AIMC Topic:
Middle Aged
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Human-centred AI for emergency cardiac care: Evaluating RAPIDx AI with PROLIFERATE_AI.
International journal of medical informatics
Jan 28, 2025
BACKGROUND: Chest pain diagnosis in emergency care is hindered by overlapping cardiac and non-cardiac symptoms, causing diagnostic uncertainty. Artificial Intelligence, such as RAPIDx AI, aims to enhance accuracy through clinical and biochemical data...
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...
Deep Learning-Powered CT-Less Multitracer Organ Segmentation From PET Images: A Solution for Unreliable CT Segmentation in PET/CT Imaging.
Clinical nuclear medicine
Jan 28, 2025
PURPOSE: The common approach for organ segmentation in hybrid imaging relies on coregistered CT (CTAC) images. This method, however, presents several limitations in real clinical workflows where mismatch between PET and CT images are very common. Mor...
ProCUSNet: Prostate Cancer Detection on B-mode Transrectal Ultrasound Using Artificial Intelligence for Targeting During Prostate Biopsies.
European urology oncology
Jan 28, 2025
BACKGROUND AND OBJECTIVE: To assess whether conventional brightness-mode (B-mode) transrectal ultrasound images of the prostate reveal clinically significant cancers with the help of artificial intelligence methods.
F-FDG PET/CT radiomic analysis and artificial intelligence to predict pathological complete response after neoadjuvant chemotherapy in breast cancer patients.
La Radiologia medica
Jan 28, 2025
PURPOSE: Build machine learning (ML) models able to predict pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients based on conventional and radiomic signatures extracted from baseline [F]FDG PET/CT.
Aged
Adult
Breast Neoplasms
Radiomics
Artificial Intelligence
Machine Learning
Retrospective Studies
Female
Predictive Value of Tests
Positron Emission Tomography Computed Tomography
Middle Aged
Humans
Radiopharmaceuticals
Treatment Outcome
Fluorodeoxyglucose F18
Neoadjuvant Therapy
Chemotherapy, Adjuvant
Lymphatic Metastasis
Prediction of mortality in heart failure by machine learning. Comparison with statistical modeling.
European journal of internal medicine
Jan 28, 2025
BACKGROUND: Assessing the relative performance of machine learning (ML) methods and conventional statistical methods in predicting prognosis in heart failure (HF) still remains a challenging research field.
Optimizing warfarin dosing in diabetic patients through BERT model and machine learning techniques.
Computers in biology and medicine
Jan 28, 2025
This study highlights the importance of evaluating warfarin dosing in diabetic patients, who require careful anticoagulation management. With rising rates of diabetes and cardiovascular diseases, understanding the factors influencing warfarin therapy...
Incidence and Predictors of Acute Kidney Injury Following Advanced Ovarian Cancer Cytoreduction at a Tertiary UK Centre: An Exploratory Analysis and Insights from Explainable Artificial Intelligence.
Current oncology (Toronto, Ont.)
Jan 28, 2025
BACKGROUND/OBJECTIVES: The incidence of acute kidney injury (AKI) following advanced epithelial ovarian cancer (EOC) surgery has not been extensively studied. This study aimed to investigate the incidence of AKI and identify preoperative and intraope...
Establishing a preoperative predictive model for gallbladder adenoma and cholesterol polyps based on machine learning: a multicentre retrospective study.
World journal of surgical oncology
Jan 28, 2025
BACKGROUND: With the rising diagnostic rate of gallbladder polypoid lesions (GPLs), differentiating benign cholesterol polyps from gallbladder adenomas with a higher preoperative malignancy risk is crucial. This study aimed to establish a preoperativ...
Identification of patient demographic, clinical, and SARS-CoV-2 genomic factors associated with severe COVID-19 using supervised machine learning: a retrospective multicenter study.
BMC infectious diseases
Jan 28, 2025
BACKGROUND: Drivers of COVID-19 severity are multifactorial and include multidimensional and potentially interacting factors encompassing viral determinants and host-related factors (i.e., demographics, pre-existing conditions and/or genetics), thus ...
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