Diagnosing acute promyelocytic leukemia by using convolutional neural network

Ouyang, Nengliang; Wang, Weijia; Ma, Li; Wang, Yanfang; Chen, Qingwu; Yang, Shanhong; Xie, Jinye; Su, Shaoshen; Cheng, Yin; Cheng, Qiong; Zheng, Lei; Yuan, Yong

doi:10.1016/j.cca.2020.10.039

Cited by 30 publications

(12 citation statements)

References 19 publications

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“…The time it takes from the upload of a BMS image to the model and subsequent output of a suggested diagnosis was 45.3 s. To the best of our knowledge, our model represents one of the first and the most accurate DL approach to recognize APL from bone marrow cytomorphology. The few recent studies have either focused on peripheral blood smears [38] or report a lower accuracy [39]. Given considerable early death rates especially in elderly patients [19], reliable methods for diagnosis are crucial to provide highly effective treatment.…”

Section: Discussionmentioning

confidence: 99%

Deep learning identifies Acute Promyelocytic Leukemia in bone marrow smears

et al. 2022

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Background Acute promyelocytic leukemia (APL) is considered a hematologic emergency due to high risk of bleeding and fatal hemorrhages being a major cause of death. Despite lower death rates reported from clinical trials, patient registry data suggest an early death rate of 20%, especially for elderly and frail patients. Therefore, reliable diagnosis is required as treatment with differentiation-inducing agents leads to cure in the majority of patients. However, diagnosis commonly relies on cytomorphology and genetic confirmation of the pathognomonic t(15;17). Yet, the latter is more time consuming and in some regions unavailable. Methods In recent years, deep learning (DL) has been evaluated for medical image recognition showing outstanding capabilities in analyzing large amounts of image data and provides reliable classification results. We developed a multi-stage DL platform that automatically reads images of bone marrow smears, accurately segments cells, and subsequently predicts APL using image data only. We retrospectively identified 51 APL patients from previous multicenter trials and compared them to 1048 non-APL acute myeloid leukemia (AML) patients and 236 healthy bone marrow donor samples, respectively. Results Our DL platform segments bone marrow cells with a mean average precision and a mean average recall of both 0.97. Further, it achieves high accuracy in detecting APL by distinguishing between APL and non-APL AML as well as APL and healthy donors with an area under the receiver operating characteristic of 0.8575 and 0.9585, respectively, using visual image data only. Conclusions Our study underlines not only the feasibility of DL to detect distinct morphologies that accompany a cytogenetic aberration like t(15;17) in APL, but also shows the capability of DL to abstract information from a small medical data set, i. e. 51 APL patients, and infer correct predictions. This demonstrates the suitability of DL to assist in the diagnosis of rare cancer entities. As our DL platform predicts APL from bone marrow smear images alone, this may be used to diagnose APL in regions were molecular or cytogenetic subtyping is not routinely available and raise attention to suspected cases of APL for expert evaluation.

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Section: Discussionmentioning

confidence: 99%

Deep learning identifies Acute Promyelocytic Leukemia in bone marrow smears

et al. 2022

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“…Based on this, Kermany et al established a diagnostic tool for the screening of patients with common treatable blinding retinal diseases, which demonstrated comparable classification performance to that of human experts (17). Using CNN, mapping of the relationships between the image of bone marrow cells and classification can be established to realize the effective recognition of bone marrow morphology (18)(19)(20). Thus, establishing an efficient algorithm and training through a large number of images can break the dependence of bone marrow cell morphology analysis on human recognition and realize intelligent recognition.…”

