Hematology

Implementation of five machine learning methods to predict the 52-week blood glucose level in patients with type 2 diabetes.

OBJECTIVE: For the patients who are suffering from type 2 diabetes, blood glucose level could be affected by multiple factors. An accurate estimation of the trajectory of blood glucose is crucial in clinical decision making. Frequent glucose measurement serves as a good source of data to train machine learning models for prediction purposes. This study aimed at using machine learning methods to pr...

The identification of interacting brain networks during robot-assisted training with multimodal stimulation.

Robot-assisted rehabilitation training is an effective way to assist rehabilitation therapy. So far, various robotic devices have been developed for automatic training of central nervous system following injury. Multimodal stimulation such as visual and auditory stimulus and even virtual reality technology were usually introduced in these robotic devices to improve the effect of rehabilitation tra...

Age-specific risk factors for the prediction of obesity using a machine learning approach.

Machine Learning is a powerful tool to discover hidden information and relationships in various data-driven research fields. Obesity is an extremely c...

Application of robot-assisted endoscopic technique in the treatment of patent ductus arteriosus in 106 children.

The objective is to evaluate and apply the robot-assisted endoscopic surgical technique for treatment of patent ductus arteriosus (PDA) in children. C...

Classification of red cell dynamics with convolutional and recurrent neural networks: a sickle cell disease case study.

The fraction of red blood cells adopting a specific motion under low shear flow is a promising inexpensive marker for monitoring the clinical status o...

An AI-Aided Diagnostic Framework for Hematologic Neoplasms Based on Morphologic Features and Medical Expertise.

A morphologic examination is essential for the diagnosis of hematological diseases. However, its conventional manual operation is time-consuming and l...

Developing a Novel Methodology by Integrating Deep Learning and HMM for Segmentation of Retinal Blood Vessels in Fundus Images.

Accurate segregation of retinal blood vessels network plays a crucial role in clinical assessments, treatments, and rehabilitation process. Owing to t...

Robot-assisted complex urinary tract reconstruction using intestinal segments: redefining the paradigm.

Complex urinary tract reconstruction has significantly advanced with the increasing use of robot-assisted procedures. Robotic surgery aims to achieve ...

Machine learning in the coagulation and hemostasis arena: an overview and evaluation of methods, review of literature, and future directions.

Artificial Intelligence and machine-learning (ML) studies are increasingly populating the life science space and some have also started to integrate c...

Forecasting blood demand for different blood groups in Shiraz using auto regressive integrated moving average (ARIMA) and artificial neural network (ANN) and a hybrid approaches.

Providing fresh blood to keep people in need of blood alive, has always been a main issues of health systems. Right policy-making in this area require...

The learning curve for single-port transaxillary robotic thyroidectomy (SP-TART): experience through initial 50 cases of lobectomy.

The new da Vinci® single-port (SP) robotic system, which utilizes a smaller incision and work space compared to the previous versions, is suitable for...

Detection of factors affecting kidney function using machine learning methods.

Due to the increasing prevalence of chronic kidney disease and its high mortality rate, study of risk factors affecting the progression of the disease...

Automatic generation of artificial images of leukocytes and leukemic cells using generative adversarial networks (syntheticcellgan).

BACKGROUND AND OBJECTIVES: Visual analysis of cell morphology has an important role in the diagnosis of hematological diseases. Morphological cell rec...

Quantitative measurement of blood glucose influenced by multiple factors via photoacoustic technique combined with optimized wavelet neural networks.

In this work, the photoacoustic (PA) quantitative measurement of blood glucose concentration (BGC) influenced by multiple factors was firstly investig...

Deetect: A Deep Learning-Based Image Analysis Tool for Quantification of Adherent Cell Populations on Oxygenator Membranes after Extracorporeal Membrane Oxygenation Therapy.

The strong interaction of blood with the foreign surface of membrane oxygenators during ECMO therapy leads to adhesion of immune cells on the oxygenat...

Deep Learning-Powered Bessel-Beam Multiparametric Photoacoustic Microscopy.

Enabling simultaneous and high-resolution quantification of the total concentration of hemoglobin ( [Formula: see text]), oxygen saturation of hemoglo...

Robot-Assisted Radical Nephrectomy Using the Novel Avatera Robotic Surgical System: A Feasibility Study in a Porcine Model.

To evaluate the feasibility and intraoperative technical parameters of the new robot-assisted surgical system Avatera by performing bilateral nephrec...

Novel Use of a Real-Time Prediction Model to Enhance Early Detection of Need for Massive Transfusion-Artificial Intelligence Behind the Drapes.

Comparison of robot-assisted partial nephrectomy with soft coagulation and double-layer technique for complex and non-complex tumors.

OBJECTIVES: To compare the postoperative outcomes of robot-assisted partial nephrectomy when only the inner layer is sutured (single-layer technique w...

Artificial intelligence model with deep learning in nonalcoholic fatty liver disease diagnosis: genetic based artificial neural networks.

Nonalcoholic fatty liver disease (NAFLD) is one of the most common causes of chronic liver disease in the world. The NAFLD spectrum includes simple st...