Artificial intelligence for the detection of acute myeloid leukemia from microscopic blood images; a systematic review and meta-analysis Article Swipe
Feras Al‐Obeidat
,
Wael Hafez
,
Asrar Rashid
,
Mahir Jallo
,
Munier Gador
,
Iván Chérrez-Ojeda
,
Daniel Simancas‐Racines
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fdata.2024.1402926
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fdata.2024.1402926
Background Leukemia is the 11 th most prevalent type of cancer worldwide, with acute myeloid leukemia (AML) being the most frequent malignant blood malignancy in adults. Microscopic blood tests are the most common methods for identifying leukemia subtypes. An automated optical image-processing system using artificial intelligence (AI) has recently been applied to facilitate clinical decision-making. Aim To evaluate the performance of all AI-based approaches for the detection and diagnosis of acute myeloid leukemia (AML). Methods Medical databases including PubMed, Web of Science, and Scopus were searched until December 2023. We used the “metafor” and “metagen” libraries in R to analyze the different models used in the studies. Accuracy and sensitivity were the primary outcome measures. Results Ten studies were included in our review and meta-analysis, conducted between 2016 and 2023. Most deep-learning models have been utilized, including convolutional neural networks (CNNs). The common- and random-effects models had accuracies of 1.0000 [0.9999; 1.0001] and 0.9557 [0.9312, and 0.9802], respectively. The common and random effects models had high sensitivity values of 1.0000 and 0.8581, respectively, indicating that the machine learning models in this study can accurately detect true-positive leukemia cases. Studies have shown substantial variations in accuracy and sensitivity, as shown by the Q values and I 2 statistics. Conclusion Our systematic review and meta-analysis found an overall high accuracy and sensitivity of AI models in correctly identifying true-positive AML cases. Future research should focus on unifying reporting methods and performance assessment metrics of AI-based diagnostics. Systematic review registration https://www.crd.york.ac.uk/prospero/#recordDetails , CRD42024501980.
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- https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2024.1402926/pdf
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All OpenAlex metadata
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https://openalex.org/W4406541874Canonical identifier for this work in OpenAlex
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Artificial intelligence for the detection of acute myeloid leukemia from microscopic blood images; a systematic review and meta-analysisWork title
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reviewOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-01-17Full publication date if available
- Authors
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Feras Al‐Obeidat, Wael Hafez, Asrar Rashid, Mahir Jallo, Munier Gador, Iván Chérrez-Ojeda, Daniel Simancas‐RacinesList of authors in order
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https://doi.org/10.3389/fdata.2024.1402926Publisher landing page
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https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2024.1402926/pdfDirect link to full text PDF
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https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2024.1402926/pdfDirect OA link when available
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Myeloid leukemia, Meta-analysis, Medicine, Pathology, Internal medicineTop concepts (fields/topics) attached by OpenAlex
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