AIMC Topic: Pulmonary Embolism

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A machine learning approach to identify patients at risk for long-term consequences after pulmonary embolism.

Scientific reports
Pulmonary embolism (PE) can result in long-term sequelae, such as post-PE syndrome, including persistent dyspnea and chronic thromboembolic pulmonary hypertension (CTEPH). Existing prediction tools for severe post-PE complications lack sensitivity an...

AI-Based Algorithm to Detect Heart and Lung Disease From Acute Chest Computed Tomography Scans: Protocol for an Algorithm Development and Validation Study.

JMIR research protocols
BACKGROUND: Dyspnea is a common cause of hospitalization, posing diagnostic challenges among older adult patients with multimorbid conditions. Chest computed tomography (CT) scans are increasingly used in patients with dyspnea and offer superior diag...

Pulmonary Embolism Survival Prediction Using Multimodal Learning Based on Computed Tomography Angiography and Clinical Data.

Journal of thoracic imaging
PURPOSE: Pulmonary embolism (PE) is a significant cause of mortality in the United States. The objective of this study is to implement deep learning (DL) models using computed tomography pulmonary angiography (CTPA), clinical data, and PE Severity In...

Improved pulmonary embolism detection in CT pulmonary angiogram scans with hybrid vision transformers and deep learning techniques.

Scientific reports
Pulmonary embolism (PE) represents a severe, life-threatening cardiovascular condition and is notably the third leading cause of cardiovascular mortality, after myocardial infarction and stroke. This pathology occurs when blood clots obstruct the pul...

Identification of right ventricular dysfunction with LogNNet based diagnostic model: A comparative study with supervised ML algorithms.

Scientific reports
Right ventricular dysfunction (RVD) is strongly associated with increased mortality in patients with acute pulmonary embolism (PE), making its early detection crucial. Identifying RVD risk factors rapidly, accurately, and economically within the acut...

Machine learning model and hemoglobin to red cell distribution width ratio evaluates all-cause mortality in pulmonary embolism.

Scientific reports
The ratio of hemoglobin (Hb) to red blood cell distribution width (RDW), known as HRR, functions as an innovative indicator related to prognosis. However, whether HRR can predict the mortality for pulmonary embolism (PE) patients remains ambiguous. A...

The Current State of Artificial Intelligence on Detecting Pulmonary Embolism via Computerised Tomography Pulmonary Angiogram: A Systematic Review.

British journal of hospital medicine (London, England : 2005)
Pulmonary embolism (PE) is a life-threatening condition with significant diagnostic challenges due to high rates of missed or delayed detection. Computed tomography pulmonary angiography (CTPA) is the current standard for diagnosing PE, however, dem...

Extracting Pulmonary Embolism Diagnoses From Radiology Impressions Using GPT-4o: Large Language Model Evaluation Study.

JMIR medical informatics
BACKGROUND: Pulmonary embolism (PE) is a critical condition requiring rapid diagnosis to reduce mortality. Extracting PE diagnoses from radiology reports manually is time-consuming, highlighting the need for automated solutions. Advances in natural l...

Prognostic value of SAPS II score for 28-day mortality in ICU patients with acute pulmonary embolism.

International journal of cardiology
BACKGROUND: Acute pulmonary embolism (APE) is a common and life-threatening emergency in intensive care units (ICUs). Effective risk assessment tools are essential to improve patient outcomes. This study aims to evaluate the association between Simpl...

Prediction of pulmonary embolism by an explainable machine learning approach in the real world.

Scientific reports
In recent years, large amounts of researches showed that pulmonary embolism (PE) has become a common disease, and PE remains a clinical challenge because of its high mortality, high disability, high missed and high misdiagnosed rates. To address this...