Salivary metabolomics with artificial intelligence-based methods for breast cancer detection and subtype prediction

2020 ◽  
Vol 138 ◽  
pp. S18
Author(s):  
T. Murata ◽  
T. Yanagisawa ◽  
T. Kurihara ◽  
M. Kaneko ◽  
S. Ota ◽  
...  
2020 ◽  
Vol 2 (6) ◽  
pp. e190208
Author(s):  
Serena Pacilè ◽  
January Lopez ◽  
Pauline Chone ◽  
Thomas Bertinotti ◽  
Jean Marie Grouin ◽  
...  

2020 ◽  
Vol 2 (4) ◽  
pp. 304-314
Author(s):  
Manisha Bahl

Abstract Artificial intelligence (AI) is a branch of computer science dedicated to developing computer algorithms that emulate intelligent human behavior. Subfields of AI include machine learning and deep learning. Advances in AI technologies have led to techniques that could increase breast cancer detection, improve clinical efficiency in breast imaging practices, and guide decision-making regarding screening and prevention strategies. This article reviews key terminology and concepts, discusses common AI models and methods to validate and evaluate these models, describes emerging AI applications in breast imaging, and outlines challenges and future directions. Familiarity with AI terminology, concepts, methods, and applications is essential for breast imaging radiologists to critically evaluate these emerging technologies, recognize their strengths and limitations, and ultimately ensure optimal patient care.


Author(s):  
Bifta Sama Bari ◽  
Sabira Khatun ◽  
Kamarul Hawari Ghazali ◽  
Md. Moslemuddin Fakir ◽  
Wan Nur Azhani W. Samsudin ◽  
...  

Breast Cancer ◽  
2020 ◽  
Vol 27 (4) ◽  
pp. 642-651
Author(s):  
Michiro Sasaki ◽  
Mitsuhiro Tozaki ◽  
Alejandro Rodríguez-Ruiz ◽  
Daisuke Yotsumoto ◽  
Yumi Ichiki ◽  
...  

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