Hematology

A deep learning approach to detect blood vessels in basal cell carcinoma.

PURPOSE: Blood vessels called telangiectasia are visible in skin lesions with the aid of dermoscopy. Telangiectasia are a pivotal identifying feature of basal cell carcinoma. These vessels appear thready, serpiginous, and may also appear arborizing, that is, wide vessels branch into successively thinner vessels. Due to these intricacies, their detection is not an easy task, neither with manual ann...

Detection of Peripheral Malarial Parasites in Blood Smears Using Deep Learning Models.

Due to the plasmodium parasite, malaria is transmitted mostly through red blood cells. Manually counting blood cells is extremely time consuming and tedious. In a recommendation for the advanced technology stage and analysis of malarial disease, the performance of the XG-Boost, SVM, and neural networks is compared. In comparison to machine learning models, convolutional neural networks provide rel...

Machine Learning Models for the Diagnosis and Prognosis Prediction of High-Grade B-Cell Lymphoma.

High-grade B-cell lymphoma (HGBL) is a newly introduced category of rare and heterogeneous invasive B-cell lymphoma (BCL), which is diagnosed dependin...

Follow-Up of Robotic Mitral Valve Repair: A Single Tertiary Institution Experience in China.

Surgical treatment of mitral valve diseases has become minimally invasive. This study analyzed the follow-up results of patients after mitral valve re...

WBC image classification and generative models based on convolutional neural network.

BACKGROUND: Computer-aided methods for analyzing white blood cells (WBC) are popular due to the complexity of the manual alternatives. Recent works ha...

An image classification deep-learning algorithm for shrapnel detection from ultrasound images.

Ultrasound imaging is essential for non-invasively diagnosing injuries where advanced diagnostics may not be possible. However, image interpretation r...

Prediction of serious outcomes based on continuous vital sign monitoring of high-risk patients.

Continuous monitoring of high-risk patients and early prediction of severe outcomes is crucial to prevent avoidable deaths. Current clinical monitorin...

Expert surgeons and deep learning models can predict the outcome of surgical hemorrhage from 1 min of video.

Major vascular injury resulting in uncontrolled bleeding is a catastrophic and often fatal complication of minimally invasive surgery. At the outset o...

In-line near-infrared analysis of milk coupled with machine learning methods for the daily prediction of blood metabolic profile in dairy cattle.

Precision livestock farming technologies are used to monitor animal health and welfare parameters continuously and in real time in order to optimize n...

Reversing radiation-induced immunosuppression using a new therapeutic modality.

Radiation-induced immune suppression poses significant health challenges for millions of patients undergoing cancer chemotherapy and radiotherapy trea...

Real-time 3D optoacoustic tracking of cell-sized magnetic microrobots circulating in the mouse brain vasculature.

Mobile microrobots hold remarkable potential to revolutionize health care by enabling unprecedented active medical interventions and theranostics, suc...

A Detailed Systematic Review on Retinal Image Segmentation Methods.

The separation of blood vessels in the retina is a major aspect in detecting ailment and is carried out by segregating the retinal blood vessels from ...

Fast label-free recognition of NRBCs by deep-learning visual object detection and single-cell Raman spectroscopy.

Nucleated red blood cells (NRBCs) as a type of rare cell present in an adult's peripheral blood is a concern in hematology, intensive care medicine an...

Combining mathematical modeling and deep learning to make rapid and explainable predictions of the patient-specific response to anticoagulant therapy under venous flow.

Anticoagulant drugs are commonly prescribed to prevent hypercoagulable states in patients with venous thromboembolism. The choice of the most efficien...

Identifying Blood Biomarkers for Dementia Using Machine Learning Methods in the Framingham Heart Study.

Blood biomarkers for dementia have the potential to identify preclinical disease and improve participant selection for clinical trials. Machine learni...

Da Vinci Robot-Assisted Video Image Processing under Artificial Intelligence Vision Processing Technology.

This research was aimed to explore the application value of intelligent algorithm-based digital images in Da Vinci robot-assisted treatment of patient...

Using DeepLab v3 + -based semantic segmentation to evaluate platelet activation.

This research used DeepLab v3 + -based semantic segmentation to automatically evaluate the platelet activation process and count the number of platele...

Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method.

White blood cells (WBCs) are blood cells that fight infections and diseases as a part of the immune system. They are also known as "defender cells." B...

Machine learning based deconvolution of microarray atrial samples from atrial fibrillation patients reveals increased fractions of follicular CD4+ T lymphocytes and gamma-delta T cells.

A potential relationship between T cell immunity and development of atrial fibrillation (AF) has been proposed. Historically in AF patients it has bee...

A new deep learning framework based on blood pressure range constraint for continuous cuffless BP estimation.

Blood pressure (BP) is known as an indicator of human health status, and regular measurement is helpful for early detection of cardiovascular diseases...