Section: Original Articlementioning

confidence: 99%

A deep learning method and device for bone marrow imaging cell detection

Liu¹,

Yuan²,

Li³

et al. 2022

Ann Transl Med

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Background: Morphological analysis of bone marrow cells is considered as the gold standard for the diagnosis of leukemia. However, due to the diverse morphology of bone marrow cells, extensive experience and patience are needed for morphological examination. automatic diagnosis system through the comprehensive application of image analysis and pattern recognition technology is urgently needed to reduce work intensity, error probability and improves work efficiency. Methods: In this article, we establish a new morphological diagnosis system for bone marrow cell detection based on the deep learning object detection framework. The model is based on the Faster Region-Convolutional Neural Network (R-CNN), a classical object detection model. The system automatically detects bone marrow cells and determines their types. As specimens have severe long-tail distribution, i.e., the frequency of different types of cells varies dramatically, we proposed a general score ranking loss to solve such a problem. The general score ranking loss considers the ranking relationship between positive and negative samples and optimizes the positive sample with a higher classification probability value.Results: We verified this system with 70 bone marrow specimens of leukemia patients, which proved that it can realize intelligent recognition with high efficiency. The software is finally integrated into the microscope system to build an augmented reality system.Conclusions: Clinical tests show that the response speed of the newly developed diagnostic system is faster than that of trained diagnostic experts.

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“…Their findings showed that the introduced CNN is robust and achieves competitive segmentation accuracy. Ouyang et al [ 30 ] proposed a diagnosis system for AML based on instance segmentation with CNNs. A custom dataset of microscopic blood images were subjected to instance segmentation using the Mask R-CNN algorithm to detect the nucleated WBCs.…”

Section: Literature Reviewmentioning

confidence: 99%

“…CNN-based computer-aided diagnosis (CAD) systems have been recognized for their effectiveness in detecting the presence of numerous diseases, such as diabetic retinopathy and its complications [ 20 , 21 ], various types of cancer [ 22 , 23 ], and COVID-19 [ 12 ]. This cutting-edge technology has been used for image segmentation [ 24 , 25 , 26 , 27 ], classification [ 28 , 29 ], and object identification and recognition [ 30 , 31 , 32 ]. The CNN structure is designed to learn spatial hierarchies of features automatically and adaptively from gridded data such as images [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

BO-ALLCNN: Bayesian-Based Optimized CNN for Acute Lymphoblastic Leukemia Detection in Microscopic Blood Smear Images

Atteia

Alhussan

2022

Sensors

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Acute lymphoblastic leukemia (ALL) is a deadly cancer characterized by aberrant accumulation of immature lymphocytes in the blood or bone marrow. Effective treatment of ALL is strongly associated with the early diagnosis of the disease. Current practice for initial ALL diagnosis is performed through manual evaluation of stained blood smear microscopy images, which is a time-consuming and error-prone process. Deep learning-based human-centric biomedical diagnosis has recently emerged as a powerful tool for assisting physicians in making medical decisions. Therefore, numerous computer-aided diagnostic systems have been developed to autonomously identify ALL in blood images. In this study, a new Bayesian-based optimized convolutional neural network (CNN) is introduced for the detection of ALL in microscopic smear images. To promote classification performance, the architecture of the proposed CNN and its hyperparameters are customized to input data through the Bayesian optimization approach. The Bayesian optimization technique adopts an informed iterative procedure to search the hyperparameter space for the optimal set of network hyperparameters that minimizes an objective error function. The proposed CNN is trained and validated using a hybrid dataset which is formed by integrating two public ALL datasets. Data augmentation has been adopted to further supplement the hybrid image set to boost classification performance. The Bayesian search-derived optimal CNN model recorded an improved performance of image-based ALL classification on test set. The findings of this study reveal the superiority of the proposed Bayesian-optimized CNN over other optimized deep learning ALL classification models.

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Diagnosing acute promyelocytic leukemia by using convolutional neural network

Cited by 30 publications

References 19 publications

Deep learning identifies Acute Promyelocytic Leukemia in bone marrow smears

Deep learning identifies Acute Promyelocytic Leukemia in bone marrow smears

A deep learning method and device for bone marrow imaging cell detection

BO-ALLCNN: Bayesian-Based Optimized CNN for Acute Lymphoblastic Leukemia Detection in Microscopic Blood Smear Images

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