Hematology

Diagnosing brain tumours by routine blood tests using machine learning.

Routine blood test results are assumed to contain much more information than is usually recognised even by the most experienced clinicians. Using routine blood tests from 15,176 neurological patients we built a machine learning predictive model for the diagnosis of brain tumours. We validated the model by retrospective analysis of 68 consecutive brain tumour and 215 control patients presenting to ...

Application of Artificial Intelligence to Gastroenterology and Hepatology.

Since 2010, substantial progress has been made in artificial intelligence (AI) and its application to medicine. AI is explored in gastroenterology for endoscopic analysis of lesions, in detection of cancer, and to facilitate the analysis of inflammatory lesions or gastrointestinal bleeding during wireless capsule endoscopy. AI is also tested to assess liver fibrosis and to differentiate patients w...

Machine Learning Approach for Prediction of Hematic Parameters in Hemodialysis Patients.

This paper shows the application of machine learning techniques to predict hematic parameters using blood visible spectra during ex-vivo treatments. ...

Probability density function based modeling of spatial feature variation in capsule endoscopy data for automatic bleeding detection.

Wireless capsule endoscopy (WCE) is a video technology to inspect abnormalities, like bleeding in the gastrointestinal tract. In order to avoid a comp...

Non-faradaic electrochemical impedimetric profiling of procalcitonin and C-reactive protein as a dual marker biosensor for early sepsis detection.

In this work, we demonstrate a robust, dual marker, biosensing strategy for specific and sensitive electrochemical response of Procalcitonin and C-rea...

Artificial intelligence using a convolutional neural network for automatic detection of small-bowel angioectasia in capsule endoscopy images.

BACKGROUND AND AIM: Although small-bowel angioectasia is reported as the most common cause of bleeding in patients and frequently diagnosed by capsule...

Clinical Study on Minimally Invasive Liquefaction and Drainage of Hypertensive Putaminal Hemorrhage through Frontal Approach.

 Hypertensive intracerebral hemorrhage is one of the most common cerebrovascular diseases with high mortality and high disability rate. The aim of th...

Machine-based detection and classification for bone marrow aspirate differential counts: initial development focusing on nonneoplastic cells.

Bone marrow aspirate (BMA) differential cell counts (DCCs) are critical for the classification of hematologic disorders. While manual counts are consi...

Hemophilia C: A Case Report With Updates on Diagnosis and Management of a Rare Bleeding Disorder.

Hemophilia C or factor XI deficiency is a rare clotting disorder with prevalence of only 1 per 1 million. A 24-year-old male with multiple abdominal s...

Using Artificial Intelligence to Manage Thrombosis Research, Diagnosis, and Clinical Management.

Thrombosis development in either arterial or venous system remains a major cause of death and disability worldwide. This poorly controlled in vivo clo...

Automated diagnosis of heart valve degradation using novelty detection algorithms and machine learning.

The blood flow through the major vessels holds great diagnostic potential for the identification of cardiovascular complications and is therefore rout...

Validation of a Machine Learning Model That Outperforms Clinical Risk Scoring Systems for Upper Gastrointestinal Bleeding.

BACKGROUND & AIMS: Scoring systems are suboptimal for determining risk in patients with upper gastrointestinal bleeding (UGIB); these might be improve...

Multi-Objective Optimization for Personalized Prediction of Venous Thromboembolism in Ovarian Cancer Patients.

Thrombotic events are one of the leading causes of mortality and morbidity related to cancer, with ovarian cancer having one of the highest incidence ...

A novel automated image analysis system using deep convolutional neural networks can assist to differentiate MDS and AA.

Detection of dysmorphic cells in peripheral blood (PB) smears is essential in diagnostic screening of hematological diseases. Myelodysplastic syndrome...

Predicting Quality of Overnight Glycaemic Control in Type 1 Diabetes Using Binary Classifiers.

In type 1 diabetes management, maintaining nocturnal blood glucose within target range can be challenging. Although semi-automatic systems to modulate...

Continuous Glucose Monitoring and Glycemic Control in Patients With Type 2 Diabetes Mellitus and CKD.

RATIONALE & OBJECTIVE: The accuracy of glycated hemoglobin (HbA) level for assessment of glycemic control in patients with chronic kidney disease (CKD...

Machine learning predicts putative hematopoietic stem cells within large single-cell transcriptomics data sets.

Hematopoietic stem cells (HSCs) are an essential source and reservoir for normal hematopoiesis, and their function is compromised in many blood disord...

Abdominal artery segmentation method from CT volumes using fully convolutional neural network.

PURPOSE : The purpose of this paper is to present a fully automated abdominal artery segmentation method from a CT volume. Three-dimensional (3D) bloo...

Machine learning based prediction of perioperative blood loss in orthognathic surgery.

The aim of this study was to evaluate, if and with what accuracy perioperative blood loss can be calculated by a machine learning algorithm prior to o...

Reducing demand uncertainty in the platelet supply chain through artificial neural networks and ARIMA models.

One of the significant issues in global healthcare systems is improving the supply chain performance and addressing the uncertainties in demand. Blood...