TT@MHA: A machine learning-based webpage tool for discriminating thalassemia trait from microcytic hypochromic anemia patients

Zhang, Fan; Yang, Jing; Wang, Yan; Cai, Manyi; Ouyang, Jian; Li, Junxun

doi:10.1016/j.cca.2023.117368

Cited by 7 publications

(4 citation statements)

References 25 publications

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“…This is particularly beneficial for improving the diagnosis and treatment efficiency in regions with relatively limited medical resources. 7 The TT@Normal prediction model performed excellently in both the training and validation sets. The weight feature map of the TT@Normal model showed that the top three weights in the TT@Normal model were those of the target cells, microcytes, and teardrop cells.…”

Section: Discussionmentioning

confidence: 92%

“…In particular, ML has an incomparable advantage in large-scale data analysis. 7,23,24 The DxAI platform harnesses ML for data analysis, employing networks to connect constructed models and achieve model visualization and result sharing. This is particularly beneficial for improving the diagnosis and treatment efficiency in regions with relatively limited medical resources.…”

Section: Discussionmentioning

confidence: 99%

“…However, this method is expensive; it demands significant laboratory resources and trained personnel, making it impractical for implementation in underdeveloped regions. 6,7 Many non-anemic people in remote and less-developed regions do not undergo prenatal screening. 3,6 Therefore, to reduce the chances of misdiagnosis, there is a need to introduce new screening methods to efficiently identify TT carriers with routine tests.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing thalassemia gene carrier identification in non‐anemic populations using artificial intelligence erythrocyte morphology analysis and machine learning

Zhang,

Zhan,

Wang

et al. 2023

European J of Haematology

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BackgroundNon‐anemic thalassemia trait (TT) accounted for a high proportion of TT cases in South China.ObjectiveTo use artificial intelligence (AI) analysis of erythrocyte morphology and machine learning (ML) to identify TT gene carriers in a non‐anemic population.MethodsDigital morphological data from 76 TT gene carriers and 97 controls were collected. The AI technology‐based Mindray MC‐100i was used to quantitatively analyze the percentage of abnormal erythrocytes. Further, ML was used to construct a prediction model.ResultsNon‐anemic TT carriers accounted for over 60% of the TT cases. Random Forest was selected as the prediction model and named TT@Normal. The TT@Normal algorithm showed outstanding performance in the training, validation, and external validation sets and could efficiently identify TT carriers in the non‐anemic population. The top three weights in the TT@Normal model were the target cells, microcytes, and teardrop cells. Elevated percentages of abnormal erythrocytes should raise a strong suspicion of being a TT gene carrier. TT@Normal could be promoted and used as a visualization and sharing tool. It is accessible through a URL link and can be used by medical staff online to predict the possibility of TT gene carriage in a non‐anemic population.ConclusionsThe ML‐based model TT@Normal could efficiently identify TT carriers in non‐anemic people. Elevated percentages of target cells, microcytes, and teardrop cells should raise a strong suspicion of being a TT gene carrier.

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Section: Discussionmentioning

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhancing thalassemia gene carrier identification in non‐anemic populations using artificial intelligence erythrocyte morphology analysis and machine learning

Zhang,

Zhan,

Wang

et al. 2023

European J of Haematology

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“…TT patients out of a total collection of 798 patients with MHA had a high number of TT (43.33%) and TT simultaneous with IDA (TT&IDA) patients (14.04%). To form a discriminant model, five ML algorithms are used: L-SVC, XGB, SVM, RF, and LR [73]. The information and links for the online thalassemia application are included in Table 4.…”

Section: Thalassemia Applicationsmentioning

confidence: 99%

Predicting Thalassemia Using Feature Selection Techniques: A Comparative Analysis

