EASYCELL CELL LOCATOR

The EasyCell is an automated in vitro diagnostic system that scans and analyzes peripheral blood smears to aid in hematological assessments. It employs image capture, mathematical algorithms, and artificial neural networks to locate, pre-classify white blood cells, and present images of red blood cells and platelets for operator review and evaluation.

The EasyCell is an automated cell-locating device designed for in vitro diagnostic use by skilled professionals. Its primary function is to locate and present digital images of white blood cells, red blood cells, and platelets from prepared peripheral blood smears. This system assists qualified technologists in conducting white blood cell (WBC) differential counts, evaluating red blood cell (RBC) morphology, and estimating platelet numbers, thereby streamlining and supporting manual review processes. The device operates by scanning Romanowsky-stained blood films on glass slides using a battlement track pattern to ensure comprehensive area coverage. Cell images are then analyzed using standard mathematical methods, critically incorporating deterministic artificial neural networks (ANNs) specifically trained to distinguish between different classes of white blood cells. The EasyCell pre-classifies WBCs, which are then reviewed, confirmed, or re-classified by the operator. For RBCs, it presents a series of images for morphological assessment, and for platelets, images are provided for operator estimation according to standardized procedures. Performance studies involving 304 patient samples demonstrated high correlation with traditional manual microscopy for WBC differentials (e.g., neutrophil r=0.99, lymphocyte r=0.98), overall sensitivity of 91% and specificity of 72% in identifying abnormalities, good agreement for platelet estimates, and over 90% agreement for RBC morphology assessment.

AI Regulatory Concerns

Powered by

Jev reads the full FDA letter and returns calibrated concern probabilities per regulatory category. These are AI estimates, not regulatory advice.

• Q1. How does the performance of the EasyCell's artificial neural network vary with challenging or rare cell morphologies not extensively represented in its training dataset?; What is the specific training methodology and dataset size used for the deterministic artificial neural networks in classifying white blood cells?; What are the quantitative limits of detection for abnormal cells (e.g., blast cells) and how does this compare to manual microscopy, particularly at low concentrations?; Could the EasyCell's image analysis capabilities be expanded to quantify other hematological parameters, such as reticulocyte counts or identify parasitic inclusions?; How does the 'battlement track pattern' for cell location optimize for both speed and representativeness of the entire blood smear compared to other scanning patterns?