Hematology

A deep-learning approach to predict bleeding risk over time in patients on extended anticoagulation therapy.

BACKGROUND: Thus far, all the clinical models developed to predict major bleeding in patients on extended anticoagulation therapy use the baseline predictors to stratify patients into different risk groups. Therefore, these models do not account for the clinical changes and events that occur after the baseline visit, which can modify risk of bleeding. However, it is difficult to develop predictive...

Predicting community acquired bloodstream infection in infants using full blood count parameters and C-reactive protein; a machine learning study.

Early recognition of bloodstream infection (BSI) in infants can be difficult, as symptoms may be non-specific, and culture can take up to 48 h. As a result, many infants receive unneeded antibiotic treatment while awaiting the culture results. In this study, we aimed to develop a model that can reliably identify infants who do not have positive blood cultures (and, by extension, BSI) based on the ...

Immune Regulation Patterns in Response to Environmental Pollutant Chromate Exposure-Related Genetic Damage: A Cross-Sectional Study Applying Machine Learning Methods.

Exposure to hexavalent chromium damages genetic materials like DNA and chromosomes, further elevating cancer risk, yet research rarely focuses on rela...

Mapping Blood Lead Levels in China during 1980-2040 with Machine Learning.

Lead poisoning is globally concerning, yet limited testing hinders effective interventions in most countries. We aimed to create annual maps of county...

Deep learning-enabled detection of rare circulating tumor cell clusters in whole blood using label-free, flow cytometry.

Metastatic tumors have poor prognoses for progression-free and overall survival for all cancer patients. Rare circulating tumor cells (CTCs) and rarer...

An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images.

BACKGROUND: Malaria is a serious public health concern worldwide. Early and accurate diagnosis is essential for controlling the disease's spread and a...

Assessing the perioperative outcomes of abdominal drain omission after robot-assisted partial nephrectomy.

The study aimed to evaluate the impact of abdominal drain placement (vs. omission) on perioperative outcomes of robot-assisted partial nephrectomy (RA...

On the relationship between various anticoagulants and robot-assisted radical prostatectomy: a single-surgeon serial analysis.

Prostate cancer patients often have other health conditions and take anticoagulants. It was believed that surgery under anticoagulants could worsen su...

Predictive modeling of deep vein thrombosis risk in hospitalized patients: A Q-learning enhanced feature selection model.

Deep vein thrombosis (DVT) represents a critical health concern due to its potential to lead to pulmonary embolism, a life-threatening complication. E...

Applications of artificial intelligence (AI) in drinking water treatment processes: Possibilities.

In water treatment processes (WTPs), artificial intelligence (AI) based techniques, particularly machine learning (ML) models have been increasingly a...

Cascade-EC Network: Recognition of Gastrointestinal Multiple Lesions Based on EfficientNet and CA_stm_Retinanet.

Capsule endoscopy (CE) is non-invasive and painless during gastrointestinal examination. However, capsule endoscopy can increase the workload of image...

Deep learning-based image annotation for leukocyte segmentation and classification of blood cell morphology.

The research focuses on the segmentation and classification of leukocytes, a crucial task in medical image analysis for diagnosing various diseases. T...

Machine learning identifies novel coagulation genes as diagnostic and immunological biomarkers in ischemic stroke.

BACKGROUND: Coagulation system is currently known associated with the development of ischemic stroke (IS). Thus, the current study is designed to iden...

Comprehensive data analysis of white blood cells with classification and segmentation by using deep learning approaches.

Deep learning approaches have frequently been used in the classification and segmentation of human peripheral blood cells. The common feature of previ...

Is the robotic approach the future of distal pancreatectomy with splenectomy? A propensity score matched analysis.

Our study provides a comparative analysis of the Laparo-Endoscopic Single Site (LESS) and robotic surgical approaches for distal pancreatectomy and sp...

Predictive factors of renal function after robot-assisted partial nephrectomy in clinical T1b tumors.

Robot-assisted partial nephrectomy (RAPN) has been shown to be a safe and effective method for treatment of small renal tumors, including clinical T1b...

AML leukocyte classification method for small samples based on ACGAN.

Leukemia is a class of hematologic malignancies, of which acute myeloid leukemia (AML) is the most common. Screening and diagnosis of AML are performe...

Deep learning-based quantification of total bleeding volume and its association with complications, disability, and death in patients with aneurysmal subarachnoid hemorrhage.

OBJECTIVE: The relationships between immediate bleeding severity, postoperative complications, and long-term functional outcomes in patients with aneu...

Mechanistic Assessment of Cardiovascular State Informed by Vibroacoustic Sensors.

Monitoring blood pressure, a parameter closely related to cardiovascular activity, can help predict imminent cardiovascular events. In this paper, a n...

Electrophysiological and Visual Parameter Changes in Retinitis Pigmentosa Patients undergoing Autologous Platelet-Rich Plasma Therapy: A Randomized Control Trial.

PURPOSE: To assess the efficacy of autologous platelet-rich plasma (PRP) injections in suprachoroidal space and subtenon space in cases of retinitis p...