AIMC Topic: Retrospective Studies

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Machine learning-based hemodynamics quantitative assessment of pulmonary circulation using computed tomographic pulmonary angiography.

International journal of cardiology
BACKGROUND: Pulmonary hypertension (pH) is a malignant pulmonary circulation disease. Right heart catheterization (RHC) is the gold standard procedure for quantitative evaluation of pulmonary hemodynamics. Accurate and noninvasive quantitative evalua...

Identification of key factors and explainability analysis for surgical decision-making in hepatic alveolar echinococcosis assisted by machine learning.

World journal of gastroenterology
BACKGROUND: Echinococcosis, caused by Echinococcus parasites, includes alveolar echinococcosis (AE), the most lethal form, primarily affecting the liver with a 90% mortality rate without prompt treatment. While radical surgery combined with antiparas...

History matters: Preventing severe allergic transfusion reactions.

American journal of clinical pathology
OBJECTIVE: Prior studies have shown that pretransfusion medication is not effective in preventing allergic transfusion reactions (ATRs), but these studies did not consider the patient's history of ATR. This study evaluated whether pretransfusion anti...

Prognostication in patients with idiopathic pulmonary fibrosis using quantitative airway analysis from HRCT: a retrospective study.

The European respiratory journal
BACKGROUND: Predicting shorter life expectancy is crucial for prioritising antifibrotic therapy in fibrotic lung diseases (FLDs), where progression varies widely, from stability to rapid deterioration. This heterogeneity complicates treatment decisio...

Comparison of CT referral justification using clinical decision support and large language models in a large European cohort.

European radiology
BACKGROUND: Ensuring appropriate use of CT scans is critical for patient safety and resource optimization. Decision support tools and artificial intelligence (AI), such as large language models (LLMs), have the potential to improve CT referral justif...

Enhancing pathological myopia diagnosis: a bimodal artificial intelligence approach integrating fundus and optical coherence tomography imaging for precise atrophy, traction and neovascularisation grading.

The British journal of ophthalmology
BACKGROUND: Pathological myopia (PM) has emerged as a leading cause of global visual impairment, early detection and precise grading of PM are crucial for timely intervention. The atrophy, traction and neovascularisation (ATN) system is applied to de...

Can polycythaemia vera disease be predicted from haematologic parameters? A machine learning-based study.

Journal of clinical pathology
AIMS: The aim of this research is to diagnose polycythaemia vera (PV) disease using different machine learning (ML) algorithms with complete blood count (CBC) parameters before further investigations such as Janus kinase 2 (), erythropoietin (EPO) an...

Enhanced Detection, Using Deep Learning Technology, of Medial Meniscal Posterior Horn Ramp Lesions in Patients with ACL Injury.

The Journal of bone and joint surgery. American volume
BACKGROUND: Meniscal ramp lesions can impact knee stability, particularly when associated with anterior cruciate ligament (ACL) injuries. Although magnetic resonance imaging (MRI) is the primary diagnostic tool, its diagnostic accuracy remains subopt...

Impact of a computed tomography-based artificial intelligence software on radiologists' workflow for detecting acute intracranial hemorrhage.

Diagnostic and interventional radiology (Ankara, Turkey)
PURPOSE: To assess the impact of a commercially available computed tomography (CT)-based artificial intelligence (AI) software for detecting acute intracranial hemorrhage (AIH) on radiologists' diagnostic performance and workflow in a real-world clin...

Development and external validation of a prediction model for prolonged intensive care unit stay in heart failure patients.

European journal of cardiovascular nursing
AIMS: Prolonged intensive care unit (ICU) stays in heart failure patients are associated with poor prognosis and result in high medical expenses. To develop and validate a predictive model for prolonged ICU stays in heart failure patients.