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. 2025 Jan 17:7:1402926.
doi: 10.3389/fdata.2024.1402926. eCollection 2024.

Artificial intelligence for the detection of acute myeloid leukemia from microscopic blood images; a systematic review and meta-analysis

Feras Al-Obeidat  1 , Wael Hafez  2   3 , Asrar Rashid  3 , Mahir Khalil Jallo  4 , Munier Gador  3 , Ivan Cherrez-Ojeda  5   6 , Daniel Simancas-Racines  7
Affiliations

Artificial intelligence for the detection of acute myeloid leukemia from microscopic blood images; a systematic review and meta-analysis

Feras Al-Obeidat et al. Front Big Data. .

Abstract

Background: Leukemia is the 11th 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 I2 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.

Keywords: acute myeloid leukemia; artificial intelligence; blood images; machine learning; meta-analysis; neural networks.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The Preferred reporting items for systematic reviews and meta-analyses (PRISMA) 2020 flow chart depicting the screening process for included studies.
Figure 2
Figure 2
Quality Assessment of included studies using QUADAS-2 tool.
Figure 3
Figure 3
Forest plot for analyzing the accuracy of the different models used across the studies. CI, confidence interval.
Figure 4
Figure 4
Forest plot for analyzing the sensitivity of the different models used across the studies. CI, confidence interval.
Figure 5
Figure 5
Precision funnel plot of the estimated effects from studies on artificial intelligence model performance accuracy.
Figure 6
Figure 6
Precision funnel plot of the estimated effects from studies on artificial intelligence model performance sensitivity.
See this image and copyright information in PMC

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