AIMC Topic: Retrospective Studies

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Virtual monochromatic image-based automatic segmentation strategy using deep learning method.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
BACKGROUND AND PURPOSE: The image quality of single-energy CT (SECT) limited the accuracy of automatic segmentation. Dual-energy CT (DECT) may potentially improve automatic segmentation yet the performance and strategy have not been investigated thor...

Automated machine learning model for predicting anastomotic strictures after esophageal cancer surgery: a retrospective cohort study.

Surgical endoscopy
BACKGROUND: Anastomotic strictures (AS) frequently occurs in patients following esophageal cancer surgery, significantly affecting their long-term quality of life. This study aims to develop a machine learning model to predict high-risk AS, enabling ...

A magnetic resonance imaging (MRI)-based deep learning radiomics model predicts recurrence-free survival in lung cancer patients after surgical resection of brain metastases.

Clinical radiology
AIM: To develop and validate a magnetic resonance imaging (MRI)-based deep learning radiomics model (DLRM) to predict recurrence-free survival (RFS) in lung cancer patients after surgical resection of brain metastases (BrMs).

AI-based prediction of left bundle branch block risk post-TAVI using pre-implantation clinical parameters.

Future cardiology
BACKGROUND AND AIMS: Transcatheter Aortic Valve Implantation (TAVI) has revolutionized the treatment of severe aortic stenosis. Although its clinical efficacy is well established, the development of new-onset left bundle branch block (LBBB) following...

Multidimensional Feature Analysis of Meniere's Disease and Vestibular Migraine: Insights from Machine Learning and Vestibular Testing.

Journal of the Association for Research in Otolaryngology : JARO
OBJECTIVE: Differentiating between Meniere's disease (MD) and vestibular migraine (VM) is challenging due to overlapping symptoms and limited diagnostic tools. Traditional statistical methods often rely on physician judgment and struggle with complex...

Predicting Severe Postoperative Complications after CRS-HIPEC: An Externally Validated Machine-Learning Tool.

World journal of surgery
INTRODUCTION: Current decision support tools designed to predict postoperative complications, following cytoreductive surgery with hyperthermic intraperitoneal chemotherapy (CRS-HIPEC), are limited by small sample sizes and lack of external validatio...

Machine Learning for Predicting Waitlist Mortality in Pediatric Heart Transplantation.

Pediatric transplantation
BACKGROUND: Waitlist mortality remains a critical issue for pediatric heart transplant (HTx) candidates, particularly for candidates with congenital heart disease. Listing center organ offer acceptance practices have been identified as a factor influ...

Development of a deep neural network model for ultra-early neurological deterioration in ischemic stroke and analysis of associated risk factors.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
BACKGROUND: In this study, we established a deep neural network (DNN)-based predictive model, aiming to provide a basis for improving the treatment prognosis of early neurological deterioration (END) in patients with ultra-early ischemic stroke after...

A framework to create, evaluate and select synthetic datasets for survival prediction in oncology.

Computers in biology and medicine
BACKGROUND AND PURPOSE: Data-driven decision-making in radiation oncology (RO) relies on integrating real-world data effectively. Synthetic data (SD), generated through machine learning, offers a solution by mimicking real-world data without compromi...