AIMC Topic: Retrospective Studies
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Smart contours: deep learning-driven internal gross tumor volume delineation in non-small cell lung cancer using 4D CT maximum and average intensity projections.
Radiation oncology (London, England)
Apr 18, 2025
BACKGROUND: Delineating the internal gross tumor volume (IGTV) is crucial for the treatment of non-small cell lung cancer (NSCLC). Deep learning (DL) enables the automation of this process; however, current studies focus mainly on multiple phases of ...
Optimizing prediction of metastasis among colorectal cancer patients using machine learning technology.
BMC gastroenterology
Apr 18, 2025
BACKGROUND AND AIM: Colorectal cancer is among the most prevalent and deadliest cancers. Early prediction of metastasis in patients with colorectal cancer is crucial in preventing it from the advanced stages and enhancing the prognosis among these pa...
Unsupervised machine learning analysis of optical coherence tomography radiomics features for predicting treatment outcomes in diabetic macular edema.
Scientific reports
Apr 18, 2025
This study aimed to identify distinct clusters of diabetic macular edema (DME) patients with differential anti-vascular endothelial growth factor (VEGF) treatment outcomes using an unsupervised machine learning (ML) approach based on radiomic feature...
Diabetic Retinopathy
Retrospective Studies
Female
Aged
Radiomics
Male
Middle Aged
Angiogenesis Inhibitors
Humans
Treatment Outcome
Principal Component Analysis
Cluster Analysis
Tomography, Optical Coherence
Unsupervised Machine Learning
Macular Edema
Visual Acuity
Vascular Endothelial Growth Factor A
Daily Automated Prediction of Delirium Risk in Hospitalized Patients: Model Development and Validation.
JMIR medical informatics
Apr 18, 2025
BACKGROUND: Delirium is common in hospitalized patients and is correlated with increased morbidity and mortality. Despite this, delirium is underdiagnosed, and many institutions do not have sufficient resources to consistently apply effective screeni...
Combined peritumoral radiomics and clinical features predict 12-month progression free survival in glioblastoma.
Journal of neuro-oncology
Apr 17, 2025
PURPOSE: Analyzing post-treatment MRIs from glioblastoma patients can be challenging due to similar radiological presentations of disease progression and treatment effects. Identifying radiomics features (RFs) revealing progressive glioblastoma can c...
Development and Validation an AI Model to Improve the Diagnosis of Deep Infiltrating Endometriosis for Junior Sonologists.
Ultrasound in medicine & biology
Apr 17, 2025
OBJECTIVE: This study aims to develop and validate an artificial intelligence (AI) model based on ultrasound (US) videos and images to improve the performance of junior sonologists in detecting deep infiltrating endometriosis (DE).
A machine learning-based framework for predicting postpartum chronic pain: a retrospective study.
BMC medical informatics and decision making
Apr 17, 2025
BACKGROUND: Postpartum chronic pain is prevalent, affecting many women after delivery. Machine learning algorithms have been widely used in predicting postoperative conditions. We investigated the prevalence of and risk factors for postpartum chronic...
Automated machine learning for early prediction of systemic inflammatory response syndrome in acute pancreatitis.
BMC medical informatics and decision making
Apr 17, 2025
BACKGROUND: Systemic inflammatory response syndrome (SIRS) is a frequent and serious complication of acute pancreatitis (AP), often associated with increased mortality. This study aims to leverage automated machine learning (AutoML) algorithms to cre...
Machine learning center-specific models show improved IVF live birth predictions over US national registry-based model.
Nature communications
Apr 17, 2025
Expanding in vitro fertilization (IVF) access requires improved patient counseling and affordability via cost-success transparency. Clinicians ask how two types of live birth prediction (LBP) models perform: machine learning, center-specific (MLCS) m...
Enhancing Physician-Patient Communication in Oncology Using GPT-4 Through Simplified Radiology Reports: Multicenter Quantitative Study.
Journal of medical Internet research
Apr 17, 2025
BACKGROUND: Effective physician-patient communication is essential in clinical practice, especially in oncology, where radiology reports play a crucial role. These reports are often filled with technical jargon, making them challenging for patients t...
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