AIMC Topic: Retrospective Studies
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An AI deep learning algorithm for detecting pulmonary nodules on ultra-low-dose CT in an emergency setting: a reader study.
European radiology experimental
Nov 20, 2024
BACKGROUND: To retrospectively assess the added value of an artificial intelligence (AI) algorithm for detecting pulmonary nodules on ultra-low-dose computed tomography (ULDCT) performed at the emergency department (ED).
Explainable predictions of a machine learning model to forecast the postoperative length of stay for severe patients: machine learning model development and evaluation.
BMC medical informatics and decision making
Nov 20, 2024
BACKGROUND: Predicting the length of stay in advance will not only benefit the hospitals both clinically and financially but enable healthcare providers to better decision-making for improved quality of care. More importantly, understanding the lengt...
Prediction of benign and malignant pulmonary nodules using preoperative CT features: using PNI-GARS as a predictor.
Frontiers in immunology
Nov 20, 2024
PURPOSE: The aim of this study was to develop and validate a prediction model for classification of pulmonary nodules based on preoperative CT imaging.
Impact of Deep Learning-Based Computer-Aided Detection and Electronic Notification System for Pneumothorax on Time to Treatment: Clinical Implementation.
Journal of the American College of Radiology : JACR
Nov 19, 2024
OBJECTIVE: To assess whether the implementation of deep learning (DL) computer-aided detection (CAD) that screens for suspected pneumothorax (PTX) on chest radiography (CXR) combined with an electronic notification system (ENS) that simultaneously al...
Machine learning-based prediction of elevated N terminal pro brain natriuretic peptide among US general population.
ESC heart failure
Nov 19, 2024
AIMS: Natriuretic peptide-based pre-heart failure screening has been proposed in recent guidelines. However, an effective strategy to identify screening targets from the general population, more than half of which are at risk for heart failure or pre...
Machine learning outperforms the Canadian Triage and Acuity Scale (CTAS) in predicting need for early critical care.
CJEM
Nov 19, 2024
STUDY OBJECTIVE: This study investigates the potential to improve emergency department (ED) triage using machine learning models by comparing their predictive performance with the Canadian Triage Acuity Scale (CTAS) in identifying the need for critic...
A machine learning prediction model for Cardiac Amyloidosis using routine blood tests in patients with left ventricular hypertrophy.
Scientific reports
Nov 19, 2024
Current approaches for cardiac amyloidosis (CA) identification are time-consuming, labor-intensive, and present challenges in sensitivity and accuracy, leading to limited treatment efficacy and poor prognosis for patients. In this retrospective study...
Aneurysmal formation of periventricular anastomosis is associated with collateral development of Moyamoya disease and its rupture portends poor prognosis: detailed analysis by multivariate statistical and machine learning approaches.
Neurosurgical review
Nov 19, 2024
Periventricular anastomosis (PA) is the characteristic collateral network in Moyamoya disease (MMD). However, PA aneurysms are rare, resulting in limited knowledge of their clinical significance. We aimed to elucidate the associated factors and clini...
Classifying Tumor Reportability Status From Unstructured Electronic Pathology Reports Using Language Models in a Population-Based Cancer Registry Setting.
JCO clinical cancer informatics
Nov 19, 2024
PURPOSE: Population-based cancer registries (PBCRs) collect data on all new cancer diagnoses in a defined population. Data are sourced from pathology reports, and the PBCRs rely on manual and rule-based solutions. This study presents a state-of-the-a...
Machine Learning Identifies Clinically Distinct Phenotypes in Patients With Aortic Regurgitation.
Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography
Nov 18, 2024
BACKGROUND: Aortic regurgitation (AR) is a prevalent valve disease with a long latent period before symptoms appear. Recent data has suggested the role of novel markers of myocardial overload in assessing onset of decompensation.
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