AIMC Topic: Prospective Studies
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Machine learning model for predicting DIBH non-eligibility in left-sided breast cancer radiotherapy: Development, validation and clinical impact analysis.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Jan 31, 2025
OBJECTIVE: Multi-day assessments accurately identify patients with left-sided breast cancer who are ineligible for irradiation in Deep Inspiration Breath Hold (DIBH) and minimise on-couch treatment time in those who are eligible. The challenge of imp...
Exploring the assessment of post-cardiac valve surgery pulmonary complication risks through the integration of wearable continuous physiological and clinical data.
BMC medical informatics and decision making
Jan 31, 2025
BACKGROUND: Postoperative pulmonary complications (PPCs) following cardiac valvular surgery are characterized by high morbidity, mortality, and economic cost. This study leverages wearable technology and machine learning algorithms to preoperatively ...
Machine learning to detect recent recreational drug use in intensive cardiac care units.
Archives of cardiovascular diseases
Jan 30, 2025
BACKGROUND: Although recreational drug use is a strong risk factor for acute cardiovascular events, systematic testing is currently not performed in patients admitted to intensive cardiac care units, with a risk of underdetection. To address this iss...
AI-based analysis of fetal growth restriction in a prospective obstetric cohort quantifies compound risks for perinatal morbidity and mortality and identifies previously unrecognized high risk clinical scenarios.
BMC pregnancy and childbirth
Jan 30, 2025
BACKGROUND: Fetal growth restriction (FGR) is a leading risk factor for stillbirth, yet the diagnosis of FGR confers considerable prognostic uncertainty, as most infants with FGR do not experience any morbidity. Our objective was to use data from a l...
Triaging mammography with artificial intelligence: an implementation study.
Breast cancer research and treatment
Jan 29, 2025
PURPOSE: Many breast centers are unable to provide immediate results at the time of screening mammography which results in delayed patient care. Implementing artificial intelligence (AI) could identify patients who may have breast cancer and accelera...
Fully automated segmentation and classification of renal tumors on CT scans via machine learning.
BMC cancer
Jan 29, 2025
BACKGROUND: To develop and test the performance of a fully automated system for classifying renal tumor subtypes via deep machine learning for automated segmentation and classification.
Intraindividual Comparison of Image Quality Between Low-Dose and Ultra-Low-Dose Abdominal CT With Deep Learning Reconstruction and Standard-Dose Abdominal CT Using Dual-Split Scan.
Investigative radiology
Jan 28, 2025
OBJECTIVE: The aim of this study was to intraindividually compare the conspicuity of focal liver lesions (FLLs) between low- and ultra-low-dose computed tomography (CT) with deep learning reconstruction (DLR) and standard-dose CT with model-based ite...
Predictive models of clinical outcome of endovascular treatment for anterior circulation stroke using machine learning.
Journal of neuroscience methods
Jan 28, 2025
BACKGROUND AND PURPOSE: Mechanical Thrombectomy (MT) has recently become the standard of care for anterior circulation stroke with large vessel occlusion, but predictive factors of successful MT are still not clearly defined. To tailor treatment indi...
A deep-learning system integrating electrocardiograms and laboratory indicators for diagnosing acute aortic dissection and acute myocardial infarction.
International journal of cardiology
Jan 27, 2025
BACKGROUND: Acute Stanford Type A aortic dissection (AAD-type A) and acute myocardial infarction (AMI) present with similar symptoms but require distinct treatments. Efficient differentiation is critical due to limited access to radiological equipmen...
Plasma IL-17A is increased in patients with critical MIS-C and associated to in-hospital mortality.
Frontiers in immunology
Jan 27, 2025
BACKGROUND: Multisystem inflammatory syndrome in children (MIS-C) is a rare and severe post-COVID-19 complication with multiple phenotypes.
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