Integrating AI and ML in Myelodysplastic Syndrome Diagnosis: State-of-the-Art and Future Prospects.
Simple Summary: This paper aims to highlight the latest advancements in the application of artificial intelligence in the diagnosis of myelodysplastic syndrome. This research focuses on a group of blood disorders called Myelodysplastic Syndrome (MDS), which can potentially develop into a more severe...
| Publicado en: | Cancers Vol. 16; no. 1; pp. 65 - 80 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | review tables/charts Journal Article |
| Publicado: |
MDPI
Jan2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=174717508&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 174717508 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726694 B74B jtl: Cancers issn: 20726694 maglogo: N pubinfo: dt: Jan2024 vid: 16 iid: 1 pid: 97109 pub: MDPI artinfo: ui: 174717508 174717508 174717508 10.3390/cancers16010065 174717508 ppf: 65 ppct: 15 formats: tig: atl: Integrating AI and ML in Myelodysplastic Syndrome Diagnosis: State-of-the-Art and Future Prospects. aug: au: Elshoeibi, Amgad Mohamed Badr, Ahmed Elsayed, Basel Metwally, Omar Elshoeibi, Raghad Elhadary, Mohamed Ragab Elshoeibi, Ahmed Attya, Mohamed Amro Khadadah, Fatima Alshurafa, Awni Alhuraiji, Ahmad Yassin, Mohamed affil: College of Medicine, QU Health, Qatar University, Doha 2713, Qatar sug: subj: Myelodysplastic Syndromes Diagnosis Artificial Intelligence Methods Machine Learning Methods Early Diagnosis Methods Myelodysplastic Syndromes Complications Leukemia, Myeloid, Acute Prevention and Control Early Detection of Cancer Methods Prediction Models Psychometrics Disease Progression Flow Cytometry Bone Marrow Examination ab: Simple Summary: This paper aims to highlight the latest advancements in the application of artificial intelligence in the diagnosis of myelodysplastic syndrome. This research focuses on a group of blood disorders called Myelodysplastic Syndrome (MDS), which can potentially develop into a more severe condition called Acute Myeloid Leukemia (AML). Detecting MDS early is crucial, but the current methods are time-consuming and labor-intensive. We aim to explore how artificial intelligence (AI) and machine learning (ML) can make the diagnosis of MDS faster and more accurate. AI involves computer programs that can think like humans, and ML is a part of AI that helps computers learn patterns and make predictions. By using these technologies, doctors can improve how they diagnose MDS, leading to better treatment and outcomes for patients. Myelodysplastic syndrome (MDS) is composed of diverse hematological malignancies caused by dysfunctional stem cells, leading to abnormal hematopoiesis and cytopenia. Approximately 30% of MDS cases progress to acute myeloid leukemia (AML), a more aggressive disease. Early detection is crucial to intervene before MDS progresses to AML. The current diagnostic process for MDS involves analyzing peripheral blood smear (PBS), bone marrow sample (BMS), and flow cytometry (FC) data, along with clinical patient information, which is labor-intensive and time-consuming. Recent advancements in machine learning offer an opportunity for faster, automated, and accurate diagnosis of MDS. In this review, we aim to provide an overview of the current applications of AI in the diagnosis of MDS and highlight their advantages, disadvantages, and performance metrics. pubtype: Academic Journal doctype: review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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