Smart Hematology

Traditional and AI-Powered Diagnosis and Management
Erscheinungsjahr 2027
ISBN: 978-3-032-40809-9
Verlag: Springer

Buch, Englisch, 450 Seiten, Format (B × H): 155 mm x 235 mm

 addresses a pressing challenge in contemporary hematological practice: how to thoughtfully integrate emerging artificial intelligence technologies with established diagnostic methods. As machine learning tools increasingly enter clinical laboratories and hematology departments, there exists a significant gap in literature that provides both comprehensive theoretical grounding and practical implementation guidance for these approaches alongside traditional methodologies.

This edited volume brings together international experts to present what they believe is the first systematic integration of conventional and AI-assisted approaches across the spectrum of hematological disorders. Rather than treating ML as a separate or competing paradigm, each section is structured to demonstrate how these technologies can complement and enhance established diagnostic techniques, from microscopic examination of blood films and bone marrow specimens to flow cytometry, molecular diagnostics, and hemostasis testing. What distinguishes this work is its emphasis on practical translation. Each chapter not only explains algorithmic approaches but critically examines their clinical validity, limitations, and real-world implementation challenges. Contributors draw on published research and clinical experience to present realistic applications, such as automated leukemia subtyping, predictive models for treatment response, and integration of multi-modal diagnostic data—while maintaining appropriate clinical skepticism about oversimplified technological solutions.

This book prepares the current and next generation of hematology professionals to work effectively in an evolving diagnostic landscape where human expertise and computational tools work in concert to improve patient care. It is intended primarily for practicing hematologists, clinical pathologists, and laboratory medicine specialists who recognize the growing role of computational tools in their field but may lack formal training in their application. It also serves hematology fellows, PhD students, and medical laboratory scientists seeking to understand both the clinical context and technical underpinnings of AI-assisted diagnostics.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Dorgalaleh, Akbar

Samadov, Anvarjon

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Klinische und Innere Medizin Hämatologie, Transfusionsmedizin
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizinische Fachgebiete Pathologie, Cytopathologie, Histopathologie

Inhaltsverzeichnis

SECTION I: DIGITAL HEMATOLOGY AND IMAGE ANALYSIS.- 1. Fundamentals of Computer Vision in Hematological Diagnostics.- 2. Automated Peripheral Blood Smear Analysis: Clinical Applications.- SECTION II: RED BLOOD CELL DISORDERS.- 3. Anemia: Traditional Laboratory Diagnosis and Classification.- 4. Hemolytic Anemias and Hemoglobinopathies: Conventional Approaches.- 5. Machine Learning in Red Cell Disorder Diagnosis.- SECTION III: WHITE BLOOD CELL DISORDERS AND HEMATOLOGICAL MALIGNANCIES.- 6. Acute Leukemia: Traditional Diagnostic Methods.- 7. Computational Approaches to Leukemia Classification and Subtyping.- 8. Predictive Analytics in Hematological Malignancies.- SECTION IV: HEMOSTASIS: BLEEDING DISORDERS.- 9. Coagulation Disorders: Traditional Laboratory Investigation.- 10. Bleeding Disorders: Advanced Conventional Diagnostics.- 11. Machine Learning Applications in Coagulation Diagnostics.- SECTION V: HEMOSTASIS: PLATELET DISORDERS.- 12. Thrombocytopenia: Conventional Diagnostic Approaches.- 13. Platelet Function Disorders: Traditional Testing Methods.- 14. Computational Approaches to Platelet Disorder Diagnosis.- SECTION VI: THROMBOSIS AND HYPERCOAGULABLE STATES.- 15. Thrombophilia: Conventional Laboratory Screening and Machine Learning–Based Risk Assessment.- SECTION VII: CLINICAL INTEGRATION AND FUTURE PERSPECTIVES.- 16. The AI-Driven Transformation of Precision Hematology: From Implementation to Future Practice.

Über Autor(innen)

Dr Akbar Dorgalaleh is a scientific researcher in the field of thrombosis and haemostasis who has (co-)authored over 150 publications. He is/was on the editorial board of several international journals, including , , and . In addition, Dr. Dorgalaleh is also a current or former memberof several international scientific societies and working groups, including the International Society on Thrombosis and Haemostasis (ISTH) Scientific Standardization Committees (SSCs), particularly the Subcommittee on Factor XIII and Fibrinogen, and the International Council for Standardization in Haematology (ICSH).. As editor of the Springer books (2nd edition, 2023) and (2025), he provides comprehensive insights into congenital bleeding disorders such as Glanzmann thrombasthenia, Bernard-Soulier syndrome and Factor XIII deficiency, as well as thrombophilia investigations, covering modern diagnostic and therapeutic advances.

Mr Samadov is a medical laboratory professional with extensive experience in clinical diagnostics. He completed his education in medical laboratory science at the Mashhad University of Medical Sciences. Following his studies, he moved to Dushanbe, Tajikistan, where he has built a distinguished career in medical clinical laboratory work spanning over a decade. During his tenure in Dushanbe, Samadov developed extensive expertise in the practical aspects of laboratory medicine. As co-editor of the Springer book (2025), he contributes his practical laboratory experience to this comprehensive resource on thrombophilia diagnostics.