Your browser doesn't support javascript.
loading
Artificial intelligence of digital morphology analyzers improves the efficiency of manual leukocyte differentiation of peripheral blood.
Xing, Ying; Liu, Xuekai; Dai, Juhua; Ge, Xiaoxing; Wang, Qingchen; Hu, Ziyu; Wu, Zhicheng; Zeng, Xuehui; Xu, Dan; Qu, Chenxue.
Affiliation
  • Xing Y; Department of Clinical Laboratory, Peking University First Hospital, Beijing, China.
  • Liu X; Department of Clinical Laboratory, Aerospace Center Hospital, Beijing, China.
  • Dai J; Department of Clinical Laboratory, Peking University International Hospital, Beijing, China.
  • Ge X; Department of Clinical Laboratory, Miyun District Hospital, Beijing, China.
  • Wang Q; Department of Clinical Laboratory, Peking University First Hospital, Beijing, China.
  • Hu Z; Department of Clinical Laboratory, Beijing Nuclear Industry Hospital, Beijing, China.
  • Wu Z; Department of Clinical Laboratory, Peking University Shenzhen Hospital, Shenzhen, China.
  • Zeng X; Department of Clinical Laboratory, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, China.
  • Xu D; Department of Clinical Laboratory, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China.
  • Qu C; Department of Clinical Laboratory, Peking University First Hospital, Beijing, China. qucx2012@163.com.
BMC Med Inform Decis Mak ; 23(1): 50, 2023 03 29.
Article in En | MEDLINE | ID: mdl-36991420
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.
Subject(s)
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Artificial Intelligence / Leukocytes Type of study: Diagnostic_studies Limits: Humans Language: En Journal: BMC Med Inform Decis Mak Journal subject: INFORMATICA MEDICA Year: 2023 Document type: Article Affiliation country: China

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Artificial Intelligence / Leukocytes Type of study: Diagnostic_studies Limits: Humans Language: En Journal: BMC Med Inform Decis Mak Journal subject: INFORMATICA MEDICA Year: 2023 Document type: Article Affiliation country: China