ArticleBMC medical informatics and decision making2023
Artificial intelligence of digital morphology analyzers improves the efficiency of manual leukocyte differentiation of peripheral blood.
Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 24 citations in OpenAlex.
- Clinical Validation of the MC-80 Digital Morphology Analyzer in Leukocyte Classification: A Large-Scale Comparison With Manual Microscopy.International journal of laboratory hematology · 2026Article
- An expert-level vision-language model for multitask diagnostic morphology in clinical laboratories.NPJ digital medicine · 2026Article
- Review
- Artificial intelligence in haematology laboratory diagnosis: Current applications, challenges, and future directions.African journal of laboratory medicine · 2026Review
- The Diagnostic Performance of the Cellavision DC-1 Digital Morphology Analyser on Leukaemia Samples.Diagnostics (Basel, Switzerland) · 2025Article
- Advanced leukocyte classification using attention mechanisms and dual channel U-Net architecture.Scientific reports · 2025Article
- Real-World Application of Digital Morphology Analyzers: Practical Issues and Challenges in Clinical Laboratories.Diagnostics (Basel, Switzerland) · 2025Review
- A novel method for screening malignant hematological diseases by constructing an optimal machine learning model based on blood cell parameters.BMC medical informatics and decision making · 2025Article
- Reducing the workload of medical diagnosis through artificial intelligence: A narrative review.Medicine · 2025Review
- Advancing Laboratory Medicine Practice With Machine Learning: Swift yet Exact.Annals of laboratory medicine · 2025Review
- A prospective study for the examination of peripheral blood smear samples in pediatric population using artificial intelligence.Turkish journal of medical sciences · 2025Article
- Artificial intelligence-assisted early screening of acute promyelocytic leukaemia in blood smears: a prospective evaluation of MC-100i.Frontiers in oncology · 2025Article
- Performance evaluation of the digital morphology analyser Sysmex DI-60 for white blood cell differentials in abnormal samples.Scientific reports · 2024Article
- ChatGPT's innovative application in blood morphology recognition.Journal of the Chinese Medical Association : JCMA · 2024Article
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Authors and funding
10 authors at 7 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectiveMorphological identification of peripheral leukocytes is a complex and time-consuming task, having especially high requirements for personnel expertise. This study is to investigate the role of artificial intelligence (AI) in assisting the manual leukocyte differentiation of peripheral blood.
methodsA total of 102 blood samples that triggered the review rules of hematology analyzers were enrolled. The peripheral blood smears were prepared and analyzed by Mindray MC-100i digital morphology analyzers. Two hundreds leukocytes were located and their cell images were collected. Two senior technologists labeled all cells to form standard answers. Afterward, the digital morphology analyzer unitized AI to pre-classify all cells. Ten junior and intermediate technologists were selected to review the cells with the AI pre-classification, yielding the AI-assisted classifications. Then the cell images were shuffled and re-classified without AI. The accuracy, sensitivity and specificity of the leukocyte differentiation with or without AI assistance were analyzed and compared. The time required for classification by each person was recorded.
resultsFor junior technologists, the accuracy of normal and abnormal leukocyte differentiation increased by 4.79% and 15.16% with the assistance of AI. And for intermediate technologists, the accuracy increased by 7.40% and 14.54% for normal and abnormal leukocyte differentiation, respectively. The sensitivity and specificity also significantly increased with the help of AI. In addition, the average time for each individual to classify each blood smear was shortened by 215 s with AI.
conclusionAI can assist laboratory technologists in the morphological differentiation of leukocytes. In particular, it can improve the sensitivity of abnormal leukocyte differentiation and lower the risk of missing detection of abnormal WBCs.
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