AIMC Topic: Pulmonary Embolism
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Deep learning-based automated detection of pulmonary embolism on CT pulmonary angiograms: No significant effects on report communication times and patient turnaround in the emergency department nine months after technical implementation.
European journal of radiology
Jun 11, 2021
OBJECTIVES: Rapid communication of CT exams positive for pulmonary embolism (PE) is crucial for timely initiation of anticoagulation and patient outcome. It is unknown if deep learning automated detection of PE on CT Pulmonary Angiograms (CTPA) in co...
A comparison of natural language processing to ICD-10 codes for identification and characterization of pulmonary embolism.
Thrombosis research
May 6, 2021
INTRODUCTION: The 10th revision of the International Classification of Diseases (ICD-10) codes is frequently used to identify pulmonary embolism (PE) events, although the validity of ICD-10 has been questioned. Natural language processing (NLP) is a ...
Performance of an artificial intelligence tool with real-time clinical workflow integration - Detection of intracranial hemorrhage and pulmonary embolism.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Mar 25, 2021
Missed Incidental Pulmonary Embolism: Harnessing Artificial Intelligence to Assess Prevalence and Improve Quality Improvement Opportunities.
Journal of the American College of Radiology : JACR
Feb 16, 2021
PURPOSE: Incidental pulmonary embolism (IPE) can be found on body CT. The aim of this study was to evaluate the feasibility of using artificial intelligence to identify missed IPE on a large number of CT examinations.
Suspected Acute Pulmonary Embolism: Gestalt, Scoring Systems, and Artificial Intelligence.
Seminars in respiratory and critical care medicine
Feb 16, 2021
Pulmonary embolism (PE) remains a diagnostic challenge in 2021. As the pathology is potentially fatal and signs and symptoms are nonspecific, further investigations are classically required. Based on the Bayesian approach, clinical probability became...
Multimodal fusion with deep neural networks for leveraging CT imaging and electronic health record: a case-study in pulmonary embolism detection.
Scientific reports
Dec 17, 2020
Recent advancements in deep learning have led to a resurgence of medical imaging and Electronic Medical Record (EMR) models for a variety of applications, including clinical decision support, automated workflow triage, clinical prediction and more. H...
Thrombolysis of Pulmonary Emboli via Endobronchial Ultrasound-Guided Transbronchial Needle Injection.
The Annals of thoracic surgery
Oct 24, 2020
BACKGROUND: Endobronchial ultrasound-guided transbronchial needle injection (EBUS-TBNI) is a novel technique for treating peribronchial targets. The aim of this study was to evaluate preliminary feasibility of thrombolysis of pulmonary emboli via EBU...
Pulmonary Embolism in Acute Asthma Exacerbation: Clinical Characteristics, Prediction Model and Hospital Outcomes.
Lung
May 18, 2020
PURPOSE: Little is known about the characteristics and impact of acute pulmonary embolism (PE) during episodes of asthma exacerbation. We aimed to characterize patients diagnosed with acute PE in the setting of asthma exacerbation, develop a predicti...
Fibrin Fibrinogen Degradation Products
Computed Tomography Angiography
Humans
Clinical Decision Rules
Machine Learning
Hospitals, University
Heart Rate
Natriuretic Peptide, Brain
International Normalized Ratio
Pulmonary Embolism
Middle Aged
Creatinine
Disease Progression
Comorbidity
Adult
Intensive Care Units
Body Mass Index
Asthma
Case-Control Studies
Female
Oxygen
Male
Length of Stay
Aged
Evaluation of acute pulmonary embolism and clot burden on CTPA with deep learning.
European radiology
Feb 16, 2020
OBJECTIVES: To take advantage of the deep learning algorithms to detect and calculate clot burden of acute pulmonary embolism (APE) on computed tomographic pulmonary angiography (CTPA).
Towards automated generation of curated datasets in radiology: Application of natural language processing to unstructured reports exemplified on CT for pulmonary embolism.
European journal of radiology
Feb 6, 2020
PURPOSE: To design and evaluate a self-trainable natural language processing (NLP)-based procedure to classify unstructured radiology reports. The method enabling the generation of curated datasets is exemplified on CT pulmonary angiogram (CTPA) repo...
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