Applications of Artificial Intelligence in Thalassemia: A Comprehensive Review.
Khaled FerihBasel ElsayedAmgad Mohamed ElshoeibiAhmed Adel ElsabaghMohamed Ragab ElhadaryAshraf SolimanMohammed AbdalgayoomMohamed YassinPublished in: Diagnostics (Basel, Switzerland) (2023)
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.
Keyphrases
- artificial intelligence
- iron deficiency
- machine learning
- sickle cell disease
- deep learning
- chronic kidney disease
- end stage renal disease
- big data
- newly diagnosed
- ejection fraction
- genome wide
- gene expression
- peritoneal dialysis
- dna methylation
- locally advanced
- prognostic factors
- radiation therapy
- replacement therapy