In order to improve the thickness performance, we develop a deep learning model based on multi-modality that extracts a feature vector from the two types of ...
result that achieved 96% accuracy for the thickness. Keywords— Multiclass Classification, Blood Clot, Thickness. Prediction, Multi-Modal Deep Learning. I ...
A deep learning model based on multi-modality that extracts a feature vector from the two types of data and infers the thickness of a blood clot and shows a ...
Multi Modal Deep Learning Based on Feature Attention for Prediction of Blood Clot Elasticity · Jiseon Moon · Sangil Ahn · Min Gyu Joo · Jitae Shin.
The modelling and prediction field boasts various practical applications, such as deep learning, which is a powerful tool used in this field.
AI may aid in the diagnosis and prediction of venous thrombosis, demonstrating high sensitivity, specificity and area under the SROC curve values.
Missing: Multi- Modal Thickness
Feb 16, 2024 · Multimodal CT, radiomic, and histological analysis of stroke clots can help bridge the gap between pre-treatment imaging and clot pathobiology.
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Jan 5, 2024 · This study aimed to create and validate predictive models for DVT with the help of machine learning (ML) algorithms.
Oct 4, 2023 · To systematically review the performance of AI in the diagnosis and prediction of VTE and compare it to clinical risk assessment models (RAMs) or logistic ...
The paper describes particular approach how to apply Artificial Intelligence for purposes of separating two major acute ischemic stroke (AIS) etiology subtypes:.