Machine learning algorithms for accurate differential diagnosis of lymphocytosis based on cell population data

Bigorra, Laura; Larriba, Iciar; Gutiérrez-Gallego, Ricardo

doi:10.1111/bjh.15230

Cited by 16 publications

(18 citation statements)

References 8 publications

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“…In a study using CBC test data, three data mining methods: association rules, rule induction and deep learning were tested and the results showed that the deep learning classifier with the best ability for predicting tumors from blood diseases with an accuracy of 79.45%, with the limitation of no explanation of results 31 . Another related study 32 used machine learning algorithm to differentiate lymphoid classification using CPD parameters from 3 cohorts: healthy control, viral infection and chronic lymphocytic leukemia. In that study, the best result came from Neural Networks classifier with an accuracy of 98.7% followed by SVM 98.0% and KNN 98.0% 32 .…”

Section: Discussionmentioning

confidence: 99%

“…Another related study 32 used machine learning algorithm to differentiate lymphoid classification using CPD parameters from 3 cohorts: healthy control, viral infection and chronic lymphocytic leukemia. In that study, the best result came from Neural Networks classifier with an accuracy of 98.7% followed by SVM 98.0% and KNN 98.0% 32 . A recent study using CPD showed Random Forest algorithm as the best model with two www.nature.com/scientificreports www.nature.com/scientificreports/ practices, using all parameters and reduced parameters.…”

Section: Discussionmentioning

confidence: 99%

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Artificial Intelligence based Models for Screening of Hematologic Malignancies using Cell Population Data

Syed-Abdul

Firdani

Chung

et al. 2020

Sci Rep

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Cell Population Data (CPD) provides various blood cell parameters that can be used for differential diagnosis. Data analytics using Machine Learning (ML) have been playing a pivotal role in revolutionizing medical diagnostics. This research presents a novel approach of using ML algorithms for screening hematologic malignancies using CPD. The data collection was done at Konkuk University Medical Center, Seoul. A total of (882 cases: 457 hematologic malignancy and 425 hematologic nonmalignancy) were used for analysis. In our study, seven machine learning models, i.e., SGD, SVM, RF, DT, Linear model, Logistic regression, and ANN, were used. In order to measure the performance of our ML models, stratified 10-fold cross validation was performed, and metrics, such as accuracy, precision, recall, and AUC were used. We observed outstanding performance by the ANN model as compared to other ML models. The diagnostic ability of ANN achieved the highest accuracy, precision, recall, and AUC ± Standard Deviation as follows: 82.8%, 82.8%, 84.9%, and 93.5% ± 2.6 respectively. ANN algorithm based on CPD appeared to be an efficient aid for clinical laboratory screening of hematologic malignancies. Our results encourage further work of applying ML to wider field of clinical practice.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence based Models for Screening of Hematologic Malignancies using Cell Population Data

Syed-Abdul

Firdani

Chung

et al. 2020

Sci Rep

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“…More generally, we note that the present imaging cytometry platform integrated with artificial intelligence-aided image processing would allow multiparametric quantification of cell populations with unrivaled throughput and resolution and facilitate the identification of cell characteristics relevant for sample classification in a range of molecular diagnostic strategies, such as morphology-based diagnosis of rare cells in blood diseases (Brereton et al, 2015;Bigorra et al, 2019) or circulating tumor cells (Ogle et al, 2016). In addition, our platform is ideally suited for the classification of membrane-less organelles on the basis of their localization and composition inside cells.…”

Section: Discussionmentioning

confidence: 99%

High-throughput multiparametric imaging flow cytometry: toward diffraction-limited sub-cellular detection and monitoring of sub-cellular processes

et al. 2021

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“…All programs showed high predictability and versatility . AI and ML have also been applied in the following fields: the morphological analysis of blood cells, the identification of prognostic factors of ALL in childhood, and the differential diagnosis of hematological diseases …”

Section: Discussionmentioning

confidence: 99%

“…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. [3][4][5] To improve outcomes, attempts are being made to develop strategies that reduce the risk of relapse.…”

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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Machine learning algorithms for accurate differential diagnosis of lymphocytosis based on cell population data

Cited by 16 publications

References 8 publications

Artificial Intelligence based Models for Screening of Hematologic Malignancies using Cell Population Data

Artificial Intelligence based Models for Screening of Hematologic Malignancies using Cell Population Data

High-throughput multiparametric imaging flow cytometry: toward diffraction-limited sub-cellular detection and monitoring of sub-cellular processes

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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