AIMC Topic: Blood Cell Count

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A noninvasive machine learning model using a complete blood count for screening of primary vitreoretinal lymphoma.

Nature communications
Primary vitreoretinal lymphoma (PVRL) is a rare and aggressive intraocular malignancy that is frequently misdiagnosed because of its nonspecific early manifestations and the lack of effective screening tools. We conduct a multicentre case-control stu...

AI-driven pre-screening for colorectal cancer using complete blood counts: toward broader population impact.

International journal of colorectal disease
PURPOSE: Early colorectal cancer (CRC) detection is crucial for effective treatment; however, traditional screening methods face challenges. Colonoscopy, though highly effective, has limited availability, and fecal immunochemical tests (FIT) are more...

A Curated and Re-annotated Peripheral Blood Cell Dataset Integrating Four Public Resources.

Scientific data
We present TXL-PBC, a curated and re-annotated peripheral blood cell dataset constructed by integrating four publicly available resources: Blood Cell Count and Detection (BCCD), Blood Cell Detection Dataset (BCDD), Peripheral Blood Cells (PBC), and R...

Multiclass classification of thalassemia types using complete blood count and HPLC data with machine learning.

Scientific reports
Mild to severe anemia is caused by thalassemia, a common genetic disorder affecting over 100 countries worldwide, that results from the abnormality of one or several of the four globin genes. This leads to chronic hemolytic anemia and disrupted synth...

Edges are all you need: Potential of medical time series analysis on complete blood count data with graph neural networks.

PloS one
PURPOSE: Machine learning is a powerful tool to develop algorithms for clinical diagnosis. However, standard machine learning algorithms are not perfectly suited for clinical data since the data are interconnected and may contain time series. As show...

Analysis of maternal fetal outcomes and complete blood count parameters according to the stages of placental abruption: a retrospective study.

European journal of medical research
BACKGROUND: To compare the demographic characteristics, maternal and perinatal outcomes and hemoglobin parameters according to stages diagnosed with placental abruption.

Advancing blood cell detection and classification: performance evaluation of modern deep learning models.

BMC medical informatics and decision making
The detection and classification of blood cells are important in diagnosing and monitoring a variety of blood-related illnesses, such as anemia, leukemia, and infection, all of which may cause significant mortality. Accurate blood cell identification...

Association between the (neutrophil + monocyte)/albumin ratio and all-cause mortality in sepsis patients: a retrospective cohort study and predictive model establishment according to machine learning.

BMC infectious diseases
INTRODUCTION: Sepsis is a life-threatening condition characterized by widespread inflammatory response syndrome in the body resulting from infection. Previous studies have demonstrated that some inflammatory factors or nutritional elements contribute...

Complete Blood Count and Monocyte Distribution Width-Based Machine Learning Algorithms for Sepsis Detection: Multicentric Development and External Validation Study.

Journal of medical Internet research
BACKGROUND: Sepsis is an organ dysfunction caused by a dysregulated host response to infection. Early detection is fundamental to improving the patient outcome. Laboratory medicine can play a crucial role by providing biomarkers whose alteration can ...

AI-Driven Innovations for Early Sepsis Detection by Combining Predictive Accuracy With Blood Count Analysis in an Emergency Setting: Retrospective Study.

Journal of medical Internet research
BACKGROUND: Sepsis, a critical global health challenge, accounted for approximately 20% of worldwide deaths in 2017. Although the Sequential Organ Failure Assessment (SOFA) score standardizes the diagnosis of organ dysfunction, early sepsis detection...