Affiliation:
1. Clinical Laboratory Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China
2. Clinical Laboratory Huazhong University of Science and Technology Tongji Medical College Tongji Hospital Wuhan China
3. Clinical Laboratory The First Affiliated Hospital of Soochow University Suzhou China
Abstract
AbstractIntroductionThis study aims to assess the performance of the platelet count estimation using artificial intelligence technology on the MC‐80 digital morphology analyzer.MethodsDigital morphology analyzer uses two different computational principles for platelet count estimation: based on PLT/RBC ratio (PLT‐M1) and estimate factor (PLT‐M2). 977 samples with various platelet counts (low, median, and high) were collected. Out of these, 271 samples were immunoassayed using CD61 and CD41 antibodies. The platelet counts obtained from the hematology analyzer (PLT‐I and PLT‐O), digital morphology analyzer (PLT‐M1 and PLT‐M2), and flow cytometry (PLT‐IRM) were compared.ResultsThere was no significant deviation observed before and after verification for both PLT‐M1 and PLT‐M2 across the analysis range (average bias: −0.845/−0.682, 95% limit of agreement (LOA): −28.675–26.985/−29.420–28.056). When platelet alarms appeared, PLT‐M1/PLT‐M2 showed the strongest correlation with PLT‐IRM than PLT‐I with PLT‐IRM (r: 0.9814/0.9796 > 0.9601). The correlation between PLT‐M1/PLT‐M2 and PLT‐IRM was strong for samples with interference, such as large platelets or RBC fragments, but relatively weak in small RBCs. The deviation between PLT‐M1 and PLT‐M2 is related to the number of RBCs. Compared with PLT‐I, PLT‐M1/PLT‐M2 showed higher accuracy for platelet transfusion decisions, especially for samples with low‐value PLT.ConclusionThe novel platelet count estimation on the MC‐80 digital morphology analyzer provides high accuracy, especially the reviewed result, which can effectively confirm suspicious platelet count.
Funder
Wu Jieping Medical Foundation