Pilot Study on the Performance of a New System for Image Based Analysis of Peripheral Blood Smears on Normal Samples

Abstract

Image analysis based automated systems aiming to automate the manual microscopic review of peripheral blood smears have gained popularity in recent times. In this paper, we evaluate a new blood smear analysis system based on artificial intelligence, by SigTuple Technologies Private Limited. One hundred normal samples with no flags from an automated haematology analyser were taken. Peripheral blood smear slides were prepared using the autostainer integrated with an automated haematology analyser and stained using May-Grunwald-Giemsa stain. These slides were analysed with. The metrics for evaluation included (1) accuracy of white blood cell classification for the five normal white blood cell types, and (2) comparison of white blood cell differential count with the automated haematology analyser. In addition, we also explored the possibility of estimating the value of red blood cell and platelet indices via image analysis. Overall white blood cell classification specificity was greater than 97.90% and the precision was greater than 93.90% for all the five white blood cell classes. The correlation of the white blood cell differential count between the automated haematology analyser and was found to be within the known inter cell-counter variability. was found to show promise in terms of its ability to analyse peripheral blood smear images to derive quantifiable metrics useful for clinicians. Future enhancement should include the ability to analyse abnormal blood samples.

Artificial intelligence and the blood film: Performance of the MC‐80 digital morphology analyzer in samples with neoplastic and reactive cell types

“…Sensitivity, specificity, and predictive values for identifying films containing such cells which are normally absent in circulating blood, were good for IGs, NRBCs, neoplastic cells (blasts) and reactive lymphocytes. These results were generally comparable with those obtained by other studies evaluating digital morphology analyzers for the hematology laboratory 9–13 . They ensure the excellent clinical performance of the MC‐80 in terms of clinical diagnostic utility and as a completion of the WBC differential count provided by the flow cytometry‐based blood cell counters.…”

“…These results were generally comparable with those obtained by other studies evaluating digital morphology analyzers for the hematology laboratory. [9][10][11][12][13] They ensure the excellent clinical performance of the MC-80 in terms of clinical diagnostic utility and as a completion of the WBC differential count provided by the flow cytometry-based blood cell counters.…”

Analysis of red blood cells from peripheral blood smear images for anemia detection: a methodological review

“…Adagale et al [16] proposed an overlapped RBC counting algorithm using Pulse Coupled Neural Network with a template matching technique and obtained 90% average accuracy for 40 images. Chari et al [51] presented a pilot study on the analysis of MGG stained normal images using Shonit TM artificial intelligence system. The extracted cells were classified using three different deep neural network models based on images annotated by three experts.…”

Microcontroller based in vitro hematocrit measurement system

“…Numerous systems are designed to measure the HCT automatically. The microscopic images of blood sample, for example, has been used to calculate the total number of red and white blood cells [9][10][11][12][13]. The red blood cells (RBCs) have been used to calculate the level of HCT and to measure the Hemoglobin [14][15].…”