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The CellaVision DM1200 with the Advanced RBC Application is an automated cell-locating device, intended for in-vitro diagnostic use.

The CellaVision DM1200 with the Advanced RBC Application automatically locates and presents images of blood cells on peripheral blood smears. The operator identifies and verifies the suggested classification of each cell according to type.

The CellaVision DM1200 with the Advanced RBC Application is intended for blood samples that have been flagged as abnormal by an automated cell counter.

The CellaVision DM1200 with the Advanced RBC Application is intended to be used by skilled operators, trained in the use of the device and in recognition of blood cells.

The CellaVision DM96 with the Advanced RBC Application is an automated cell-locating device, intended for in-vitro diagnostic use.

The CellaVision DM96 with the Advanced RBC Application automatically locates and presents images of blood cells on peripheral blood smears. The operator identifies and verifies the suggested classification of each cell according to type.

The CellaVision DM96 with the Advanced RBC Application is intended for blood samples that have been flagged as abnormal by an automated cell counter.

The CellaVision DM96 with the Advanced RBC Application is intended to be used by skilled operators, trained in the use of the device and in recognition of blood cells.

The Advanced RBC Application is substantially equivalent to the RBC functionality included in the predicate DM Systems. It pre-characterizes the morphology of the red blood cells in a sample based on abnormal color, size, and shape (Poikilocytosis). In addition to that, the Advanced RBC Application also pre-characterizes based on different types of Poikilocytosis and on the presence of certain inclusions.

The DM Systems display the result of the RBC pre-characterization as the percentage of abnormal cells for each morphological characteristic and as an automatically calculated grade (0 - normal through 3 - marked), corresponding to that percentage. It also displays an overview image of the RBC monolayer. The difference between the current RBC functionality and Advanced RBC Application is the analysis technique, which enables the Advanced RBC Application to pre-characterize RBC into 21 morphological characteristics as opposed to the current RBC functionality with 6 morphological characteristics. The cell images are pre-characterized into different groups of morphological characteristics based on size, color, shape and inclusion using segmentation, feature calculation and the deterministic artificial neural networks (ANNs) trained to distinquish between morphology characteristics of red blood cells.

Another difference is that the red blood cells, pre-characterized by the Advanced RBC Application, can be displayed both in an overview and in individual images on the screen, while the current RBC functionality displays the pre-characterized red blood cells in an overview image only.

As in the current RBC functionality, the user reviews the overview image and can change the characterization by manually changing the grades for any morphological characteristic. With the Advanced RBC Application, the user can also view individual cells, grouped by morphological characteristic and change the characterization by reclassifying individual cells.

The provided text describes the CellaVision DM96 and DM1200 with Advanced RBC Application, an automated cell-locating device. Here's a breakdown of the requested information based on the text:

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

The document states that a clinical evaluation was conducted, and the results "met the predefined acceptance criteria" for various metrics. However, the precise quantitative acceptance criteria and the exact reported performance values are not explicitly stated in this summary. The summary only mentions that the results fulfilled the acceptance criteria.

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

• Sample size: The document states, "Samples were collected and tested for RBC characterization on DM96 and DM1200 at different laboratories." However, the exact sample size for the clinical evaluation (test set) is not specified.
• Data provenance: The samples were collected "from routine workflow from hospital laboratories" and "in accordance with the target patient population, i.e. from samples flagged as abnormal by an automated cell counter." This suggests prospective collection from clinical settings, but specific countries of origin are not mentioned.

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)

For the clinical evaluation, the "manual microscopy (Reference Method)" was used as the primary comparator for most morphology groups. For the "RBC group Size," an "automated cell counter" was used as a "convenient predicate device."

• The document implies that the ground truth for most RBC characteristics was established by manual microscopy, which would involve human experts. However, the number of experts and their specific qualifications are not provided. The device's intended use states, "The CellaVision DM96/DM1200 with the Advanced RBC Application is intended to be used by skilled operators, trained in the use of the device and in recognition of blood cells," which implies that the reference method would also involve such skilled operators.

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

The document does not specify an adjudication method for establishing the ground truth from manual microscopy or for resolving discrepancies between readers (if multiple readers were used). It simply refers to "manual microscopy (Reference Method)" as the comparator.

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

• No, an MRMC comparative effectiveness study was not explicitly described in the provided text. The study conducted was a comparison between the automated device (CellaVision Advanced RBC Application) and manual microscopy (Reference Method), not a study evaluating human reader improvement with AI assistance.
• The device "automatically locates and presents images of blood cells on peripheral blood smears. The operator identifies and verifies the suggested classification of each cell according to type." While the device assists the human, the study's objective was about the equivalence of the device's characterization results to manual microscopy, not the improvement of human readers.

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

• No, a standalone (algorithm only) performance study was not described. The device is explicitly designed for a human-in-the-loop workflow: "The operator identifies and verifies the suggested classification of each cell according to type." Therefore, the evaluation would inherently include this human interaction. The clinical evaluation compared the "Advanced RBC Application installed on CellaVision DM96 and CellaVision DM1200 (Test Methods)" to the manual method, implying system performance with human verification.

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

The ground truth used was primarily:

• Expert consensus from manual microscopy for most RBC morphological characteristics.
• Automated cell counter results for the "RBC group Size" (Macrocytes, Microcytes, and Anisocytosis), as manual microscopy was deemed "highly difficult, time consuming and thereby impractical" for this group.

8. The sample size for the training set

The document does not specify the sample size used for the training set of the deterministic artificial neural networks (ANNs). It only mentions that the ANNs were "trained to distinguish between morphology characteristics of red blood cells."

