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

Background: Leukemia is the 11 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 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.