AIMC Topic: Middle Aged

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Identification of A0 minimum ablative margins for colorectal liver metastases: multicentre, retrospective study using deformable CT registration and artificial intelligence-based autosegmentation.

The British journal of surgery
BACKGROUND: Several ablation confirmation software methods for minimum ablative margin assessment have recently been developed to improve local outcomes for patients undergoing thermal ablation of colorectal liver metastases. Previous assessments wer...

Multimodal ischemic stroke recurrence prediction model based on the capsule neural network and support vector machine.

Medicine
Ischemic stroke (IS) has a high recurrence rate. Machine learning (ML) models have been developed based on single-modal biochemical tests, and imaging data have been used to predict stroke recurrence. However, the prediction accuracy of these models ...

Machine learning-driven risk assessment of coronary heart disease: Analysis of NHANES data from 1999 to 2018.

Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences
OBJECTIVES: The high incidence of coronary artery heart disease (CHD) poses a significant burden and challenge to public health systems globally. Effective prevention and early diagnosis of CHD have become key strategies to alleviate this burden. Thi...

Prospective deep learning-based quantitative assessment of coronary plaque by computed tomography angiography compared with intravascular ultrasound: the REVEALPLAQUE study.

European heart journal. Cardiovascular Imaging
AIMS: Coronary computed tomography angiography provides non-invasive assessment of coronary stenosis severity and flow impairment. Automated artificial intelligence (AI) analysis may assist in precise quantification and characterization of coronary a...

Machine Learning-Based Predictive Models for Early Detection of Cardiovascular Diseases: A Study Utilizing Patient Samples from a Tertiary Health Promotion Center in Korea.

Studies in health technology and informatics
A machine learning model was developed for cardiovascular diseases prediction based on 21,118 patient checkups data from a tertiary medical institution in Seoul, Korea, collected between 2009 and 2021. XGBoost algorithm showed the highest predictive ...

Machine Learning with Clinical and Intraoperative Biosignal Data for Predicting Cardiac Surgery-Associated Acute Kidney Injury.

Studies in health technology and informatics
Early identification of patients at high risk of cardiac surgery-associated acute kidney injury (CSA-AKI) is crucial for its prevention. We aimed to leverage perioperative clinical and intraoperative biosignal data to develop machine learning models ...

Vertebral compression fractures at abdominal CT: underdiagnosis, undertreatment, and evaluation of an AI algorithm.

Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research
Vertebral compression fractures (VCFs) are common and indicate a high future risk of additional osteoporotic fractures. However, many VCFs are unreported by radiologists, and even if reported, many patients do not receive treatment. The purpose of th...

Fracture risk prediction in postmenopausal women with traditional and machine learning models in a nationwide, prospective cohort study in Switzerland with validation in the UK Biobank.

Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research
Fracture prediction is essential in managing patients with osteoporosis and is an integral component of many fracture prevention guidelines. We aimed to identify the most relevant clinical fracture risk factors in contemporary populations by training...

Machine learning-based analysis identifies and validates serum exosomal proteomic signatures for the diagnosis of colorectal cancer.

Cell reports. Medicine
The potential of serum extracellular vesicles (EVs) as non-invasive biomarkers for diagnosing colorectal cancer (CRC) remains elusive. We employed an in-depth 4D-DIA proteomics and machine learning (ML) pipeline to identify key proteins, PF4 and AACT...