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

The document does not explicitly state how the ground truth for the training set was established. It mentions that the ANNs were "trained to distinguish between morphology characteristics of red blood cells," implying that labeled data was used for training, but the method of obtaining these labels (e.g., expert annotations, specific pathological confirmation) is not detailed.

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DM1200 is an automated system intended for in-vitro diagnostic use.

The body fluid application is intended for differential count of white blood cells. The system automatically locates and presents images of cells on cytocentrifuged body fluid preparations. The operator identifies and verifies the suggested classification of each cell according to type.

DM1200 is intended to be used by skilled operators, trained in the use of the device and in recognition of blood cells.

CellaVision DM1200 with the body fluid application automatically locates and presents images of nucleated cells on cytocentrifuged body fluid preparations. The system suggests a classification for each cell and the operator verifies the classification and has the opportunity to change the suggested classification of any cell.
The system preclassifies to the following WBC classes: Unidentified, Neutrophils, Eosinophils, Lymphocytes, Macrophages (including Monocytes) and Other. Cells preclassified as Basophils, Lymphoma cells, Atypical lymphocytes, Blasts and Tumor cells are automatically forwarded to the cell class Other.
Unidentified is a class for cells and objects which the system has pre-classified with a low confidence level.

Here's an analysis of the provided text, outlining the acceptance criteria and study details for the CellaVision DM1200 with the body fluid application:

Acceptance Criteria and Device Performance Study for CellaVision DM1200 with Body Fluid Application

The CellaVision DM1200 with body fluid application is an automated cell-locating device intended for in-vitro diagnostic use, specifically for the differential count of white blood cells in cytocentrifuged body fluid preparations. The system automatically locates and presents cell images, suggests a classification, and requires operator verification.

1. Table of Acceptance Criteria and Reported Device Performance

The provided document does not explicitly state pre-defined "acceptance criteria" as pass/fail thresholds for accuracy or precision. Instead, it presents the results of a method comparison study between the CellaVision DM1200 (Test Method) and its predicate device, CellaVision DM96 (Reference Method), for various leukocyte classifications. The implied acceptance criteria are that the DM1200 demonstrates comparable accuracy and precision to the predicate device.

The conclusion states that the short-term imprecision was found to be equivalent for the test method and the reference method, and the accuracy results (high R-squared values, slopes close to 1, and intercepts close to 0) demonstrate substantial equivalence.

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

• Sample Size for Test Set: 260 samples.
  • CSF: 62 samples
  • Serous fluid: 151 samples
  • Synovial fluid: 47 samples
• Data Provenance:
  • Country of Origin: Not explicitly stated, but samples were collected from "two sites." Given the submitter is in Sweden, and the regulatory contact is in the USA, it's unclear if these sites were in Sweden, the USA, or elsewhere.
  • Retrospective or Prospective: Not explicitly stated, but the description "collected from two sites" and then analyzed suggests a prospective collection or at least fresh samples for the study.

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: Not explicitly stated for the establishment of ground truth for the test set's initial classifications. However, the document mentions:
  • "The results were then verified by skilled human operators." This indicates human review post-analysis by both the test and reference methods.
  • The "Intended Use" section states: "DM1200 is intended to be used by skilled operators, trained in the use of the device and in recognition of blood cells."
• Qualifications of Experts: "Skilled human operators, trained in the use of the device and in recognition of blood cells." No specific professional qualifications (e.g., "radiologist with 10 years of experience") are provided.

4. Adjudication Method for the Test Set

The document states: "The results were then verified by skilled human operators." It does not specify a multi-reader adjudication method like 2+1 or 3+1. It implies a single operator verification for each result generated by both the test and reference methods.

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

No, a multi-reader multi-case (MRMC) comparative effectiveness study designed to measure the effect size of how much human readers improve with AI vs. without AI assistance was not explicitly described. This study was a method comparison between two devices (one with the new application, one being the predicate) with human verification. The device's function is to suggest classifications, which implies an assistive role, but the study design was not an MRMC study comparing human performance with and without AI.

6. Standalone (Algorithm Only) Performance Study

Yes, a standalone performance was implicitly done. The "Test Method" (CellaVision DM1200) "suggests a classification for each cell," meaning the algorithm performs an initial classification without human intervention. The reported accuracy metrics ($R^2$, slope, intercept) compare these suggested classifications to those obtained by the reference method (DM96, which also involves algorithmic preclassification). However, the study concludes with human verification of these results, suggesting the standalone performance without the "human-in-the-loop" step described in the intended use is not the final reported performance. The "Accuracy results" table (Table 3.3) and "Precision/Reproducibility" table (Table 3.4) reflect the device's performance before the final human verification step that might change classifications.

7. Type of Ground Truth Used

The ground truth used for the comparison was established by the predicate device (CellaVision DM96) with its own human verification, after undergoing "a 200-cell differential count... with both the test method and the reference method. The results were then verified by skilled human operators." Therefore, it's a form of expert-verified reference measurement. It is not pathology, or outcomes data.

8. Sample Size for the Training Set

The document does not explicitly state the sample size for the training set used to develop the CellaVision DM1200's classification algorithms. It mentions "deterministic artificial neural networks (ANN's) trained to distinguish between classes of white blood cells," but no details about the training data are provided within this summary.

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

The document does not explicitly describe how the ground truth for the training set was established. It states that the ANNs were "trained to distinguish between classes of white blood cells," implying that a labeled dataset was used for training, but the process of creating these labels (e.g., expert consensus, manual review) is not detailed in this 510(k) summary.

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