Cardiovascular

AutoCorNN: An Unsupervised Physics-Aware Deep Learning Model for Geometric Distortion Correction of Brain MRI Images Towards MR-Only Stereotactic Radiosurgery.

Geometric distortions in brain MRI images arising from susceptibility artifacts at air-tissue interf...

Interpretable machine learning model based on clinical factors for predicting muscle radiodensity loss after treatment in ovarian cancer.

PURPOSE: Muscle radiodensity loss after surgery and adjuvant chemotherapy is associated with poor ou...

Combining Metabolomics and Machine Learning to Identify Diagnostic and Prognostic Biomarkers in Patients with Non-Small Cell Lung Cancer Pre- and Post-Radiation Therapy.

Lung cancer is the leading cause of cancer-related deaths globally, with non-small cell lung cancer ...

Artificial intelligence-derived left ventricular strain in echocardiography in patients treated with chemotherapy.

Global longitudinal strain (GLS) is an echocardiographic measure to detect chemotherapy-related card...

Machine learning-based estimation of patient body weight from radiation dose metrics in computed tomography.

PURPOSE: Currently, precise patient body weight (BW) at the time of diagnostic imaging cannot always...

Artificial intelligence for assisted HER2 immunohistochemistry evaluation of breast cancer: A systematic review and meta-analysis.

Accurate assessment of HER2 expression in tumor tissue is crucial for determining HER2-targeted trea...

Novel Artificial Intelligence Combining Convolutional Neural Network and Support Vector Machine to Predict Colorectal Cancer Prognosis and Mutational Signatures From Hematoxylin and Eosin Images.

Reducing recurrence following radical resection of colon cancer without overtreatment or undertreatm...

Combined deep learning and radiomics in pretreatment radiation esophagitis prediction for patients with esophageal cancer underwent volumetric modulated arc therapy.

PURPOSE: To develop a combined radiomics and deep learning (DL) model in predicting radiation esopha...

Development of a deep-learning model tailored for HER2 detection in breast cancer to aid pathologists in interpreting HER2-low cases.

AIMS: Over 50% of breast cancer cases are "Human epidermal growth factor receptor 2 (HER2) low breas...

Deep learning analysis of serial digital breast tomosynthesis images in a prospective cohort of breast cancer patients who received neoadjuvant chemotherapy.

PURPOSE: Different imaging tools, including digital breast tomosynthesis (DBT), are frequently used ...

The American Society of Radiologic Technologists (ASRT) AI educator survey: A cross-sectional study to explore knowledge, experience, and use of AI within education.

INTRODUCTION: Artificial Intelligence (AI) is revolutionizing medical imaging and radiation therapy....

Nano fuzzy alarming system for blood transfusion requirement detection in cancer using deep learning.

Periodic blood transfusion is a need in cancer patients in which the disease process as well as the ...

AI-based selection of individuals for supplemental MRI in population-based breast cancer screening: the randomized ScreenTrustMRI trial.

Screening mammography reduces breast cancer mortality, but studies analyzing interval cancers diagno...

Artificial Intelligence and Cancer Clinical Research: III Risk Prediction Models for Febrile Neutropenia in Patients Receiving Cancer Chemotherapy.

A high hydrophobic moment arginine-rich peptide screened by a machine learning algorithm enhanced ADC antitumor activity.

Cell-penetrating peptides (CPPs) with better biomolecule delivery properties will expand their clini...

A non-invasive artificial intelligence model for identifying axillary pathological complete response to neoadjuvant chemotherapy in breast cancer: a secondary analysis to multicenter clinical trial.

BACKGROUND: This study aims to develop a stacking model for accurately predicting axillary lymph nod...

Artificial intelligence in radiotherapy: Current applications and future trends.

Radiation therapy has dramatically changed with the advent of computed tomography and intensity modu...

Identifying radiogenomic associations of breast cancer based on DCE-MRI by using Siamese Neural Network with manufacturer bias normalization.

BACKGROUND AND PURPOSE: The immunohistochemical test (IHC) for Human Epidermal Growth Factor Recepto...

Development and Validation Study of the Prognostic Impact of Deep Learning-Determined Myxoid Stroma After Neoadjuvant Chemotherapy in Patients with Esophageal Squamous Cell Carcinoma.

PURPOSE: This study was designed to investigate the prognostic significance of artificial intelligen...

Prediction of hepatic metastasis in esophageal cancer based on machine learning.

This study aimed to establish a machine learning (ML) model for predicting hepatic metastasis in eso...

Development of a risk prediction model for radiation dermatitis following proton radiotherapy in head and neck cancer using ensemble machine learning.

PURPOSE: This study aims to develop an ensemble machine learning-based (EML-based) risk prediction m...