AIMC Topic: Retrospective Studies
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Interpretable machine learning for thyroid cancer recurrence predicton: Leveraging XGBoost and SHAP analysis.
European journal of radiology
Mar 14, 2025
PURPOSE: For patients suffering from differentiated thyroid cancer (DTC), several clinical, laboratory, and pathological features (including patient age, tumor size, extrathyroidal extension, or serum thyroglobulin levels) are currently used to ident...
Subphenotyping prone position responders with machine learning.
Critical care (London, England)
Mar 14, 2025
BACKGROUND: Acute respiratory distress syndrome (ARDS) is a heterogeneous condition with varying response to prone positioning. We aimed to identify subphenotypes of ARDS patients undergoing prone positioning using machine learning and assess their a...
Developing a machine learning-based predictive model for levothyroxine dosage estimation in hypothyroid patients: a retrospective study.
Frontiers in endocrinology
Mar 14, 2025
Hypothyroidism, a common endocrine disorder, has a high incidence in women and increases with age. Levothyroxine (LT4) is the standard therapy; however, achieving clinical and biochemical euthyroidism is challenging. Therefore, developing an accurate...
An explainable web application based on machine learning for predicting fragility fracture in people living with HIV: data from Beijing Ditan Hospital, China.
Frontiers in cellular and infection microbiology
Mar 14, 2025
PURPOSE: This study aimed to develop and validate a novel web-based calculator using machine learning algorithms to predict fragility fracture risk in People living with HIV (PLWH), who face increased morbidity and mortality from such fractures.
Deep learning for the prediction of acute kidney injury after coronary angiography and intervention in patients with chronic kidney disease: a model development and validation study.
Renal failure
Mar 13, 2025
BACKGROUND: Patients with chronic kidney disease (CKD) are considered the primary population at risk for post-contrast acute kidney injury (PC-AKI), yet there are few predictive tools specifically designed for this vulnerable population.
DCE-MRI based deep learning analysis of intratumoral subregion for predicting Ki-67 expression level in breast cancer.
Magnetic resonance imaging
Mar 13, 2025
OBJECTIVE: To evaluate whether deep learning (DL) analysis of intratumor subregion based on dynamic contrast-enhanced MRI (DCE-MRI) can help predict Ki-67 expression level in breast cancer.
Machine learning prediction of right ventricular volume and ejection fraction from two-dimensional echocardiography in patients with pulmonary regurgitation.
The international journal of cardiovascular imaging
Mar 13, 2025
Right ventricular (RV) end-diastolic volume (RVEDV) and ejection fraction (RVEF) by cardiac MRI (cMRI) guide management in chronic pulmonary regurgitation (PR). Two-dimensional echocardiography suboptimally correlate with RV volumes. This study teste...
Ventricular Dysfunction, Right
Image Interpretation, Computer-Assisted
Ventricular Function, Right
Pulmonary Valve Insufficiency
Reproducibility of Results
Young Adult
Echocardiography
Humans
Adult
Severity of Illness Index
Machine Learning
Adolescent
Male
Prognosis
Chronic Disease
Stroke Volume
Retrospective Studies
Predictive Value of Tests
Female
Development and evaluation of a deep learning framework for pelvic and sacral tumor segmentation from multi-sequence MRI: a retrospective study.
Cancer imaging : the official publication of the International Cancer Imaging Society
Mar 13, 2025
BACKGROUND: Accurate segmentation of pelvic and sacral tumors (PSTs) in multi-sequence magnetic resonance imaging (MRI) is essential for effective treatment and surgical planning.
Integration of intratumoral and peritumoral CT radiomic features with machine learning algorithms for predicting induction therapy response in locally advanced non-small cell lung cancer.
BMC cancer
Mar 13, 2025
OBJECTIVES: To extract intratumoral, peritumoral, and integrated intratumoral-peritumoral CT radiomic features, develop multi-source radiomic models using various machine learning algorithms to identify the optimal model, and integrate clinical facto...
Super-resolution deep learning reconstruction for improved quality of myocardial CT late enhancement.
Japanese journal of radiology
Mar 12, 2025
PURPOSE: Myocardial computed tomography (CT) late enhancement (LE) allows assessment of myocardial scarring. Super-resolution deep learning image reconstruction (SR-DLR) trained on data acquired from ultra-high-resolution CT may improve image quality...
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