Integrating AI and ML in Myelodysplastic Syndrome Diagnosis: State-of-the-Art and Future Prospects

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

doi:10.3390/cancers16010065

Cancers

2023

DOI: 10.3390/cancers16010065

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Integrating AI and ML in Myelodysplastic Syndrome Diagnosis: State-of-the-Art and Future Prospects

Amgad Mohamed Elshoeibi,

Ahmed Badr,

Basel Elsayed

et al.

Abstract: 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 informa… Show more

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2024

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Cited by 3 publications

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Optimization of diagnosis and treatment of hematological diseases via artificial intelligence

Wang,

Huang,

et al. 2024

Front. Med.

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BackgroundOptimizing the diagnosis and treatment of hematological diseases is a challenging yet crucial research area. Effective treatment plans typically require the comprehensive integration of cell morphology, immunology, cytogenetics, and molecular biology. These plans also consider patient-specific factors such as disease stage, age, and genetic mutation status. With the advancement of artificial intelligence (AI), more “AI + medical” application models are emerging. In clinical practice, many AI-assisted systems have been successfully applied to the diagnosis and treatment of hematological diseases, enhancing precision and efficiency and offering valuable solutions for clinical practice.ObjectiveThis study summarizes the research progress of various AI-assisted systems applied in the clinical diagnosis and treatment of hematological diseases, with a focus on their application in morphology, immunology, cytogenetics, and molecular biology diagnosis, as well as prognosis prediction and treatment.MethodsUsing PubMed, Web of Science, and other network search engines, we conducted a literature search on studies from the past 5 years using the main keywords “artificial intelligence” and “hematological diseases.” We classified the clinical applications of AI systems according to the diagnosis and treatment. We outline and summarize the current advancements in AI for optimizing the diagnosis and treatment of hematological diseases, as well as the difficulties and challenges in promoting the standardization of clinical diagnosis and treatment in this field.ResultsAI can significantly shorten turnaround times, reduce diagnostic costs, and accurately predict disease outcomes through applications in image-recognition technology, genomic data analysis, data mining, pattern recognition, and personalized medicine. However, several challenges remain, including the lack of AI product standards, standardized data, medical–industrial collaboration, and the complexity and non-interpretability of AI systems. In addition, regulatory gaps can lead to data privacy issues. Therefore, more research and improvements are needed to fully leverage the potential of AI to promote standardization of the clinical diagnosis and treatment of hematological diseases.ConclusionOur results serve as a reference point for the clinical diagnosis and treatment of hematological diseases and the development of AI-assisted clinical diagnosis and treatment systems. We offer suggestions for further development of AI in hematology and standardization of clinical diagnosis and treatment.

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Optimization of diagnosis and treatment of hematological diseases via artificial intelligence

Wang,

Huang,

et al. 2024

Front. Med.

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Application of Pathomic Features for Differentiating Dysplastic Cells in Patients with Myelodysplastic Syndrome

Hong,

Jeong,

Park

et al. 2024

Bioengineering

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Myelodysplastic syndromes (MDSs) are a group of hematologic neoplasms accompanied by dysplasia of bone marrow (BM) hematopoietic cells with cytopenia. Recently, digitalized pathology and pathomics using computerized feature analysis have been actively researched for classifying and predicting prognosis in various tumors of hematopoietic tissues. This study analyzed the pathomic features of hematopoietic cells in BM aspiration smears of patients with MDS according to each hematopoietic cell lineage and dysplasia. We included 24 patients with an MDS and 21 with normal BM. The 12,360 hematopoietic cells utilized were to be classified into seven types: normal erythrocytes, normal granulocytes, normal megakaryocytes, dysplastic erythrocytes, dysplastic granulocytes, dysplastic megakaryocytes, and others. Four hundred seventy-six pathomic features quantifying cell intensity, shape, and texture were extracted from each segmented cell. After comparing the combination of feature selection and machine learning classifier methods using 5-fold cross-validation area under the receiver operating characteristic curve (AUROC), the quadratic discriminant analysis (QDA) with gradient boosting decision tree (AUROC = 0.63) and QDA with eXtreme gradient boosting (XGB) (AUROC = 0.64) showed a high AUROC combination. Through a feature selection process, 30 characteristics were further analyzed. Dysplastic erythrocytes and granulocytes showed lower median values on heatmap analysis compared to that of normal erythrocytes and granulocytes. The data suggest that pathomic features could be applied to cell differentiation in hematologic malignancies. It could be used as a new biomarker with an auxiliary role for more accurate diagnosis. Further studies including prediction survival and prognosis with larger cohort of patients are needed.

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Advances in Microflow Cytometry-Based Molecular Detection Methods for Improved Future MDS Cancer Diagnosis

Gonsalves,

Escobar,

Altarabishi

et al. 2024

CIMB

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Myelodysplastic syndromes (MDS) are a rare form of early-stage blood cancer that typically leads to leukemia and other deadly complications. The typical diagnosis for MDS involves a mixture of blood tests, a bone marrow biopsy, and genetic analysis. Flow cytometry has commonly been used to analyze these types of samples, yet there still seems to be room for advancement in several areas, such as the limit of detection, turnaround time, and cost. This paper explores recent advancements in microflow cytometry technology and how it may be used to supplement conventional methods of diagnosing blood cancers, such as MDS and leukemia, through flow cytometry. Microflow cytometry, a more recent adaptation of the well-researched and conventional flow cytometry techniques, integrated with microfluidics, demonstrates significant potential in addressing many of the shortcomings flow cytometry faces when diagnosing a blood-related disease such as MDS. The benefits that this platform brings, such as portability, processing speed, and operating cost, exemplify the importance of exploring microflow cytometry as a point-of-care (POC) diagnostic device for MDS and other forms of blood cancer.

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Integrating AI and ML in Myelodysplastic Syndrome Diagnosis: State-of-the-Art and Future Prospects

Cited by 3 publications

References 57 publications

Optimization of diagnosis and treatment of hematological diseases via artificial intelligence

Optimization of diagnosis and treatment of hematological diseases via artificial intelligence

Application of Pathomic Features for Differentiating Dysplastic Cells in Patients with Myelodysplastic Syndrome

Advances in Microflow Cytometry-Based Molecular Detection Methods for Improved Future MDS Cancer Diagnosis

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