Saleem,

Aslam,

Lali

et al. 2023

Diagnostics

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Thalassemia represents one of the most common genetic disorders worldwide, characterized by defects in hemoglobin synthesis. The affected individuals suffer from malfunctioning of one or more of the four globin genes, leading to chronic hemolytic anemia, an imbalance in the hemoglobin chain ratio, iron overload, and ineffective erythropoiesis. Despite the challenges posed by this condition, recent years have witnessed significant advancements in diagnosis, therapy, and transfusion support, significantly improving the prognosis for thalassemia patients. This research empirically evaluates the efficacy of models constructed using classification methods and explores the effectiveness of relevant features that are derived using various machine-learning techniques. Five feature selection approaches, namely Chi-Square (χ2), Exploratory Factor Score (EFS), tree-based Recursive Feature Elimination (RFE), gradient-based RFE, and Linear Regression Coefficient, were employed to determine the optimal feature set. Nine classifiers, namely K-Nearest Neighbors (KNN), Decision Trees (DT), Gradient Boosting Classifier (GBC), Linear Regression (LR), AdaBoost, Extreme Gradient Boosting (XGB), Random Forest (RF), Light Gradient Boosting Machine (LGBM), and Support Vector Machine (SVM), were utilized to evaluate the performance. The χ2 method achieved accuracy, registering 91.56% precision, 91.04% recall, and 92.65% f-score when aligned with the LR classifier. Moreover, the results underscore that amalgamating over-sampling with Synthetic Minority Over-sampling Technique (SMOTE), RFE, and 10-fold cross-validation markedly elevates the detection accuracy for αT patients. Notably, the Gradient Boosting Classifier (GBC) achieves 93.46% accuracy, 93.89% recall, and 92.72% F1 score.

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Machine Learning-Based Prediction of Hemoglobinopathies Using Complete Blood Count Data

Schipper,

Rutten,

van Gammeren

et al. 2024

Clinical Chemistry

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Background Hemoglobinopathies, the most common inherited blood disorder, are frequently underdiagnosed. Early identification of carriers is important for genetic counseling of couples at risk. The aim of this study was to develop and validate a novel machine learning model on a multicenter data set, covering a wide spectrum of hemoglobinopathies based on routine complete blood count (CBC) testing. Methods Hemoglobinopathy test results from 10 322 adults were extracted retrospectively from 8 Dutch laboratories. eXtreme Gradient Boosting (XGB) and logistic regression models were developed to differentiate negative from positive hemoglobinopathy cases, using 7 routine CBC parameters. External validation was conducted on a data set from an independent Dutch laboratory, with an additional external validation on a Spanish data set (n = 2629) specifically for differentiating thalassemia from iron deficiency anemia (IDA). Results The XGB and logistic regression models achieved an area under the receiver operating characteristic (AUROC) of 0.88 and 0.84, respectively, in distinguishing negative from positive hemoglobinopathy cases in the independent external validation set. Subclass analysis showed that the XGB model reached an AUROC of 0.97 for β-thalassemia, 0.98 for α0-thalassemia, 0.95 for homozygous α+-thalassemia, 0.78 for heterozygous α+-thalassemia, and 0.94 for the structural hemoglobin variants Hemoglobin C, Hemoglobin D, Hemoglobin E. Both models attained AUROCs of 0.95 in differentiating IDA from thalassemia. Conclusions Both the XGB and logistic regression model demonstrate high accuracy in predicting a broad range of hemoglobinopathies and are effective in differentiating hemoglobinopathies from IDA. Integration of these models into the laboratory information system facilitates automated hemoglobinopathy detection using routine CBC parameters.

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TT@MHA: A machine learning-based webpage tool for discriminating thalassemia trait from microcytic hypochromic anemia patients

Cited by 7 publications

References 25 publications

Enhancing thalassemia gene carrier identification in non‐anemic populations using artificial intelligence erythrocyte morphology analysis and machine learning

Enhancing thalassemia gene carrier identification in non‐anemic populations using artificial intelligence erythrocyte morphology analysis and machine learning

Predicting Thalassemia Using Feature Selection Techniques: A Comparative Analysis

Machine Learning-Based Prediction of Hemoglobinopathies Using Complete Blood Count Data

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