AIMC Topic: Hematologic Diseases

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Partial splenectomy in the era of minimally invasive surgery: the current laparoscopic and robotic experiences.

Surgical endoscopy Mar 5, 2015
BACKGROUND: Partial splenectomy (PS) is a spleen-preserving technique that is applied as a result of trauma, focal lesions or hematological conditions. Despite the improvement of laparoscopic techniques within the past several decades, minimally inva...
Hematologic Diseases Splenectomy Minimally Invasive Surgical Procedures Humans Laparoscopy Robotics
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Big data analytics and machine learning in hematology: Transformative insights, applications and challenges.

Medicine Mar 7, 2025
The integration of big data analytics and machine learning (ML) into hematology has ushered in a new era of precision medicine, offering transformative insights into disease management. By leveraging vast and diverse datasets, including genomic profi...
Humans Machine Learning Hematologic Diseases Hematology Data Analytics Precision Medicine Big Data
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Digital Microscopy Augmented by Artificial Intelligence to Interpret Bone Marrow Samples for Hematological Diseases.

Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada Mar 7, 2024
Analysis of bone marrow aspirates (BMAs) is an essential step in the diagnosis of hematological disorders. This analysis is usually performed based on a visual examination of samples under a conventional optical microscope, which involves a labor-int...
Microscopy Humans Hematologic Diseases Artificial Intelligence Algorithms Bone Marrow
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Highly accurate differentiation of bone marrow cell morphologies using deep neural networks on a large image data set.

Blood Nov 18, 2021
Biomedical applications of deep learning algorithms rely on large expert annotated data sets. The classification of bone marrow (BM) cell cytomorphology, an important cornerstone of hematological diagnosis, is still done manually thousands of times e...
Neural Networks, Computer Image Processing, Computer-Assisted Humans Microscopy Cell Differentiation Hematologic Diseases Bone Marrow Cells
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[Artificial intelligence (AI) and hematological diseases: establishment of a peripheral blood convolutional neural network (CNN)-based digital morphology analysis system].

[Rinsho ketsueki] The Japanese journal of clinical hematology Jan 1, 2020
Morphological analysis of the blood smear is an essential element of diagnosing a disease hematologically and has been performed by conventional manual light microscopy for several decades. Although this method is the gold standard, it is labor-inten...
Microscopy Hematologic Diseases Neural Networks, Computer Artificial Intelligence Hematologic Tests Humans
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Using a machine learning algorithm to predict acute graft-versus-host disease following allogeneic transplantation.

Blood advances Nov 26, 2019
Acute graft-versus-host disease (aGVHD) is 1 of the critical complications that often occurs following allogeneic hematopoietic stem cell transplantation (HSCT). Thus far, various types of prediction scores have been created using statistical calcula...
Hematopoietic Stem Cell Transplantation Transplantation, Homologous Hematologic Diseases Machine Learning Area Under Curve Algorithms Graft vs Host Disease Male Female Humans Models, Theoretical Prognosis Reproducibility of Results
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Hematological disturbances in Down syndrome: single centre experience of thirteen years and review of the literature.

The Turkish journal of pediatrics Jan 1, 2019
Karakurt N, Uslu İ, Aygün C, Albayrak C. Hematological disturbances in Down syndrome: single centre experience of thirteen years and review of the literature. Turk J Pediatr 2019; 61: 664-670. Neonates with Down syndrome (DS) may have hematological a...
Follow-Up Studies Blood Cell Count Risk Factors Child Female Down Syndrome Logistic Models Male Infant, Newborn Infant Humans Retrospective Studies Child, Preschool Hematologic Diseases
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Effect of Aggregation Operators on Network-Based Disease Gene Prioritization: A Case Study on Blood Disorders.

IEEE/ACM transactions on computational biology and bioinformatics Jan 1, 2017
Owing to the innate noise in the biological data sources, a single source or a single measure do not suffice for an effective disease gene prioritization. So, the integration of multiple data sources or aggregation of multiple measures is the need of...
Humans Protein Interaction Maps Genetic Predisposition to Disease Computational Biology Hematologic Diseases Databases, Genetic Fuzzy Logic
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