A convolutional neural network-based, quantitative complete blood count scattergram-mapping framework promptly screens acute promyelocytic leukemia with high sensitivity.
Hongyan LiaoYuanxin XuQiang MengZhigang MaoYifan QiaoYan LiuQin ZhengPublished in: Cancer (2023)
The authors propose an innovative way to visualize complete blood counts (CBCs) by mapping the difference in white blood cell counts using automated CBC analysis to identify potential acute promyelocytic leukemia (APL) using a convolutional neural network (CNN), which can eliminate the potential pitfalls of manual observation. Analyses of an unprecedented, realistic data set validated that the quantitative relationship between the CBC scattergram and an APL abnormality is highly consistent. This is the first study to date focusing on screening for APL using scattergrams of the difference in white blood cell counts from routine CBC tests and has significant clinical relevance. The authors recommend using this method even before analyzing cell images, which could provide the earliest way to screen for APL in a sensitive and accurate way.
Keyphrases
- convolutional neural network
- deep learning
- high resolution
- single cell
- cell therapy
- high throughput
- liver failure
- peripheral blood
- acute myeloid leukemia
- bone marrow
- machine learning
- mass spectrometry
- respiratory failure
- acute respiratory distress syndrome
- electronic health record
- drug induced
- gene expression
- artificial intelligence
- aortic dissection
- hepatitis b virus
- extracorporeal membrane oxygenation