ReviewDiagnostics (Basel, Switzerland)2023
Applications of Artificial Intelligence in Thalassemia: A Comprehensive Review.
Review in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 33 citations in OpenAlex.
- Diagnosis and classification of thalassemia disease using machine learning: Comparative analysis of traditional models and a novel hybrid approach.Technology and health care : official journal of the European Society for Engineering and Medicine · 2026Article
- Thalassaemia.Nature reviews. Disease primers · 2026Review
- Application of targeted sequencing in the molecular diagnosis of thalassemia in Southern China.Scientific reports · 2026Article
- Diagnostic performance of discriminant formulas and machine learning models for detecting β-thalassemia trait in Bangladesh.PloS one · 2026Article
- Exploring the determinants of anemia in young adult women (20-24 years): the role of AI-based interventions in health management.Frontiers in artificial intelligence · 2026Article
- Artificial Intelligence in Haematologic Diagnostics: Current Applications and Future Perspectives.Acta haematologica · 2026Review
- Prediction ofFrontiers in medicine · 2026Article
- A New Method for Screening Thalassemia Patients Using Mid-Infrared Spectroscopy.Diagnostics (Basel, Switzerland) · 2025Article
- From CBC to clarity: Interpretable detection of beta-thalassemia carriers in imbalanced datasets.PloS one · 2025Article
- The Importance and Essentiality of Natural and Synthetic Chelators in Medicine: Increased Prospects for the Effective Treatment of Iron Overload and Iron Deficiency.International journal of molecular sciences · 2024Review
- Integrating AI and ML in Myelodysplastic Syndrome Diagnosis: State-of-the-Art and Future Prospects.Cancers · 2023Review
- Artificial Intelligence-Assisted Diagnostic Cytology and Genomic Testing for Hematologic Disorders.Cells · 2023Review
- Differentinating between non-transfusion dependant β-thalassemia and iron deficinecy anemia in children using ROC and logistic regression analysis: two novel discrimination indices designed for pediatric patients.Frontiers in pediatrics · 2023Article
- Deep learning enhances acute lymphoblastic leukemia diagnosis and classification using bone marrow images.Frontiers in oncology · 2023Review
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Thalassemia is an autosomal recessive genetic disorder that affects the beta or alpha subunits of the hemoglobin structure. Thalassemia is classified as a hypochromic microcytic anemia and a definitive diagnosis of thalassemia is made by genetic testing of the alpha and beta genes. Thalassemia carries similar features to the other diseases that lead to microcytic hypochromic anemia, particularly iron deficiency anemia (IDA). Therefore, distinguishing between thalassemia and other causes of microcytic anemia is important to help in the treatment of the patients. Different indices and algorithms are used based on the complete blood count (CBC) parameters to diagnose thalassemia. In this article, we review how effective artificial intelligence is in aiding in the diagnosis and classification of thalassemia.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.