Background Limited data are available regarding the accuracy of artificial intelligence (AI) algorithms trained on bilateral mammograms for second breast cancer surveillance in patients with a personal history of breast cancer treated with unilateral...
BACKGROUND: Breast cancer (BC) is a major global health concern with rising incidence and mortality rates in many developing countries. Effective BC risk assessment models are crucial for prevention and early detection. While the Gail model, a tradit...
The international journal of medical robotics + computer assisted surgery : MRCAS
Apr 1, 2025
BACKGROUND: At present, breast cancer has become the cancer with the highest incidence rate in the world. Breast intervention robot is an important biopsy or targeted therapy method for breast diseases.
OBJECTIVE: Artificial intelligence (AI) has been shown to hold promise for improving breast cancer screening, offering advanced capabilities to enhance diagnostic accuracy and efficiency. This study aimed to evaluate the impact of a multimodal multi-...
OBJECTIVE: The performance of a commercially available artificial intelligence (AI)-based software that detects breast arterial calcifications (BACs) on mammograms is presented.
The complementary information found in different modalities of patient data can aid in more accurate modelling of a patient's disease state and a better understanding of the underlying biological processes of a disease. However, the analysis of multi...
BACKGROUND: Trophoblast cell surface antigen 2 (TROP2) is a promising target for various cancers, including breast cancer. The development of noninvasive techniques for assessing TROP2 expression in tumors holds considerable importance. This study ai...
Background The ScreenTrustCAD trial was a prospective study that evaluated the cancer detection rates for combinations of artificial intelligence (AI) computer-aided detection (CAD) and two radiologists. The results raised concerns about the tendency...
BACKGROUNDS: Exploring the molecular features that drive breast cancer invasion and migration remains an important biological and clinical challenge. In recent years, the use of interpretable machine learning models has enhanced our understanding of ...
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