Research and application of a new deep learning based strategy for platelet histogram review
10.3760/cma.j.cn114452-20250626-00377
- VernacularTitle:基于深度学习的血小板图形复检新策略的研究与应用
- Author:
Enming ZHANG
1
;
Chao YANG
;
Xianchun CHEN
;
Yan LIN
;
Taixue AN
;
Haixia LI
;
Yongjian HE
;
Zhiwei LIU
;
Limei FENG
;
Wanying LIN
;
Tie XIONG
;
Kai QIU
;
Ya GAO
;
Lizhu HUANG
;
Jing HE
;
Chunyan WANG
;
Dehua SUN
;
Bo SITU
;
Lei ZHENG
Author Information
1. 南方医科大学南方医院检验医学科，广东省精准医学诊断重点实验室，广东省快速诊断生物传感器工程技术研究中心，广东省单细胞技术与应用重点实验室，广州 510515
- Publication Type:Journal Article
- Keywords:
Artificial intelligence;
Deep learning;
Platelet review;
Blood cell count;
Turnaround time
- From:
Chinese Journal of Laboratory Medicine
2025;48(9):1201-1206
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.
