Artificial intelligence for the detection of acute myeloid leukemia from microscopic blood images; a systematic review and meta-analysis - Al-Obeidat, Feras; Hafez, Wael; Rashid, Asrar; Jallo, Mahir Khalil; Gador, Munier; Cherrez-Ojeda, Ivan; Simancas-Racines, Daniel (Frontiers in Big Data, 2025)
BackgroundLeukemia 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.AimTo evaluate the performance of all AI-based approaches for the detection and diagnosis of acute myeloid leukemia (AML).MethodsMedical 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.ResultsTen 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.ConclusionOur 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 registrationhttps://www.crd.york.ac.uk/prospero/#recordDetails, CRD42024501980.