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SYSTEMATIC REVIEW article

Front. Big Data
Sec. Medicine and Public Health
Volume 7 - 2024 | doi: 10.3389/fdata.2024.1402926
This article is part of the Research Topic Cross-Modal Learning in Medicine: Bridging Large Language Models with Medical Image Analysis View all 4 articles

Artificial Intelligence for the detection of Acute Myeloid Leukemia from microscopic blood images; A Systematic Review and Meta-analysis

Provisionally accepted
Feras Al-Obeidat Feras Al-Obeidat 1*Wael Hafez Wael Hafez 2*Asrar Rashid Asrar Rashid 3Mahir Khalil Khalil Mahir Khalil Khalil 4Ivan Cherrez-Ojeda Ivan Cherrez-Ojeda 5
  • 1 Zayed University, Abu Dhabi, United Arab Emirates
  • 2 National Research Centre (Egypt), Cairo, Cairo, Egypt
  • 3 NMC Royal Hospital, Abu Dhabi, United Arab Emirates
  • 4 Gulf Medical University, Ajman, Ajman, United Arab Emirates
  • 5 Pontificia Universidad Católica del Ecuador, Quito, Pichincha, Ecuador

The final, formatted version of the article will be published soon.

    Background: Leukemia is the eleventh 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.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.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.

    Keywords: artificial intelligence, Acute Myeloid Leukemia, blood images, machine learning, neural networks, Meta-analysis

    Received: 18 Mar 2024; Accepted: 23 Dec 2024.

    Copyright: © 2024 Al-Obeidat, Hafez, Rashid, Khalil and Cherrez-Ojeda. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Feras Al-Obeidat, Zayed University, Abu Dhabi, United Arab Emirates
    Wael Hafez, National Research Centre (Egypt), Cairo, 12622, Cairo, Egypt

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