Hematology

Application of artificial intelligence-driven endoscopic screening and diagnosis of gastric cancer.

The landscape of gastrointestinal endoscopy continues to evolve as new technologies and techniques become available. The advent of image-enhanced and magnifying endoscopies has highlighted the step toward perfecting endoscopic screening and diagnosis of gastric lesions. Simultaneously, with the development of convolutional neural network, artificial intelligence (AI) has made unprecedented breakth...

Development of a field artificial intelligence triage tool: Confidence in the prediction of shock, transfusion, and definitive surgical therapy in patients with truncal gunshot wounds.

BACKGROUND: In-field triage tools for trauma patients are limited by availability of information, linear risk classification, and a lack of confidence reporting. We therefore set out to develop and test a machine learning algorithm that can overcome these limitations by accurately and confidently making predictions to support in-field triage in the first hours after traumatic injury.

Temporal and spectral unmixing of photoacoustic signals by deep learning.

Improving the imaging speed of multi-parametric photoacoustic microscopy (PAM) is essential to leveraging its impact in biomedicine. However, to avoid...

[Robot-assisted nephroureterectomy requiring no robot redocking or patient repositioning: experience from a single center with 62 cases].

To examine a new technique of robot-assisted nephroureterectomy without robot reldocking or patient repositioning. Patients diagnosed as upper tract...

[Robot-assisted single-anastomosis duodeno-ileal bypass with sleeve gastrectomy].

Single-anastomosis duodeno-ileal bypass with sleeve gastrectomy (SADI-S) is simpler and has similar efficacy for obesity and obesity-associated metabo...

Digital holographic deep learning of red blood cells for field-portable, rapid COVID-19 screening.

Rapid screening of red blood cells for active infection of COVID-19 is presented using a compact and field-portable, 3D-printed shearing digital holog...

Predictors of bleeding event among elderly patients with mechanical valve replacement using random forest model: A retrospective study.

Available classification tools and risk factors predicting bleeding events in elderly patients after mechanical valve replacement may not be suitable ...

Interactive Web Application for Plotting Personalized Prognosis Prediction Curves in Allogeneic Hematopoietic Cell Transplantation Using Machine Learning.

BACKGROUND: Allogeneic hematopoietic cell transplantation (allo-HCT) is a curative treatment option for malignant hematological disorders. Transplant ...

[Complications of Da Vinci robot thyroid surgery by bilateral axillo-breast approach].

To evaluate the complications of Da Vinci robotic thyroid surgery by bilateral axillo-breast approach. A retrospective analysis of complications was...

Artificial intelligence velocimetry and microaneurysm-on-a-chip for three-dimensional analysis of blood flow in physiology and disease.

Understanding the mechanics of blood flow is necessary for developing insights into mechanisms of physiology and vascular diseases in microcirculation...

[Clinical outcomes for young patients with muscular invasive bladder cancer who received robot-assisted radical cystectomy].

To evaluate the clinical features, surgical effects and factors that may affect prognosis of muscular invasive bladder cancer in young people. The c...

Augmented Human Intelligence and Automated Diagnosis in Flow Cytometry for Hematologic Malignancies.

OBJECTIVES: Clinical flow cytometry is laborious, time-consuming, and expensive given the need for data review by highly trained personnel such as tec...

Predicting 2-Day Mortality of Thrombocytopenic Patients Based on Clinical Laboratory Data Using Machine Learning.

BACKGROUND: Clinical laboratories have traditionally used a single critical value for thrombocytopenic events. This system, however, could lead to ina...

High-throughput label-free cell detection and counting from diffraction patterns with deep fully convolutional neural networks.

SIGNIFICANCE: Digital holographic microscopy (DHM) is a promising technique for the study of semitransparent biological specimen such as red blood cel...

Advancing care for acute gastrointestinal bleeding using artificial intelligence.

The future of gastrointestinal bleeding will include the integration of machine learning algorithms to enhance clinician risk assessment and decision ...

PV[O]H: Noninvasive Enabling Technology, New Physiological Monitoring, and Big Data.

INTRODUCTION: Measures of normal and abnormal physiology are interrelated and vary continuously. Our ability to detect and predict changes in physiolo...

Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets.

BACKGROUND: The accuracy of current prediction tools for ischaemic and bleeding events after an acute coronary syndrome (ACS) remains insufficient for...

Machine Learning Applied to Registry Data: Development of a Patient-Specific Prediction Model for Blood Transfusion Requirements During Craniofacial Surgery Using the Pediatric Craniofacial Perioperative Registry Dataset.

BACKGROUND: Craniosynostosis is the premature fusion of ≥1 cranial sutures and often requires surgical intervention. Surgery may involve extensive ost...

Artificial intelligence-based prediction of transfusion in the intensive care unit in patients with gastrointestinal bleeding.

OBJECTIVE: Gastrointestinal (GI) bleeding commonly requires intensive care unit (ICU) in cases of potentialhaemodynamiccompromise or likely urgent int...

A Machine Learning Approach to Predict Deep Venous Thrombosis Among Hospitalized Patients.

Deep venous thrombosis (DVT) is associated with significant morbidity, mortality, and increased healthcare costs. Standard scoring systems for DVT ris...