Fig. 1

Analysis workflow and performance evaluation of AMLnet. a Bone marrow smears were first stained by Wright staining and digitized with an oil immersion microscope at ×100 magnification to images. The images were then labeled for training models. The trained models were used to analyze the patient’s images and applied to clinical practice. b The performance of our AMLnet for detecting the presence of AML on the validation set and test set. c Comparison of the current mainstream deep-learning neural networks in detecting different subtypes of AML in the test set, including EfficientNet-b4, RepVGG-b0, and ResNet18. d The confusion matrix of the AMLnet at the image level on the test set. e The ROC curve of the AMLnet at the image level and patient level on the test set. We used bootstrapping to estimate the confidence intervals of the AUC. f Top-1 to top-3 accuracy of the AMLnet at the image level and patient level based on majority votes across all subtypes of AML. g The accuracy curve of the diverse vote approaches at the patient level. As the number of images for each patient increases, the accuracy of our AMLnet increases