Hematology

Identifying psychosis spectrum youth using support vector machines and cerebral blood perfusion as measured by arterial spin labeled fMRI.

Altered cerebral blood flow (CBF), as measured by arterial spin labelling (ASL), has been observed in several psychiatric conditions, but is a generally underutilized MRI technique, especially in the study of psychosis spectrum (PS) symptoms. We aimed to determine group differences in ASL resting state functional connectivity (rsFC) between PS and non-PS youth, and the reliability of a support vec...

Mechanical heart valves and pregnancy: Issues surrounding anticoagulation. Experience from two obstetric cardiac centres.

BACKGROUND: Pregnant women with mechanical heart valves are at significant risk of obstetric/cardiac complications. This study compares the anticoagulation management in two obstetric cardiac centres.

Artificial intelligence in gastroenterology: where are we heading?

Artificial intelligence (AI) is coming to medicine in a big wave. From making diagnosis in various medical conditions, following the latest advancemen...

Machine learning provides evidence that stroke risk is not linear: The non-linear Framingham stroke risk score.

Current stroke risk assessment tools presume the impact of risk factors is linear and cumulative. However, both novel risk factors and their interplay...

Ultrasound Deep Learning for Wall Segmentation and Near-Wall Blood Flow Measurement.

Studies of medical flow imaging have technical limitations for accurate analysis of blood flow dynamics and vessel wall interaction at arteries. We pr...

Quantitative Prediction of Hemolytic Toxicity for Small Molecules and Their Potential Hemolytic Fragments by Machine Learning and Recursive Fragmentation Methods.

Hemolytic toxicity, as one of the key toxicity endpoints for small molecules, can cause lysis of the erythrocyte membrane and subsequent release of he...

A machine learning approach to risk assessment for alcohol withdrawal syndrome.

At present, risk assessment for alcohol withdrawal syndrome relies on clinical judgment. Our aim was to develop accurate machine learning tools to pre...

Prediction of hypertension, hyperglycemia and dyslipidemia from retinal fundus photographs via deep learning: A cross-sectional study of chronic diseases in central China.

Retinal fundus photography provides a non-invasive approach for identifying early microcirculatory alterations of chronic diseases prior to the onset ...

Diagnosis of Asthma Based on Routine Blood Biomarkers Using Machine Learning.

Intelligent medical diagnosis has become common in the era of big data, although this technique has been applied to asthma only in limited contexts. U...

Using Machine Learning to Predict Early Onset Acute Organ Failure in Critically Ill Intensive Care Unit Patients With Sickle Cell Disease: Retrospective Study.

BACKGROUND: Sickle cell disease (SCD) is a genetic disorder of the red blood cells, resulting in multiple acute and chronic complications, including p...

Intelligent classification of platelet aggregates by agonist type.

Platelets are anucleate cells in blood whose principal function is to stop bleeding by forming aggregates for hemostatic reactions. In addition to the...

Deep learning-based monocular placental pose estimation: towards collaborative robotics in fetoscopy.

PURPOSE: Twin-to-twin transfusion syndrome (TTTS) is a placental defect occurring in monochorionic twin pregnancies. It is associated with high risks ...

Expert-level automated malaria diagnosis on routine blood films with deep neural networks.

Over 200 million malaria cases globally lead to half a million deaths annually. Accurate malaria diagnosis remains a challenge. Automated imaging proc...

Convolutional Neural Network Can Recognize Drug Resistance of Single Cancer Cells.

It is known that single or isolated tumor cells enter cancer patients' circulatory systems. These circulating tumor cells (CTCs) are thought to be an ...

FetNet: a recurrent convolutional network for occlusion identification in fetoscopic videos.

PURPOSE: Fetoscopic laser photocoagulation is a minimally invasive surgery for the treatment of twin-to-twin transfusion syndrome (TTTS). By using a l...

Artificial Intelligence: The Future for Diabetes Care.

Artificial intelligence (AI) is a fast-growing field and its applications to diabetes, a global pandemic, can reform the approach to diagnosis and man...

Combination Strategies for Immune-Checkpoint Blockade and Response Prediction by Artificial Intelligence.

The therapeutic concept of unleashing a pre-existing immune response against the tumor by the application of immune-checkpoint inhibitors (ICI) has re...

Early-Stage Lung Cancer Diagnosis by Deep Learning-Based Spectroscopic Analysis of Circulating Exosomes.

Lung cancer has a high mortality rate, but an early diagnosis can contribute to a favorable prognosis. A liquid biopsy that captures and detects tumor...

Development of a prediction model for hypotension after induction of anesthesia using machine learning.

Arterial hypotension during the early phase of anesthesia can lead to adverse outcomes such as a prolonged postoperative stay or even death. Predictin...

Development of machine learning and natural language processing algorithms for preoperative prediction and automated identification of intraoperative vascular injury in anterior lumbar spine surgery.

BACKGROUND: Intraoperative vascular injury (VI) may be an unavoidable complication of anterior lumbar spine surgery; however, vascular injury has impl...