Abstract
AIM: Bone marrow biopsy is essential and necessary for the diagnosis of patients with aplastic anemia (AA), myelodysplastic syndromes (MDS), and acute myeloid leukemia (AML). However, the convolutional neural networks (CNN) model that automatically distinguished AA, MDS, and AML based on bone marrow smears has not been reported. METHODS: Image-net pretrained model of CNN was used to construct the recognition model. Data extracted from the American Society of Hematology (ASH) Image Bank were utilized to develop the model and data extracted from the clinic were used for external validation. The model had two output layers: whether the patient was MDS (two-classification) and which of AA, MDS, and AML the patient was (three-classification). Different outcome weights (two-classification/three-classification = 5:5, 2:8, 1:9) and epochs (30, 50, 200) were used to select the optimal model. The model performance was evaluated by the Accuracy-Loss curves and calculating the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS: A total of 115 bone marrow smears from the ASH Image Bank and 432 bone marrow smears from the clinic were included in this study. The results of Accuracy-Loss curves showed that the best model training effect was observed in the model with the outcome weight and epoch of 1:9 and 200. Similarly, this model also performed well performances in the two-classification of MDS and the three-classification of AA, MDS, AML. The AUC, accuracy and sensitivity of the MDS two-classification model in the testing set were 0.985 [95% confidence interval (CI), 0.979-0.991], 0.914 (95%CI, 0.895-0.934), and 0.992 (95%CI, 0.980-1.000), respectively. The AUC, accuracy and sensitivity of the AA, MDS, AML three-classification model in the testing set were 0.968 (95%CI, 0.960-0.976), 0.929 (95%CI, 0.916-0.941), and 0.857 (95%CI, 0.828-0.886), respectively. CONCLUSION: The image-net pretrained model was able to obtain high accuracy AA, MDS, AML distinction, and may provide clinicians with a convenient tool to distinguish AA, MDS, and AML.