AIMC Topic: Calcinosis

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Deep learning detection and classification of fungal and non-fungal calcifications on paranasal sinus CT imaging.

PloS one
This study aimed to develop and evaluate a deep learning algorithm for detecting and classifying intrasinus calcifications on paranasal sinus (PNS) computed tomography (CT) for the diagnosis of fungal sinusitis and differentiation of fungal and non-f...

Deep learning-based classification of benign and malignant breast microcalcifications in mammography.

Scientific reports
The classification of malignant versus benign microcalcifications in mammograms remains a critical yet challenging task in breast cancer screening. Deep learning models, particularly convolutional neural networks, have demonstrated promising results;...

Machine learning and multi-omics integration reveal TRPV2 as a central regulator in bicuspid aortic valve calcification.

Biochemical and biophysical research communications
BACKGROUND: Bicuspid aortic valve (BAV), the most common congenital heart defect, is strongly predisposed to early calcification, yet the molecular drivers remain poorly defined. This study aims to identify the functional role of transient receptor p...

Breast cancer classification based on the integration of diagnostic algorithms for calcifications and masses using a mixture of experts.

PloS one
PURPOSE: To investigate the effectiveness of an integrated deep-learning (DL) algorithm, the Mixture of Radiological Findings Specific Experts (MoRFSE), in breast cancer classification by imitating the diagnostic decision-making process of radiologis...

Segmentation of coronary calcifications with a domain knowledge-based lightweight 3D convolutional neural network.

Computers in biology and medicine
Cardiovascular diseases are the leading cause of death in the world, with coronary artery disease being the most prevalent. Coronary artery calcifications are critical biomarkers for cardiovascular disease, and their quantification via non-contrast c...

Deep learning based automatic quantification of aortic valve calcification on contrast enhanced coronary CT angiography.

Scientific reports
Quantifying aortic valve calcification is critical for assessing the severity of aortic stenosis, predicting cardiovascular risk, and guiding treatment decisions. This study evaluated the feasibility of a deep learning-based automatic quantification ...

Segmentation of coronary artery and calcification using prior knowledge based deep learning framework.

Medical physics
BACKGROUND: Computed tomography angiography (CTA) is used to screen for coronary artery calcification. As the coronary artery has complicated structure and tiny lumen, manual screening is a time-consuming task. Recently, many deep learning methods ha...

Negative prognostic factors and clinical improvement prediction modeling for extracorporeal shockwave therapy in calcific shoulder tendinitis using artificial intelligence techniques.

Journal of shoulder and elbow surgery
BACKGROUND: The efficacy of extracorporeal shockwave therapy (ESWT) for treating shoulder calcific tendinitis can be influenced by various prognostic factors. This study aimed to identify prognostic factors associated with the failure of ESWT for sym...

Identification and validation of the diagnostic biomarker MFAP5 for CAVD with type 2 diabetes by bioinformatics analysis.

Frontiers in immunology
INTRODUCTION: Calcific aortic valve disease (CAVD) is increasingly prevalent among the aging population, and there is a notable lack of drug therapies. Consequently, identifying novel drug targets will be of utmost importance. Given that type 2 diabe...

Artificial Intelligence-Assisted Segmentation of a Falx Cerebri Calcification on Cone-Beam Computed Tomography: A Case Report.

Medicina (Kaunas, Lithuania)
Intracranial calcifications, particularly within the falx cerebri, serve as crucial diagnostic markers ranging from benign accumulations to signs of severe pathologies. The falx cerebri, a dural fold that separates the cerebral hemispheres, presents ...