Sickle Cell Disease, Thalassemia, Leukemia, Lymphoma, Myelodysplastic Syndromes, Hemophilia, Anemia, Eosinophilia, Thrombocytopenia: Pathogens, Pathophysiology, Etiology, Epidemiology, AI, ML Modeling, 3D Fingerprinting, Hematologic Disorders

Abstract

Hematologic disorders refer to a broad range of both genetic and non-genetic disorders involving the red blood cells, white blood cells, platelets, coagulation factors, and bone marrow, such as sickle cell disease (SCD), thalassemia, leukemia, lymphoma, myelodysplastic disorders, hemophilia, anemia, eosinophilia, and thrombocytopenia. The review highlights contemporary evidence in humans about the etiology, pathophysiology, epidemiology, diagnosis, and computational implications of these disorders. Special attention is paid to the application of artificial intelligence (AI), machine learning (ML), digital pathology, genomics, and multimodal data fusion to their diagnostics and prognosis. The review further describes three-dimensional (3D) morphologic and molecular fingerprinting as an alternative computational method to analyze the spatial morphology of cells and tissues beyond standard microscopy. While AI shows great promise when combined with clinical, laboratory, molecular, and imaging data, its broad application in the clinical setting is constrained by issues such as heterogeneous databases, insufficient multicenter validation, poor model interpretability, and inadequate reporting. Going forward, there needs to be standardization of human databases, explainable AI, prospective validation, and federated learning that protects patient privacy.