AIMC Topic: Calcinosis

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Deep transfer learning for detection of breast arterial calcifications on mammograms: a comparative study.

European radiology experimental
INTRODUCTION: Breast arterial calcifications (BAC) are common incidental findings on routine mammograms, which have been suggested as a sex-specific biomarker of cardiovascular disease (CVD) risk. Previous work showed the efficacy of a pretrained con...

Deep-learning survival analysis for patients with calcific aortic valve disease undergoing valve replacement.

Scientific reports
Calcification of the aortic valve (CAVDS) is a major cause of aortic stenosis (AS) leading to loss of valve function which requires the substitution by surgical aortic valve replacement (SAVR) or transcatheter aortic valve intervention (TAVI). These ...

MOB-CBAM: A dual-channel attention-based deep learning generalizable model for breast cancer molecular subtypes prediction using mammograms.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Deep Learning models have emerged as a significant tool in generating efficient solutions for complex problems including cancer detection, as they can analyze large amounts of data with high efficiency and performance. Recen...

Quantifying the calcification of abdominal aorta and major side branches with deep learning.

Clinical radiology
AIM: To explore the possibility of a neural network-based method for quantifying calcifications of the abdominal aorta and its branches.

Deep learning approaches for differentiating thyroid nodules with calcification: a two-center study.

BMC cancer
BACKGROUND: Calcification is a common phenomenon in both benign and malignant thyroid nodules. However, the clinical significance of calcification remains unclear. Therefore, we explored a more objective method for distinguishing between benign and m...

Deep learning performance for detection and classification of microcalcifications on mammography.

European radiology experimental
BACKGROUND: Breast cancer screening through mammography is crucial for early detection, yet the demand for mammography services surpasses the capacity of radiologists. Artificial intelligence (AI) can assist in evaluating microcalcifications on mammo...

Multitask deep learning on mammography to predict extensive intraductal component in invasive breast cancer.

European radiology
OBJECTIVES: To develop a multitask deep learning (DL) algorithm to automatically classify mammography imaging findings and predict the existence of extensive intraductal component (EIC) in invasive breast cancer.

A novel approach to quantify calcifications of thyroid nodules in US images based on deep learning: predicting the risk of cervical lymph node metastasis in papillary thyroid cancer patients.

European radiology
OBJECTIVE: Based on ultrasound (US) images, this study aimed to detect and quantify calcifications of thyroid nodules, which are regarded as one of the most important features in US diagnosis of thyroid cancer, and to further investigate the value of...

Can convolutional neural networks identify external carotid artery calcifications?

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVE: We developed and evaluated the accuracy and reliability of a convolutional neural network (CNN) in detecting external carotid artery calcifications (ECACs) in cone beam computed tomography scans.