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.


A novel triaging model for screening mammograms based on a density AI and a cancer detection AI

S. Pacile

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Artificial Intelligence for Digital Breast Tomosynthesis: A Tool to Enhance Radiologist’s Performance and Efficiency.

S. Pacile & P. Fillard

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Radiologists’ time to read mammograms in “real-life” conditions using an AI for breast cancer screening.

C. Aguilar, S. Pacilè, N. Weber, and P. Fillard, “Monitoring Methodology for an AI Tool for Breast Cancer Screening Deployed in Clinical Centers,” Life, vol. 13, no. 2, p. 440, Feb. 2023, doi: 10.3390/life13020440

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Application of Artificial Intelligence to Mammography-Tomosynthesis Combined Images for Breast Cancer Screening.

S. Pacilè , C.Aguilar, A.Iannessi , P.Fillard (2023, May 4-7). “APPLICATION OF ARTIFICIAL INTELLIGENCE TO MAMMOGRAPHY TOMOSYNTHESIS COMBINED IMAGES FOR BREAST CANCER SCREENING” [conference presentation]. SBI 2023, National Harbor, MD

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Evaluation of a Multi-Instant Multimodal Artificial Intelligence System Supporting Interpretive and Noninterpretive Functions.

S. Pacilè, P. Germaine, C. Sclafert, T. Bertinotti, P. Fillard, and S. Singla Long, “Evaluation of a Multi-Instant Multimodal Artificial Intelligence System Supporting Interpretive and Noninterpretive Functions,” J. Breast Imaging, doi: 10.1093/jbi/wbae062

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Reducing False-Positive Recalls by Adding Temporal Changes Information to an AI System for Breast Cancer Detection.

S. Pacile, et al.

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Improving Breast Cancer Detection Accuracy of Mammography with the Current Use of an
Artificial Intelligence Tool.

S. Pacilè, J. Lopez, P. Chone, T. Bertinotti, J. M. Grouin, and P. Fillard, “Improving Breast Cancer Detection Accuracy of Mammography with the Concurrent Use of an Artificial Intelligence Tool,” Radiol. Artif. Intell., vol. 2, no. 6, p. e190208, Nov. 2020, doi: 10.1148/ryai.2020190208.

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Exploring the Ability of an AI System to Differentiate Breast Microcalcifications.

S. Pacile, et al., Presented at SBI 2021

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Can a Screening Mammography Teaching File with AI Improve Trainees’ Interpretation Skills?

R. Seidel, et al., Presented at ECR 2022

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Breast Screening and Artificial Intelligence: An Independent Evaluation of Two Different Software Caried Out at Valenciennes Hospital.

A. L. Vourch, P. Edouard, and N. Laurent, “Breast screening and artificial intelligence: an independent evaluation of two different software carried out at Valenciennes hospital,” in 15th International Workshop on Breast Imaging (IWBI2020), International Society for Optics and Photonics, May 2020, p. 1151321. doi: 10.1117/12.2564129.

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Impact of Artificial Intelligence in Breast Cancer Screening with Mammography.

L.-A. Dang et al., “Impact of artificial intelligence in breast cancer screening with mammography,” Breast Cancer, Jun. 2022, doi: 10.1007/s12282-022-01375-9.

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Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms.

T. Schaffter et al., “Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms,” JAMA Netw. Open, vol. 3, no. 3, pp. e200265–e200265, Mar. 2020, doi: 10.1001/jamanetworkopen.2020.0265

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Learning General Cancer Distribution: Generalization of AI Models to Diagnostic Images

S. Pacilè, Y. Nikulin, P. Fillard, and F. Chammings, “Learning general cancer distribution: generalization of AI models to diagnostic images,” in 17th International Workshop on Breast Imaging (IWBI 2024), SPIE, May 2024, pp. 506–511. doi: 10.1117/12.3026769.

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Impact of artificial intelligence in breast cancer screening with mammography

L.-A. Dang et al., “Impact of artificial intelligence in breast cancer screening with mammography,” Breast Cancer, Jun. 2022, doi: 10.1007/s12282-022-01375-9.

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Potential Benefits of an AI System in the Early Detection of Breast Cancer.

S. Pacile, et al., Presented at SBI 2021

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A novel approach for the evaluation of artificial intelligence on consecutive screening mammograms.

S. Pacile, et al.

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Time-to-event learning paradigm as a generalized approach to estimate risk of breast cancer using image-based deep learning models

T. Louis, S. Pacile, and P. Fillard, “Time-to-event learning paradigm as a generalized approach to estimate risk of breast cancer using image-based deep learning models,” in 17th International Workshop on Breast Imaging (IWBI 2024), SPIE, May 2024, pp. 524–536. doi: 10.1117/12.3027038

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Regional disparities in visual assessment of breast density: implications for risk stratification in breast cancer detection

G. Operto, S. Pacilè, J. Guillaumin, and P. Fillard, “Regional disparities in visual assessment of breast density: implications for risk stratification in breast cancer detection,” in 17th International Workshop on Breast Imaging (IWBI 2024), SPIE, May 2024, pp. 132–140. doi: 10.1117/12.3025328

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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

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