RETRACTED ARTICLE: AMLnet, A deep-learning pipeline for the differential diagnosis of acute myeloid leukemia from bone marrow smears

Abstract

Acute myeloid leukemia (AML) is a deadly hematological malignancy. Cellular morphology detection of bone marrow smears based on the French–American–British (FAB) classification system remains an essential criterion in the diagnosis of hematological malignancies. However, the diagnosis and discrimination of distinct FAB subtypes of AML obtained from bone marrow smear images are tedious and time-consuming. In addition, there is considerable variation within and among pathologists, particularly in rural areas, where pathologists may not have relevant expertise. Here, we established a comprehensive database encompassing 8245 bone marrow smear images from 651 patients based on a retrospective dual-center study between 2010 and 2021 for the purpose of training and testing. Furthermore, we developed AMLnet, a deep-learning pipeline based on bone marrow smear images, that can discriminate not only between AML patients and healthy individuals but also accurately identify various AML subtypes. AMLnet achieved an AUC of 0.885 at the image level and 0.921 at the patient level in distinguishing nine AML subtypes on the test dataset. Furthermore, AMLnet outperformed junior human experts and was comparable to senior experts on the test dataset at the patient level. Finally, we provided an interactive demo website to visualize the saliency maps and the results of AMLnet for aiding pathologists’ diagnosis. Collectively, AMLnet has the potential to serve as a fast prescreening and decision support tool for cytomorphological pathologists, especially in areas where pathologists are overburdened by medical demands as well as in rural areas where medical resources are scarce.

Artificial Intelligence in Healthcare: 2023 Year in Review

“…For management many of the oncology publications focused on radiotherapy planning and predicting or measuring treatment effects for various cancers [21]. Pathology publications continue to focus on use of AI on whole slide images or cytopathology for diagnosis using deep learning [22,23]. Cardiology publications use multimodal data including EKG, CT angiograms amongst others for diagnosis and prediction of arrhythmias or ischemic heart disease,respectively [24][25][26].…”

“…Search was limited to PubMed and with the restrictions mentioned in the methodology section. It is possible that some of the significant publications might have been missed due to our methodology.In prior years, we have manually excluded many publications from the PubMed search and classified publications into various select healthcare specialities.This year due to the volume of publications (23,306), it was not possible to perform any manual evaluation of all the publications. We did trial various methods including BERT and GPT for automated classification including fine tuning on prior data but the results were not satisfactory.…”

Decoding leukemia at the single-cell level: clonal architecture, classification, microenvironment, and drug resistance

“…DL frameworks have been devised to discriminate between molecular subtypes in various cancers [ 144 ]. In the context of leukemia, different DL framework (AMLnet [ 145 ], CMLcGAN [ 146 ], ALNett [ 147 ]) has been applied to the diagnosis and classification of leukemia from medical images. The rapid emergence of AI that integrate and analyze omics data is happening in parallel with advancements in single-cell technologies.…”