Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2023

Open Access 01-12-2023 | Artificial Intelligence | Research

Artificial intelligence of digital morphology analyzers improves the efficiency of manual leukocyte differentiation of peripheral blood

Authors: Ying Xing, Xuekai Liu, Juhua Dai, Xiaoxing Ge, Qingchen Wang, Ziyu Hu, Zhicheng Wu, Xuehui Zeng, Dan Xu, Chenxue Qu

Published in: BMC Medical Informatics and Decision Making | Issue 1/2023

Login to get access

Abstract

Background and objective

Morphological 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.

Methods

A 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.

Results

For 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.

Conclusion

AI 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.
Appendix
Available only for authorised users
Literature
1.
PalmerL BriggsC. McFaddenS et al, ICSH recommendations for the standardization of nomenclature and grading of peripheral blood cell morphological features. Int J Lab Hematol. 2015;37(3):287–303.CrossRef
2.
The Hematology and Osology Group. Chinese society of laboratory medicine, guideline for the report standardization of complete blood count test. Chin J Lab Med. 2020;43(6):619–27.
3.
Ohsaka A. Artificial intelligence (AI) and hematological diseases: establishment of a peripheral blood convolutional neural network (CNN)-based digital morphology analysis system. Rinsho Ketsueki. 2020;61(5):564–9.PubMed
4.
Fan H, Zhang F, Xi L, Li Z, Liu G, Xu Y. LeukocyteMask: An automated localization and segmentation method for leukocyte in blood smear images using deep neural networks. J Biophotonics. 2019;12: e201800488.CrossRefPubMed
5.
Kono K, Hayata R, Murakami S, et al. Quantitative distinction of the morphological characteristic of erythrocyte precursor cells with texture analysis using gray level co-occurrence matrix. J Clin Lab Anal. 2018;32:1–6.CrossRef
6.
7.
Hongbo Yu, et al. Evaluation of an automated digital imaging system, nextslide digital review network, for examination of peripheral blood smears. Arch Pathol Lab Med. 2012;136:660–7.CrossRef
8.
Kratz A, Lee S-H, Zini G, et al. Digital morphology analyzers in hematology: ICSH review and recommendations. Int J Lab Hematol. 2019;41(4):437–47.PubMed
9.
Anita Yadav. A, An Intelligent Model for the Detection of White Blood Cells using Artificial Intelligence. Comput Methods Programs Biomed. 2021;199:105893.CrossRefPubMed
10.
11.
Eilertsen H, Henriksson CE, Hagve TA. The use of CellsVision DM96 in the verification of the presence of blasts in samples flagged by the sysmex XE-5000. Int J Lab Hematol. 2017;39(4):423–8.CrossRefPubMed
12.
Kim HN, Hur M, Kim H, et al. Performance of automated digital cell imaging analyzer sysmex DI-60. Clin Chem Lab Med. 2017;56:94–102.CrossRefPubMed
13.
Smits SM, Leyte A. Clinical performance evaluation of the cellavision image capture system in the white blood cell differential on peripheral blood smears. J Clin Pathol. 2014;67:168–72.CrossRefPubMed
14.
Alférez S, Merino A, Bigorra L, et al. Automatic recognition of atypical lymphoid cells from peripheral blood by digital image analysis. Am J Clin Pathol. 2015;143(2):168–76.CrossRefPubMed
15.
VanVranken SJ, Patterson ES, Rudmann SV, et al. A survey study of benefits and limitations of using CellaVision DM96 for peripheral blood differentials. Clin Lab Sci. 2014;27:32–9.CrossRefPubMed
16.
Merino A, Puigví L, Boldú L, et al. Optimizing morphology through blood cell image analysis. Int J Lab Hematol. 2018;40(Suppl 1):54–61.CrossRefPubMed
Metadata
Title
Artificial intelligence of digital morphology analyzers improves the efficiency of manual leukocyte differentiation of peripheral blood
Authors
Ying Xing
Xuekai Liu
Juhua Dai
Xiaoxing Ge
Qingchen Wang
Ziyu Hu
Zhicheng Wu
Xuehui Zeng
Dan Xu
Chenxue Qu
Publication date
01-12-2023
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2023
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-023-02153-z

Other articles of this Issue 1/2023

BMC Medical Informatics and Decision Making 1/2023 Go to the issue

Software

The Infomóvel—An information system for managing HIV/AIDS patients in rural areas of Mozambique

Research

Smartphone-based application to control and prevent overweight and obesity in children: design and evaluation

Research

The accuracy of artificial intelligence used for non-melanoma skin cancer diagnoses: a meta-analysis

Research

Accurate breast cancer diagnosis using a stable feature ranking algorithm

Research

Updating mortality risk estimation in intensive care units from high-dimensional electronic health records with incomplete data

Research

Construction and evaluation of a column chart model and a random forest model for predicting the prognosis of hydrodistention surgery in BPS/IC patients based on preoperative CD117, P2X3R, NGF, and TrkA levels