Application of convolutional neural network in flow cytometry diagnosis of acute myeloid leukemia
10.19405/j.cnki.issn1000-1492.2023.07.021
- Author:
Wei Lei
1
;
Zhiwei Li
2
,
3
;
Dongsheng Rui
1
;
Mei Zhang
1
;
Yujuan Guo
1
;
Wenli Bai
1
;
Kui Wang
1
Author Information
1. Dept of Preventive Medicine , School of Medicine , Shihezi University, Shihezi 832002
2. Clinical Testing Center, Xinjiang Uygur Autonomous Region People &prime
3. s Hospital , Urumqi 830001
- Publication Type:Journal Article
- Keywords:
flow cytometry;
acute myeloid leukemia;
convolutional neural networks;
automated diagnosis
- From:
Acta Universitatis Medicinalis Anhui
2023;58(7):1189-1193
- CountryChina
- Language:Chinese
-
Abstract:
Objective :A convolutional neural network (CNN) model was established to automatically analyze flow
cytometry (FCM) data to achieve the preliminary diagnosis of acute myeloid leukemia(AML) , and explore the feasibility of applying CNN model to FCM data analysis.
Methods :The exploratory study of CNN application was carried out using the bone marrow FCM data obtained by the FlowRepository database and the Clinical Testing Center of Xinjiang Uygur Autonomous Region People ′ s Hospital , and the data had been clinically confirmed whether AML was present. Among them , the public data was divided into training sets , validation sets and test sets according to 6 ∶ 2 ∶ 2 , and local data was used for external test; In order to adapt the FCM data to the CNN model , an
FCM data structure based on the image matrix principle was proposed , and after preprocessing the original data , the variables related to the preliminary diagnosis of AML were extracted , including sidescattered light and the expression levels of CD45 , CD13 , CD33 , HLA⁃DR , CD117 , CD34 , and each variable was written into the matrix. Cell sampling and data augmentation methods were used to increase the sample size of the training set , the keras software package was used to build the LeNet⁃5 CNN model in Python , and the training set and the validation set were used for model training and parameter tuning respectively to evaluate the performance of the model on the test set.
Results :The accuracy of CNN to identify AML on the two test sets was 0. 931 , 0. 851 , the sensitivity was 0. 667 ,
0. 636 , the specificity was 0. 968 , 0. 940 , and the area under the receiver operating characteristic curve was 0. 940
and 0. 917.
Conclusion :Based on the proposed FCM data structure , the CNN model can realize the preliminary
diagnosis of AML , indicating that CNN has certain application value in FCM data analysis.
- Full text:2024101017050041088卷积神经网络在急性髓系白血...流式细胞术自动诊断中的应用_雷伟.pdf
