KEY TAKEAWAYS
- The study aimed to explore AI’s role in predicting CML progression and optimizing treatment.
- Researchers noticed AI’s potential to significantly improve prediction, prognosis, and personalized treatment in CML management.
Navigating the complexity of chronic myeloid leukemia (CML) diagnosis and management poses significant challenges, including the need for accurate prediction of disease progression and response to treatment.
Artificial intelligence (AI) presents a transformative approach that enables the development of sophisticated predictive models and personalized treatment strategies that enhance early detection and improve therapeutic interventions for better patient outcomes.
Malihe Ram and the team aimed to assess the application of AI in CML management by evaluating its effectiveness in predicting disease progression and optimizing treatment protocols.
They performed an inclusive analysis by conducting an extensive search for relevant articles from PubMed, Scopus, and Web of Science databases up to April 24, 2023. Data were collected using a standardized extraction form, and the results were presented in tables and graphs, showing frequencies and percentages. The authors adhered to the PRISMA-ScR checklist to ensure transparent reporting of the study.
About 176 articles were initially identified, of which 12 were selected for the study after removing duplicates and applying the inclusion and exclusion criteria. The primary applications of AI in managing CML included tumor diagnosis/classification (n = 9, 75%), prediction/prognosis (n = 2, 17%), and treatment (n = 1, 8%). For tumor diagnosis, AI was categorized into blood smear image-based (n = 5), clinical parameter-based (n = 2), and gene profiling-based (n = 2) approaches.
The most commonly employed AI models were Support Vector Machine (SVM) (n = 5), eXtreme Gradient Boosting (XGBoost) (n = 4), and various neural network methods, such as Artificial Neural Network (ANN) (n = 3).
Additionally, the Hybrid Convolutional Neural Network with Interactive Autodidactic School (HCNN-IAS) achieved 100% accuracy and sensitivity in organizing leukemia data types, while MayGAN attained 99.8% accuracy and high performance in diagnosing CML from blood smear images.
The study concluded that AI offers groundbreaking insights and tools for enhancing prediction, prognosis, and personalized treatment in CML. Integrated AI systems empower healthcare practitioners with advanced analytics, optimizing patient care and improving clinical outcomes in CML management.
The study received no funds.
Source: https://pubmed.ncbi.nlm.nih.gov/39164653/
Ram M, Afrash MR, Moulaei K, et al. (2024). “Application of artificial intelligence in chronic myeloid leukemia (CML) disease prediction and management: a scoping review.” BMC Cancer. 2024;24(1):1026. Published 2024 Aug 20. doi:10.1186/s12885-024-12764-y
