Acute Myeloid Leukemia (AML) is a heterogeneous hematological malignancy with varying clinical outcomes. The prognosis of AML depends on several factors, including age, cytogenetics, and molecular abnormalities. Traditionally, AML risk stratification has been performed based on clinical and cytogenetic characteristics. However, recent studies have shown that integrating molecular data into AML risk stratification can improve prognostication accuracy. Deep learning (DL) algorithms have emerged as a powerful tool to identify novel molecular signatures that can enhance AML risk stratification.The primary objective of this book is to provide a systematic review and meta-analysis of the current literature on DL-based AML risk stratification. The book aims to summarize the current state-of-the-art of DL algorithms for AML risk stratification, identify knowledge gaps, and suggest future research directions
Target audience: The book is intended for researchers, clinicians, and students interested in the field of AML risk stratification and deep learning. It will be a valuable resource for medical professionals who want to stay up-to-date with the latest developments in AML risk stratification and explore the potential of deep learning in this area. Expected outcome: This book will provide a comprehensive and up-to-date review of the current state-of-the-art deep learning-based approaches for risk stratification in AML. It will help readers understand the potential of deep learning in improving AML risk stratification and patient outcomes. The book will also identify areas of future research and development in this field, paving the way for further progress in the diagnosis and treatment of AML.