Medical & Clinical Research

Author(s): Renu Ethirajan*, Dheeraj Mundhra, Jaiprasad Rampure, Shreepad Potadar, Sukrit Mukherjee and Bharath Cheluvaraju

In this study, we evaluate ShonitTM, an artificial intelligence (AI) system for automated analysis of images captured from peripheral blood smears, consisting of an automated digital microscope and a cloud based analysis platform. ShonitTM’s performance in classification of WBCs was evaluated by comparing ShonitTM’s results with haematologyanalysers and manual microscopy for manually stained smears. The study was carried out over 100 samples. The cases included both normal and abnormal samples, wherein the abnormal cases were from patients with one or more quantitative or qualitative flagging. All the smears were created using Hemaprep auto-smearer and stained using May Grunwald Giemsa stain. They were scanned and analysed by ShonitTM for WBC differentials under 40X magnification.WBC morphological classification by ShonitTM was verified by an experienced haemato-pathologist. Quantitative parameters were analysed by computing the mean absolute difference of the WBC DC values between ShonitTM and Sysmex XN3000, between ShonitTM and manual microscopy & between ShonitTM and Horiba ES 60. The mean absolute difference between WBC differential values of manual microscopy and ShonitTM were 7.67%, 5.93%, 4.58%, 2.69%, 0.44% for neutrophil, lymphocyte, monocyte, eosinophil and basophil respectively. The mean absolute difference between WBC differential values of Sysmex XN3000 and ShonitTM were 8.73%, 5.55%, 3.63%, 2.12%, 0.45% for neutrophil, lymphocyte, monocyte, eosinophil and basophil respectively. ShonitTM has proven to be effective in locating and examining WBCs. It saves time, accelerates the turnaround-time and increases productivity of pathologists. It has helped to overcome the time-consuming effort associated with traditional microscopy