AIMC Topic: Retrospective Studies
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Evaluation of the impact of artificial intelligence-assisted image interpretation on the diagnostic performance of clinicians in identifying endotracheal tube position on plain chest X-ray: a multi-case multi-reader study.
Critical care (London, England)
Jul 28, 2025
BACKGROUND: Incorrectly placed endotracheal tubes (ETTs) can lead to serious clinical harm. Studies have demonstrated the potential for artificial intelligence (AI)-led algorithms to detect ETT placement on chest X-Ray (CXR) images, however their eff...
A radiomics-based interpretable model integrating delayed-phase CT and clinical features for predicting the pathological grade of appendiceal pseudomyxoma peritonei.
BMC medical imaging
Jul 28, 2025
OBJECTIVE: This study aimed to develop an interpretable machine learning model integrating delayed-phase contrast-enhanced CT radiomics with clinical features for noninvasive prediction of pathological grading in appendiceal pseudomyxoma peritonei (P...
Prediction of 1p/19q state in glioma by integrated deep learning method based on MRI radiomics.
BMC cancer
Jul 28, 2025
PURPOSE: To predict the 1p/19q molecular status of Lower-grade glioma (LGG) patients nondestructively, this study developed a deep learning (DL) approach using radiomic to provide a potential decision aid for clinical determination of molecular strat...
Predicting Missed Appointments in Primary Care: A Personalized Machine Learning Approach.
Annals of family medicine
Jul 28, 2025
PURPOSE: Factors influencing missed appointments are complex and difficult to anticipate and intervene against. To optimize appointment adherence, we aimed to use personalized machine learning and big data analytics to predict the risk of and contrib...
Enhancing central visual field loss representation with a hybrid unsupervised approach.
International ophthalmology
Jul 28, 2025
PURPOSE: To effectively represent central visual field (VF) loss for individual patients using a hybrid unsupervised approach.
Development and validation of MRI-based radiomics model for clinical symptom stratification of extrinsic adenomyosis.
Annals of medicine
Jul 25, 2025
BACKGROUND: Extrinsic adenomyosis exhibits heterogeneous clinical symptoms, with pain being more commonly reported. The relationship between magnetic resonance imaging (MRI) feature and symptom remains unclear.
Identifying patterns of high intraoperative blood pressure variability in noncardiac surgery using explainable machine learning: a retrospective cohort study.
Annals of medicine
Jul 24, 2025
BACKGROUND: High intraoperative blood pressure variability (HIBPV) is significantly associated with postoperative adverse complications. However, practical tools to characterize perioperative factors associated with HIBPV remain limited. This study a...
Beyond labels: determining the true type of blood gas samples in ICU patients through supervised machine learning.
BMC medical informatics and decision making
Jul 24, 2025
BACKGROUND: In the Intensive Care Unit (ICU), data stored in patient data management systems (PDMS) is commonly used in clinical practice and research. Parameters from point-of-care arterial blood gas (BG) analysis are used in the diagnosis and defin...
Clinical diagnostic and prognostic value of homocysteine combined with hemoglobin [f (Hcy-Hb)] in cardio-renal syndrome caused by primary acute myocardial infarction.
Journal of translational medicine
Jul 23, 2025
BACKGROUND: Cardio-renal syndrome (CRS), characterized by multi-organ interaction, is frequently overlooked in clinical practice. It poses significant challenges in treatment, leading to poor long-term prognosis and substantial economic burden on pat...
Development and validation of risk prediction models for acute kidney disease in gout patients: a retrospective study using machine learning.
European journal of medical research
Jul 23, 2025
BACKGROUND: Limited research has been conducted on the prevalence of acute kidney injury (AKI) and acute kidney disease (AKD) in gout patients, as well as the impact of these renal complications on patient outcomes. This study aims to develop machine...
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