Image processing and machine learning in the morphological analysis of blood cells

Rodellar, José; Alférez, Santiago; Acevedo, Andrea; Molina, Ángel; Merino, Anna

doi:10.1111/ijlh.12818

Cited by 87 publications

(63 citation statements)

References 30 publications

(37 reference statements)

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“…Quantitative features facilitate the last step of the image analysis process, which is the automatic cell classification. The reader may find more detailed discussions about image processing and machine learning tools in the morphological analysis of blood cells …”

Section: Reactive and Malignant Cell Recognition Using Quantitative Mmentioning

confidence: 99%

Optimizing morphology through blood cell image analysis

Merino

Puigví

Boldú

et al. 2018

Int J Lab Hematology

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Section: Reactive and Malignant Cell Recognition Using Quantitative Mmentioning

confidence: 99%

Optimizing morphology through blood cell image analysis

Merino

Puigví

Boldú

et al. 2018

Int J Lab Hematology

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“…Thus, leukemia was one of the potential targets of ML utilization. Multiple articles have investigated the different techniques of segmentation and classification of different blood cells, including white blood cells . Interest in computer‐based diagnosing systems has started six decades ago, with early work on blood and cervical smears .…”

Section: Introductionmentioning

confidence: 99%

“…The introduction of ML algorithms has developed the approach to computer‐based diagnosis. Multiple approaches have been already developed to perform different tasks involved in detecting abnormal blood cells …”

Section: Introductionmentioning

confidence: 99%

Machine learning applications in the diagnosis of leukemia: Current trends and future directions

Salah

Muhsen

Salama

et al. 2019

Int J Lab Hematology

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Augmented human intelligence (AHI) and artificial intelligence (AI) tools might shape the future of medical practice. The expansion of data generated by our systems, medical literature, and the inefficiencies of healthcare systems will necessitate utilizing the power of AI tools. 1,2 The integration of AHI tools into medical practice, including machine learning (ML) and deep learning algorithms, has begun. For instance, the United States food and drug administration (US-FDA) has approved many AI-based softwares since 2017 for medical use. 2,3 The introduction of digital pathology has brought many opportunities to the field of pathology, such as telemedicine. 4,5 Recently, the use of digital pathology has allowed for the use of ML (including deep learning algorithms) in the automation of pathological diagnosis. 6,7 The challenges facing the use of ML in pathology are many, including digitalizing slides, labeling in case of Abstract Machine learning (ML) offers opportunities to advance pathological diagnosis, especially with increasing trends in digitalizing microscopic images. Diagnosing leukemia is time-consuming and challenging in many areas globally and there is a growing trend in utilizing ML techniques for its diagnosis. In this review, we aimed to describe the literature of ML utilization in the diagnosis of the four common types of leukemia: acute lymphocytic leukemia (ALL), chronic lymphocytic leukemia (CLL), acute myeloid leukemia (AML), and chronic myelogenous leukemia (CML). Using a strict selection criterion, utilizing MeSH terminology and Boolean logic, an electronic search of MEDLINE and IEEE Xplore Digital Library was performed. The electronic search was complemented by handsearching of references of related studies and the top results of Google Scholar. The full texts of 58 articles were reviewed, out of which, 22 studies were included. The number of studies discussing ALL, AML, CLL, and CML was 12, 8, 3, and 1, respectively. No studies were prospectively applying algorithms in real-world scenarios. Majority of studies had small and homogenous samples and used supervised learning for classification tasks. 91% of the studies were performed after 2010, and 74% of the included studies applied ML algorithms to microscopic diagnosis of leukemia. The included studies illustrated the need to develop the field of ML research, including the transformation from solely designing algorithms to practically applying them clinically. K E Y W O R D S diagnosis, digital, leukemia, machine learning, pathology How to cite this article: Salah HT, Muhsen IN, Salama ME, Owaidah T, Hashmi SK. Machine learning applications in the diagnosis of leukemia: Current trends and future directions. Int J Lab Hematol. 2019;41:717-725. https ://doi.

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“…All programs showed high predictability and versatility. 24 AI and ML have also been applied in the following fields: the morphological analysis of blood cells, 25 the identification of prognostic factors of ALL in childhood, 26 and the differential diagnosis of hematological diseases. 27 High CIR rates after allo-HSCT represent a clinical issue that needs to be resolved in adverse risk AL.…”

Section: Discussionmentioning

confidence: 99%

Patient‐based prediction algorithm of relapse after allo‐HSCT for acute Leukemia and its usefulness in the decision‐making process using a machine learning approach

et al. 2019

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Although allogeneic hematopoietic stem cell transplantation (allo‐HSCT) is a curative therapy for high‐risk acute leukemia (AL), some patients still relapse. Since patients simultaneously have many prognostic factors, difficulties are associated with the construction of a patient‐based prediction algorithm of relapse. The alternating decision tree (ADTree) is a successful classification method that combines decision trees with the predictive accuracy of boosting. It is a component of machine learning (ML) and has the capacity to simultaneously analyze multiple factors. Using ADTree, we attempted to construct a prediction model of leukemia relapse within 1 year of transplantation. With the model of training data (n = 148), prediction accuracy, the AUC of ROC, and the κ‐statistic value were 78.4%, 0.746, and 0.508, respectively. The false positive rate (FPR) of the relapse prediction was as low as 0.134. In an evaluation of the model with validation data (n = 69), prediction accuracy, AUC, and FPR of the relapse prediction were similar at 71.0%, 0.667, and 0.216, respectively. These results suggest that the model is generalized and highly accurate. Furthermore, the output of ADTree may visualize the branch point of treatment. For example, the selection of donor types resulted in different relapse predictions. Therefore, clinicians may change treatment options by referring to the model, thereby improving outcomes. The present results indicate that ML, such as ADTree, will contribute to the decision‐making process in the diversified allo‐HSCT field and be useful for preventing the relapse of leukemia.

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Image processing and machine learning in the morphological analysis of blood cells

Abstract: Although research is still needed, it is important to define screening strategies to exploit the potential of image-based automatic recognition systems integrated in the daily routine of laboratories along with other analysis methodologies.

Cited by 87 publications

References 30 publications

Optimizing morphology through blood cell image analysis

Optimizing morphology through blood cell image analysis

Machine learning applications in the diagnosis of leukemia: Current trends and future directions

Patient‐based prediction algorithm of relapse after allo‐HSCT for acute Leukemia and its usefulness in the decision‐making process using a machine learning approach

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