Using Artificial Intelligence to Enhance Myelodysplastic Syndrome Diagnosis, Prognosis, and Treatment
Myelodysplastic syndromes represent a group of hematological neoplastic diseases caused by defective stem cells causing cytopenia and abnormal hematopoiesis. More than 30% of myelodysplastic syndrome cases develop into acute myeloid leukemia. An analysis of bone marrow samples, peripheral blood smea...
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MDPI AG
2025-03-01
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| Series: | Biomedicines |
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| Online Access: | https://www.mdpi.com/2227-9059/13/4/835 |
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| author | Fabio Stagno Giuseppe Mirabile Patricia Rizzotti Adele Bottaro Antonio Pagana Sebastiano Gangemi Alessandro Allegra |
| author_facet | Fabio Stagno Giuseppe Mirabile Patricia Rizzotti Adele Bottaro Antonio Pagana Sebastiano Gangemi Alessandro Allegra |
| author_sort | Fabio Stagno |
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| description | Myelodysplastic syndromes represent a group of hematological neoplastic diseases caused by defective stem cells causing cytopenia and abnormal hematopoiesis. More than 30% of myelodysplastic syndrome cases develop into acute myeloid leukemia. An analysis of bone marrow samples, peripheral blood smears, multiparametric flow cytometry data, and clinical patient information is part of the current, time-consuming, and labor-intensive work up for myelodysplastic syndromes. Nowadays, clinical biomedical research has been transformed by the advent of artificial intelligence, specifically machine learning. Artificial intelligence (AI) can improve risk assessment and diagnosis, as well as boost the precision of clinical outcome prediction and illness classification. Algorithms based on artificial intelligence may be potentially helpful in discovering new needs for myelodysplastic syndrome-affected patients, choosing treatment and assessing minimal residual disease. In this review, we seek to identify the primary mechanisms and uses of artificial intelligence in myelodysplastic syndrome, pointing out its advantages and disadvantages while discussing the possible benefits of using AI pipelines in a therapeutic setting. |
| format | Article |
| id | doaj-art-f53dbbc26ecc4baa959cb082a08b491d |
| institution | OA Journals |
| issn | 2227-9059 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomedicines |
| spelling | doaj-art-f53dbbc26ecc4baa959cb082a08b491d2025-08-20T02:28:19ZengMDPI AGBiomedicines2227-90592025-03-0113483510.3390/biomedicines13040835Using Artificial Intelligence to Enhance Myelodysplastic Syndrome Diagnosis, Prognosis, and TreatmentFabio Stagno0Giuseppe Mirabile1Patricia Rizzotti2Adele Bottaro3Antonio Pagana4Sebastiano Gangemi5Alessandro Allegra6Division of Hematology, AOU Policlinico “G. Martino”, Department of Human Pathology in Adulthood and Childhood “Gaetano Barresi”, University of Messina, Via Consolare Valeria 1, 98125 Messina, ItalyDivision of Hematology, AOU Policlinico “G. Martino”, Department of Human Pathology in Adulthood and Childhood “Gaetano Barresi”, University of Messina, Via Consolare Valeria 1, 98125 Messina, ItalyDivision of Hematology, AOU Policlinico “G. Martino”, Department of Human Pathology in Adulthood and Childhood “Gaetano Barresi”, University of Messina, Via Consolare Valeria 1, 98125 Messina, ItalyDivision of Hematology, AOU Policlinico “G. Martino”, Department of Human Pathology in Adulthood and Childhood “Gaetano Barresi”, University of Messina, Via Consolare Valeria 1, 98125 Messina, ItalyDivision of Hematology, AOU Policlinico “G. Martino”, Department of Human Pathology in Adulthood and Childhood “Gaetano Barresi”, University of Messina, Via Consolare Valeria 1, 98125 Messina, ItalyAllergy and Clinical Immunology Unit, Department of Clinical and Experimental Medicine, University of Messina, Via Consolare Valeria, 98125 Messina, ItalyDivision of Hematology, AOU Policlinico “G. Martino”, Department of Human Pathology in Adulthood and Childhood “Gaetano Barresi”, University of Messina, Via Consolare Valeria 1, 98125 Messina, ItalyMyelodysplastic syndromes represent a group of hematological neoplastic diseases caused by defective stem cells causing cytopenia and abnormal hematopoiesis. More than 30% of myelodysplastic syndrome cases develop into acute myeloid leukemia. An analysis of bone marrow samples, peripheral blood smears, multiparametric flow cytometry data, and clinical patient information is part of the current, time-consuming, and labor-intensive work up for myelodysplastic syndromes. Nowadays, clinical biomedical research has been transformed by the advent of artificial intelligence, specifically machine learning. Artificial intelligence (AI) can improve risk assessment and diagnosis, as well as boost the precision of clinical outcome prediction and illness classification. Algorithms based on artificial intelligence may be potentially helpful in discovering new needs for myelodysplastic syndrome-affected patients, choosing treatment and assessing minimal residual disease. In this review, we seek to identify the primary mechanisms and uses of artificial intelligence in myelodysplastic syndrome, pointing out its advantages and disadvantages while discussing the possible benefits of using AI pipelines in a therapeutic setting.https://www.mdpi.com/2227-9059/13/4/835artificial intelligencemachine learningmyelodysplastic syndromesMDSdiagnosisflow cytometry |
| spellingShingle | Fabio Stagno Giuseppe Mirabile Patricia Rizzotti Adele Bottaro Antonio Pagana Sebastiano Gangemi Alessandro Allegra Using Artificial Intelligence to Enhance Myelodysplastic Syndrome Diagnosis, Prognosis, and Treatment Biomedicines artificial intelligence machine learning myelodysplastic syndromes MDS diagnosis flow cytometry |
| title | Using Artificial Intelligence to Enhance Myelodysplastic Syndrome Diagnosis, Prognosis, and Treatment |
| title_full | Using Artificial Intelligence to Enhance Myelodysplastic Syndrome Diagnosis, Prognosis, and Treatment |
| title_fullStr | Using Artificial Intelligence to Enhance Myelodysplastic Syndrome Diagnosis, Prognosis, and Treatment |
| title_full_unstemmed | Using Artificial Intelligence to Enhance Myelodysplastic Syndrome Diagnosis, Prognosis, and Treatment |
| title_short | Using Artificial Intelligence to Enhance Myelodysplastic Syndrome Diagnosis, Prognosis, and Treatment |
| title_sort | using artificial intelligence to enhance myelodysplastic syndrome diagnosis prognosis and treatment |
| topic | artificial intelligence machine learning myelodysplastic syndromes MDS diagnosis flow cytometry |
| url | https://www.mdpi.com/2227-9059/13/4/835 |
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