AIMC Topic: Severity of Illness Index

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Noncontrast MRI-based machine learning and radiomics signature can predict the severity of primary lower limb lymphedema.

Journal of vascular surgery. Venous and lymphatic disorders Dec 16, 2024
OBJECTIVE: According to International Lymphology Society guidelines, the severity of lymphedema is determined by the difference in volume between the affected limb and the healthy side divided by the volume of the healthy side. However, this method o...
Retrospective Studies Female Aged Adult Radiomics Male Middle Aged Severity of Illness Index Humans Machine Learning Lymphedema Magnetic Resonance Imaging Lower Extremity Predictive Value of Tests Image Interpretation, Computer-Assisted
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Artificial intelligence-derived coronary artery calcium scoring saves time and achieves close to radiologist-level accuracy accuracy on routine ECG-gated CT.

The international journal of cardiovascular imaging Dec 16, 2024
Artificial Intelligence (AI) has been proposed to improve workflow for coronary artery calcium scoring (CACS), but simultaneous demonstration of improved efficiency, accuracy, and clinical stability have not been demonstrated. 148 sequential patients...
Vascular Calcification Computed Tomography Angiography Cardiac-Gated Imaging Techniques Workflow Observer Variation Coronary Vessels Multidetector Computed Tomography Coronary Angiography Radiologists Radiographic Image Interpretation, Computer-Assisted Coronary Artery Disease Female Humans Time Factors Reproducibility of Results Artificial Intelligence Electrocardiography Retrospective Studies Middle Aged Aged Predictive Value of Tests Severity of Illness Index Male
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Artificial Intelligence-Driven Assessment of Coronary Computed Tomography Angiography for Intermediate Stenosis: Comparison With Quantitative Coronary Angiography and Fractional Flow Reserve.

The American journal of cardiology Dec 11, 2024
We aimed to compare artificial intelligence (AI)-based coronary stenosis evaluation of coronary computed tomography angiography (CCTA) with its quantitative counterpart of invasive coronary angiography (ICA) and invasive fractional flow reserve (FFR)...
Coronary Angiography Coronary Vessels Computed Tomography Angiography Coronary Stenosis Male Middle Aged Severity of Illness Index Predictive Value of Tests Fractional Flow Reserve, Myocardial Humans Artificial Intelligence Retrospective Studies Female Aged
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Exploring the complexity of obstructive sleep apnea: findings from machine learning on diagnosis and predictive capacity of individual factors.

Sleep & breathing = Schlaf & Atmung Dec 5, 2024
PURPOSE: Obstructive sleep apnoea (OSA) is a prevalent sleep disorder characterized by pharyngeal airway collapse during sleep, leading to intermittent hypoxia, intrathoracic pressure swings, and sleep fragmentation. OSA is associated with various co...
Risk Factors Adult Male Middle Aged Severity of Illness Index Humans Machine Learning Female Sleep Apnea, Obstructive Polysomnography
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Integrating structured and unstructured data for predicting emergency severity: an association and predictive study using transformer-based natural language processing models.

BMC medical informatics and decision making Dec 5, 2024
BACKGROUND: Efficient triage in emergency departments (EDs) is critical for timely and appropriate care. Traditional triage systems primarily rely on structured data, but the increasing availability of unstructured data, such as clinical notes, prese...
Middle Aged Severity of Illness Index Triage Natural Language Processing Emergency Service, Hospital Female Humans Aged Machine Learning Adult Male
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Advances in Artificial Intelligence for automated knee osteoarthritis classification using the IKDC system.

European journal of orthopaedic surgery & traumatology : orthopedie traumatologie Dec 2, 2024
INTRODUCTION: Knee osteoarthritis is one of the most prevalent and debilitating musculoskeletal diseases, with a high incidence among the elderly population. Early detection and accurate classification can improve clinical outcomes for affected patie...
Middle Aged Severity of Illness Index Radiography Osteoarthritis, Knee Female Aged Sensitivity and Specificity Male Humans Artificial Intelligence Machine Learning
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CNN-O-ELMNet: Optimized Lightweight and Generalized Model for Lung Disease Classification and Severity Assessment.

IEEE transactions on medical imaging Dec 2, 2024
The high burden of lung diseases on healthcare necessitates effective detection methods. Current Computer-aided design (CAD) systems are limited by their focus on specific diseases and computationally demanding deep learning models. To overcome these...
Severity of Illness Index Deep Learning Lung Neoplasms Lung Diseases Image Interpretation, Computer-Assisted Humans Neural Networks, Computer Algorithms Lung
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Predicting Short-Term Mortality in Patients With Acute Pulmonary Embolism With Deep Learning.

Circulation journal : official journal of the Japanese Circulation Society Nov 30, 2024
BACKGROUND: Accurate prediction of short-term mortality in patients with acute pulmonary embolism (PE) is critical for optimizing treatment strategies and improving patient outcomes. The Pulmonary Embolism Severity Index (PESI) is the current referen...
Aged, 80 and over Multidetector Computed Tomography Humans Deep Learning Male Prognosis Predictive Value of Tests Middle Aged Female Acute Disease Severity of Illness Index Aged Pulmonary Embolism
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Severity prediction markers in dengue: a prospective cohort study using machine learning approach.

Biomarkers : biochemical indicators of exposure, response, and susceptibility to chemicals Nov 28, 2024
BACKGROUND: Dengue virus causes illnesses with or without warning indicators for severe complications. There are no clear prognostic signs linked to the disease outcomes.
Dengue Severe Dengue Severity of Illness Index Humans Platelet Count Alanine Transaminase Aspartate Aminotransferases Female Adult Male Middle Aged Machine Learning Prospective Studies Biomarkers Prognosis
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Screening for severe coronary stenosis in patients with apparently normal electrocardiograms based on deep learning.

BMC medical informatics and decision making Nov 22, 2024
BACKGROUND: Patients with severe coronary arterystenosis may present with apparently normal electrocardiograms (ECGs), making it difficult to detect adverse health conditions during routine screenings or physical examinations. Consequently, these pat...
Deep Learning Electrocardiography Coronary Stenosis Sensitivity and Specificity Humans Male Female Middle Aged Aged Severity of Illness Index
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