Clinical Evidence
MammoScreen has been used and mentioned in various scientific and research projects. Discover the full list of scientific disseminations featuring both MammoScreen 2D and 3D.
Read the studies below.
Efficiency
A novel triaging model for screening mammograms based on a density AI and a cancer detection AI
S. Pacile
Artificial Intelligence for Digital Breast Tomosynthesis: A Tool to Enhance Radiologist’s Performance and Efficiency.
S. Pacile & P. Fillard
Radiologists’ time to read mammograms in “real-life” conditions using an AI for breast cancer screening.
C. Aguilar, et al.
Application of Artificial Intelligence to Mammography-Tomosynthesis Combined Images for Breast Cancer Screening.
S. Pacile, et al.
Effectiveness
Impact of a unique AI-based tool for interpretative and non-interpretative applications in breast imaging
P. Germaine, et al.
Reducing False-Positive Recalls by Adding Temporal Changes Information to an AI System for Breast Cancer Detection.
S. Pacile, et al.
Improving Breast Cancer Detection Accuracy of Mammography with the Current Use of an
Artificial Intelligence Tool.
S. Pacile, et al.
Exploring the Ability of an AI System to Differentiate Breast Microcalcifications.
S. Pacile, et al.
Can a Screening Mammography Teaching File with AI Improve Trainees’ Interpretation Skills?
R. Seidel, et al.
Breast Screening and Artificial Intelligence: An Independent Evaluation of Two Different Software Caried Out at Valenciennes Hospital.
A. Le Vourch, et al.
Impact of Artificial Intelligence in Breast Cancer Screening with Mammography.
L. Dang, et al.
Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening
Mammograms.
T. Schaffter, et al.
Learning General Cancer Distribution: Generalization of AI Models to Diagnostic Images
S. Pacile, et al.
Early Detection
Potential Benefits of an AI System in the Early Detection of Breast Cancer.
S. Pacile, et al.
A novel approach for the evaluation of artificial intelligence on consecutive screening mammograms.
S. Pacile, et al.
Time-to-event learning paradigm as a generalized approach to estimate risk of breast cancer using image-based deep learning models
T. Louis, et al.
Regional disparities in visual assessment of breast density: implications for risk stratification in breast cancer detection
G. Operto, et al.
Webinars and Presentation Recordings
No Cancer Missed: Next Generation AI for Breast Cancer Screening
P. Fillard, RSNA 2022
This video was presented in the AI Theater at RSNA 2022. Pierre Fillard, Founder & CSO, from Therapixel shares what goes into building an algorithm for breast cancer screening assessments.
Webinar: The AI Elephant in the Reading Room
Therapixel Team
Artificial Intelligence (AI) for mammography can’t be ignored. It will not take your job away, but it raises a number of questions: What is it like, how easy is it to use/understand? What is the best way to evaluate and use it in practice? Will it become the standard of care?
In this webinar, you will discover:
– The difference between AI and CAD
– What to evaluate about an AI?
– How could AI be helpful to you and your patients
In addition, you will hear from your peers and experts sharing their experience using AI