Applications of Machine Learning in Chronic Myeloid Leukemia
Chronic myeloid leukemia (CML) is a myeloproliferative neoplasm characterized by dysregulated growth and the proliferation of myeloid cells in the bone marrow caused by the BCR-ABL1 fusion gene. Clinically, CML demonstrates an increased production of mature and maturing granulocytes, mainly neutroph...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/7/1330 |
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| author | Mohamed Elhadary Ahmed Adel Elsabagh Khaled Ferih Basel Elsayed Amgad M. Elshoeibi Rasha Kaddoura Susanna Akiki Khalid Ahmed Mohamed Yassin |
| author_facet | Mohamed Elhadary Ahmed Adel Elsabagh Khaled Ferih Basel Elsayed Amgad M. Elshoeibi Rasha Kaddoura Susanna Akiki Khalid Ahmed Mohamed Yassin |
| author_sort | Mohamed Elhadary |
| collection | DOAJ |
| description | Chronic myeloid leukemia (CML) is a myeloproliferative neoplasm characterized by dysregulated growth and the proliferation of myeloid cells in the bone marrow caused by the BCR-ABL1 fusion gene. Clinically, CML demonstrates an increased production of mature and maturing granulocytes, mainly neutrophils. When a patient is suspected to have CML, peripheral blood smears and bone marrow biopsies may be manually examined by a hematologist. However, confirmatory testing for the BCR-ABL1 gene is still needed to confirm the diagnosis. Despite tyrosine kinase inhibitors (TKIs) being the mainstay of treatment for patients with CML, different agents should be used in different patients given their stage of disease and comorbidities. Moreover, some patients do not respond well to certain agents and some need more aggressive courses of therapy. Given the innovations and development that machine learning (ML) and artificial intelligence (AI) have undergone over the years, multiple models and algorithms have been put forward to help in the assessment and treatment of CML. In this review, we summarize the recent studies utilizing ML algorithms in patients with CML. The search was conducted on the PubMed/Medline and Embase databases and yielded 66 full-text articles and abstracts, out of which 11 studies were included after screening against the inclusion criteria. The studies included show potential for the clinical implementation of ML models in the diagnosis, risk assessment, and treatment processes of patients with CML. |
| first_indexed | 2024-03-11T05:40:06Z |
| format | Article |
| id | doaj.art-a6aaecdbb5114ae0b88ee682c7ac5e78 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T05:40:06Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-a6aaecdbb5114ae0b88ee682c7ac5e782023-11-17T16:31:05ZengMDPI AGDiagnostics2075-44182023-04-01137133010.3390/diagnostics13071330Applications of Machine Learning in Chronic Myeloid LeukemiaMohamed Elhadary0Ahmed Adel Elsabagh1Khaled Ferih2Basel Elsayed3Amgad M. Elshoeibi4Rasha Kaddoura5Susanna Akiki6Khalid Ahmed7Mohamed Yassin8College of Medicine, QU Health, Qatar University, Doha 2713, QatarCollege of Medicine, QU Health, Qatar University, Doha 2713, QatarCollege of Medicine, QU Health, Qatar University, Doha 2713, QatarCollege of Medicine, QU Health, Qatar University, Doha 2713, QatarCollege of Medicine, QU Health, Qatar University, Doha 2713, QatarPharmacy Department, Heart Hospital, Hamad Medical Corporation (HMC), Doha 3050, QatarDiagnostic Genomic Division, Hamad Medical Corporation (HMC), Doha 3050, QatarDepartment of Hematology, National Center for Cancer Care and Research (NCCCR), Hamad Medical Corporation (HMC), Doha 3050, QatarHematology Section, Medical Oncology, National Center for Cancer Care and Research (NCCCR), Hamad Medical Corporation (HMC), Doha 3050, QatarChronic myeloid leukemia (CML) is a myeloproliferative neoplasm characterized by dysregulated growth and the proliferation of myeloid cells in the bone marrow caused by the BCR-ABL1 fusion gene. Clinically, CML demonstrates an increased production of mature and maturing granulocytes, mainly neutrophils. When a patient is suspected to have CML, peripheral blood smears and bone marrow biopsies may be manually examined by a hematologist. However, confirmatory testing for the BCR-ABL1 gene is still needed to confirm the diagnosis. Despite tyrosine kinase inhibitors (TKIs) being the mainstay of treatment for patients with CML, different agents should be used in different patients given their stage of disease and comorbidities. Moreover, some patients do not respond well to certain agents and some need more aggressive courses of therapy. Given the innovations and development that machine learning (ML) and artificial intelligence (AI) have undergone over the years, multiple models and algorithms have been put forward to help in the assessment and treatment of CML. In this review, we summarize the recent studies utilizing ML algorithms in patients with CML. The search was conducted on the PubMed/Medline and Embase databases and yielded 66 full-text articles and abstracts, out of which 11 studies were included after screening against the inclusion criteria. The studies included show potential for the clinical implementation of ML models in the diagnosis, risk assessment, and treatment processes of patients with CML.https://www.mdpi.com/2075-4418/13/7/1330artificial intelligencechronic myeloid leukemiamachine learningconvolutional neural networkshemoglobinopathies |
| spellingShingle | Mohamed Elhadary Ahmed Adel Elsabagh Khaled Ferih Basel Elsayed Amgad M. Elshoeibi Rasha Kaddoura Susanna Akiki Khalid Ahmed Mohamed Yassin Applications of Machine Learning in Chronic Myeloid Leukemia Diagnostics artificial intelligence chronic myeloid leukemia machine learning convolutional neural networks hemoglobinopathies |
| title | Applications of Machine Learning in Chronic Myeloid Leukemia |
| title_full | Applications of Machine Learning in Chronic Myeloid Leukemia |
| title_fullStr | Applications of Machine Learning in Chronic Myeloid Leukemia |
| title_full_unstemmed | Applications of Machine Learning in Chronic Myeloid Leukemia |
| title_short | Applications of Machine Learning in Chronic Myeloid Leukemia |
| title_sort | applications of machine learning in chronic myeloid leukemia |
| topic | artificial intelligence chronic myeloid leukemia machine learning convolutional neural networks hemoglobinopathies |
| url | https://www.mdpi.com/2075-4418/13/7/1330 |
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