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Timely microscopy screening of peripheral blood smears is essential for the diagnosis of acute promyelocytic leukemia (APL) due to the occurrence of early death (ED) before or during the initial therapy. Screening manually is time-consuming and tedious, and may lead to missed diagnosis or misdiagnosis because of subjective bias. To address these problems, we develop a three-step pipeline to help in the early diagnosis of APL from peripheral blood smears. The entire pipeline consists of leukocytes focusing, cell classification and diagnostic opinions. As the key component of the pipeline, a compact classification model based on attention embedded convolutional neural network blocks is proposed to distinguish promyelocytes from normal leukocytes. The compact classification model is validated on both the combination of two public datasets, APL-Cytomorphology_LMU and APL-Cytomorphology_JHH, as well as the clinical dataset, to yield a precision of 96.53% and 99.20%, respectively. The results indicate that our model outperforms the other evaluated popular classification models owing to its better accuracy and smaller size. Furthermore, the entire pipeline is validated on realistic patient data. The proposed method promises to act as an assistant tool for APL diagnosis.

BackgroundAcute promyelocytic leukemia (APL) is a subtype of acute myeloid leukemia (AML) characterized by its rapidly progressive and fatal clinical course if untreated, although it is curable if treated in a timely manner. Promptly screening patients who have results that are suspicious for APL is vital to overcome early death.MethodsThe authors developed an innovative framework consisting of ResNet‐18, a convolutional neural network architecture, with the objective of quantitatively mapping a complete blood count (CBC) scattergram to quickly and robustly indicate a probable susceptibility to APL. Three hundred and twenty scattergrams of the white blood cell differential channel from 51 patients with APL, 510 scattergrams from 105 patients who had non‐APL AML, and 320 scattergrams from 320 healthy controls were randomly stratified at a ratio of 4:1 and split into training and testing data sets to accomplish five‐fold cross‐validation.ResultsBoth the area under the curve and the average precision of >0.99 were achieved in each fold. Three hundred four of the 320 APL scattergrams (95%) were correctly flagged by the model, which outcompeted the CBC review rules recommended by the International Society of Laboratory Hematology (all p < .001). External validation based on an independent testing data set that included 56 scattergrams from 31 patients with APL, 56 scattergrams from 55 patients with non‐APL AML, and 64 scattergrams from 64 healthy controls also confirmed the sensitivity and specificity of the framework.ConclusionsTo the authors’ knowledge, their convolutional neural network‐based framework is the first to use scattergram output from routine CBC analysis to map suspicious APL early with outstanding sensitivity, specificity, and precision. The authors also describe a new CBC workflow incorporating this framework upstream of the morphologic review, which would provide the earliest flag for APL.Plain Language Summary 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.

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