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The DiffMaster Octavia™ is an automated cell locating device. DiffMaster Octavia automatically locates and presents images of blood cells on peripheral blood specimens. The operator identifies and verifies the suggested classification of each cell according to type. DiffMaster Octavia is intended to be used by skilled operators, trained in the use of the instrument and in recognition of leukocyte classes.

The DiffMaster-Octavia™ is an automated cell locating device for differential count of white blood cells and characterization of red morphology. It is based on a CellaVision AB developed software system Cytologica. DiffMaster Octavia™consists of a commercially available positioning system for the slides, a commercially available microscope, a commercially available camera and the software system.

Here's a breakdown of the acceptance criteria and study information for the DiffMaster Octavia™ Automatic Hematology Analyzer, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The submission does not explicitly list acceptance criteria in a table format with specific thresholds. Instead, it states that the device demonstrated equivalence to the predicate method. The "results equal to the reference method" is the primary performance claim used for substantial equivalence.

2. Sample Size and Data Provenance for the Test Set

• Sample Size: The document does not specify the exact sample size (number of cases or cells) used in the clinical trials. It merely states "Two clinical trials have been performed."
• Data Provenance: Not explicitly stated. The document indicates the studies "have been performed according to the approved standard, NCCLS, vol. 23, no 1, document H-20A, March 1992: Reference Leukocyte Differential Count (proportional) and Evaluation of Instrumental Method." It does not mention the country of origin or if the data was retrospective or prospective.

3. Number of Experts and Qualifications for Ground Truth Establishment (Test Set)

• Number of Experts: Not specified.
• Qualifications of Experts: The ground truth was established by the "manual reference method results," implying experienced laboratory technologists. The description of the intended use states the device is "intended to be used by skilled operators, trained in the use of the instrument and in recognition of leukocyte classes," which suggests the benchmark manual method would also be performed by similarly qualified individuals.

4. Adjudication Method for the Test Set

• The document does not describe a specific adjudication method (e.g., 2+1, 3+1). The "manual reference method" implies a standard, accepted process for manual differential counting, which typically involves a single trained operator's assessment or potentially a consensus if discrepancies arise in real-world lab settings, but this is not detailed in the provided text.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• No, a MRMC comparative effectiveness study, as typically understood for AI-assisted workflows, was not explicitly mentioned. The studies compared the device's suggested classifications (which are then verified by a human) against the manual reference method. This is a comparison of the automated system's output (with human verification) against the traditional human-only method, rather than specifically measuring human reader improvement with AI assistance versus without AI assistance. The device's intended use is for human operators to verify/modify the AI's suggestions, so it inherently involves human-in-the-loop.
• Effect Size of Human Improvement with AI vs. without AI: Not reported, as this type of study was not described.

6. Standalone (Algorithm Only) Performance Study

• Not explicitly described as a standalone study in the traditional sense. The "accuracy of the suggested classification for each cell type (DiffMaster Octavia™ results compared to the light microscope manual diff count results)" implies that the algorithm's initial suggestion was compared to the ground truth. However, the device's intended use is always with a human verifying these suggestions. Therefore, while the algorithm's raw suggestion accuracy was likely evaluated internally, the reported clinical performance encompasses the complete human-in-the-loop workflow.

7. Type of Ground Truth Used

• Expert Consensus / Expert Reference Method: The ground truth was established by the "manual reference method results," which means the classifications assigned by skilled human operators using the traditional Romanowski (MGG)-Stain light microscopic process for cell classification.

8. Sample Size for the Training Set

• Not provided. The document states that the software uses "deterministic artificial neural networks (ANN's) trained to distinguish between classes of white blood cells," but does not specify the size of the dataset used for this training.

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

• Not explicitly stated for the training set ground truth. Similar to the test set, it can be inferred that the training data would have also been labeled based on expert classification (e.g., manual differential counts by skilled technologists) to train the artificial neural networks on correct cell classifications.

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