Ferih Khaled, Elsayed Basel, Elshoeibi Amgad M, Elsabagh Ahmed A, Elhadary Mohamed, Soliman Ashraf, Abdalgayoom Mohammed, Yassin Mohamed
College of Medicine, QU Health, Qatar University, Doha P.O. Box 2713, Qatar.
Hematology Section, Pediatrics Department, Hamad Medical Corporation (HMC), Doha P.O. Box 3050, Qatar.
Diagnostics (Basel). 2023 Apr 26;13(9):1551. doi: 10.3390/diagnostics13091551.
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.
地中海贫血是一种常染色体隐性遗传疾病,会影响血红蛋白结构的β或α亚基。地中海贫血被归类为低色素小细胞性贫血,通过对α和β基因进行基因检测可明确诊断地中海贫血。地中海贫血与其他导致小细胞低色素性贫血的疾病具有相似特征,尤其是缺铁性贫血(IDA)。因此,区分地中海贫血和小细胞贫血的其他病因对于帮助治疗患者很重要。基于全血细胞计数(CBC)参数使用不同的指标和算法来诊断地中海贫血。在本文中,我们综述了人工智能在辅助地中海贫血诊断和分类方面的有效性。