parameters.

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

The document does not specify the sample size used for the test set or the data provenance (country of origin, retrospective/prospective). It generally states that "a representative sample of the device underwent software and system verification and validation testing."

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)

The document does not provide any information regarding the number or qualifications of experts used to establish the ground truth for the test set. For hematology analyzers, the ground truth is typically established through reference methods or manual microscopy performed by trained laboratory professionals (medical technologists, hematologists).

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

The document does not describe any specific adjudication method for the test set.

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

A multi-reader multi-case (MRMC) comparative effectiveness study was not done. This device is an automated hematology analyzer, directly measuring parameters, and does not involve human readers interpreting images with or without AI assistance. It's a standalone diagnostic device.

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

Yes, a standalone study was implicitly done. The "Abacus 3CP, Automated Hematology Analyzer" is designed for "in-vitro-diagnostic use in clinical laboratories for enumeration of the following parameters". This indicates that the device operates automatically without human intervention in the primary measurement and calculation of parameters. Its performance is assessed as a standalone system.

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

While not explicitly stated, for hematology analyzers, the ground truth is typically established by:

• Reference methods: Highly accurate and precise laboratory methods (e.g., manual cell counts, flow cytometry for cell differentials) are used as the gold standard to which the automated analyzer's results are compared.
• Manual microscopy (expert review): For certain parameters, especially cell differentials or identification of abnormal cells, expert microscopic review by trained medical technologists or hematologists serves as the ground truth.

8. The sample size for the training set

The document does not mention a "training set" or its sample size. This is typical for a device like an automated hematology analyzer, where the algorithms (methodologies for calculating parameters like impedance counting and photometric measurement) are based on established scientific principles rather than being "trained" on a dataset in the modern machine learning sense. The device's performance is validated through verification and validation testing, not through training data.

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

As there's no mention of a "training set" in the context of machine learning, there's no information on how its ground truth would have been established. The core methodologies of the device (impedance, photometry, calculations) are deterministic and based on physical principles, not a learned model.

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