AIMC Topic: Anemia, Sickle Cell

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Exploring machine learning algorithms in sickle cell disease patient data: A systematic review.

PloS one Nov 11, 2024
This systematic review explores the application of machine learning (ML) algorithms in sickle cell disease (SCD), focusing on diagnosis and several clinical characteristics, such as early detection of organ failure, identification of drug dosage, and...
Anemia, Sickle Cell Machine Learning Humans Algorithms Neural Networks, Computer
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A machine learning-based workflow for predicting transplant outcomes in patients with sickle cell disease.

British journal of haematology Oct 22, 2024
Allogeneic haematopoietic cell transplantation (HCT) with HLA-matched sibling donor remains the most established curative therapeutic option for patients with sickle cell disease (SCD). However, it is not without risks, highlighting the need for a ri...
Female Risk Factors Young Adult Humans Machine Learning Treatment Outcome Adult Male Workflow Adolescent Anemia, Sickle Cell Hematopoietic Stem Cell Transplantation
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Biophysical Profiling of Sickle Cell Disease Using Holographic Cytometry and Deep Learning.

International journal of molecular sciences Jul 25, 2023
Sickle cell disease (SCD) is an inherited hematological disorder associated with high mortality rates, particularly in sub-Saharan Africa. SCD arises due to the polymerization of sickle hemoglobin, which reduces flexibility of red blood cells (RBCs),...
Erythrocytes Hematologic Diseases Hemoglobin, Sickle Humans Deep Learning Anemia, Sickle Cell
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Classification of red cell dynamics with convolutional and recurrent neural networks: a sickle cell disease case study.

Scientific reports Jan 13, 2023
The fraction of red blood cells adopting a specific motion under low shear flow is a promising inexpensive marker for monitoring the clinical status of patients with sickle cell disease. Its high-throughput measurement relies on the video analysis of...
Humans Neural Networks, Computer Machine Learning Motion Anemia, Sickle Cell Erythrocytes
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A novel deep learning approach for sickle cell anemia detection in human RBCs using an improved wrapper-based feature selection technique in microscopic blood smear images.

Biomedizinische Technik. Biomedical engineering Oct 6, 2022
Sickle Cell Anemia (SCA) is a disorder in Red Blood Cells (RBCs) of human blood. Children under five years and pregnant women are mostly affected by SCA. Early diagnosis of this ailment can save lives. In recent years, the computer aided diagnosis of...
Female Diagnosis, Computer-Assisted Deep Learning Humans Databases, Factual Pregnancy Erythrocytes Child, Preschool Child Anemia, Sickle Cell
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Phenotypes of sickle cell intensive care admissions: an unsupervised machine learning approach in a single-center retrospective cohort.

Annals of hematology Jul 15, 2022
Sickle cell disease (SCD) is associated with multiple known complications and increased mortality. This study aims to further understand the profile of intensive care unit (ICU) admissions of SCD patients. In this single-center retrospective cohort (...
Male Brazil Hospital Mortality Retrospective Studies Intensive Care Units Unsupervised Machine Learning Female Humans Critical Care Anemia, Sickle Cell Phenotype
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Profiling of 35 Cases of Hb S/Hb E (: c.20A>T/: c.79G>a), Disease and Association with α-Thalassemia and β-Globin Gene Cluster Haplotypes from Odisha, India.

Hemoglobin Mar 4, 2022
Hb S/Hb E (: c.20A>T/: c.79G>A) is an uncommon variant of sickle cell disease resulting from coinheritance of Hb S and Hb E. Clinico-hematological and biochemical parameters of 35 cases of Hb S/Hb E disease were studied and compared with 70 matched c...
beta-Globins Haplotypes Multigene Family Humans alpha-Thalassemia beta-Thalassemia Anemia, Sickle Cell
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Integrating deep learning with microfluidics for biophysical classification of sickle red blood cells adhered to laminin.

PLoS computational biology Nov 29, 2021
Sickle cell disease, a genetic disorder affecting a sizeable global demographic, manifests in sickle red blood cells (sRBCs) with altered shape and biomechanics. sRBCs show heightened adhesive interactions with inflamed endothelium, triggering painfu...
Humans Neural Networks, Computer Diagnosis, Computer-Assisted Hemoglobin, Sickle Image Interpretation, Computer-Assisted Microfluidics Biophysical Phenomena Anemia, Sickle Cell Lab-On-A-Chip Devices Deep Learning High-Throughput Screening Assays Erythrocytes, Abnormal In Vitro Techniques Laminin Erythrocyte Deformability Computational Biology Protein Multimerization
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Machine-learning algorithms for predicting hospital re-admissions in sickle cell disease.

British journal of haematology Nov 10, 2020
Reducing preventable hospital re-admissions in Sickle Cell Disease (SCD) could potentially improve outcomes and decrease healthcare costs. In a retrospective study of electronic health records, we hypothesized Machine-Learning (ML) algorithms may out...
Adolescent Anemia, Sickle Cell Patient Readmission Male Middle Aged Risk Assessment Aged, 80 and over Young Adult Adult Algorithms Humans Hospitalization Female Machine Learning Aged
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Using Machine Learning to Predict Early Onset Acute Organ Failure in Critically Ill Intensive Care Unit Patients With Sickle Cell Disease: Retrospective Study.

Journal of medical Internet research May 13, 2020
BACKGROUND: Sickle cell disease (SCD) is a genetic disorder of the red blood cells, resulting in multiple acute and chronic complications, including pain episodes, stroke, and kidney disease. Patients with SCD develop chronic organ dysfunction, which...
Male Multiple Organ Failure Anemia, Sickle Cell Humans Female Retrospective Studies Early Diagnosis Machine Learning Hospitalization Critical Illness Adult Intensive Care Units
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