Clairity’s approach is grounded in decades of work focused on the early identification of cancer. Review recent examples of research and news regarding Clairity and Clairity co-founder Dr. Connie Lehman, MD, PhD, related to computer aided diagnosis, deep learning, artificial intelligence, density assessment, predictive risk models, effective imaging workflow and risk-based triage.
Deep learning breast cancer risk models demonstrate improved accuracy compared to traditional risk models. This research highlights the promise deep learning models to support equitable, risk-based mammography screening.
Accurate density assessments could help health care systems more appropriately use limited supplemental screening resources and help better inform traditional clinical risk models.
The COVID-19 pandemic took a toll on mammography screening volumes among women of color, slashing exam uptake by more than 50% between 2019 and 2020.
Inconsistency in density assessment of mammograms has been widely recognized for the potential to cause patient anxiety and result in unnecessary procedures. Using a deep learning (DL) algorithm to assess mammographic breast density can reduce this inconsistency.
Recent deep learning (DL) approaches have shown promise in improving sensitivity but have not addressed limitations in radiologist specificity or efficiency.
The development of an AI-based system for early detection of breast cancer has the ability to predict whether a patient is likely to develop the disease in the next five years. A technology that had not yet penetrated the hospital setting now has the potential to save thousands of lives each year.
Deep learning models that use full-field mammograms yield substantially improved risk discrimination compared with the Tyrer-Cuzick (version 8) model.
Improved breast cancer risk models enable targeted screening strategies that achieve earlier detection and less screening harm than existing guidelines.
Physicians still lack consensus on the meaning of race. When the Journal took up the topic in 2003 with a debate about the role of race in medicine, one side argued that racial and ethnic categories reflected underlying population genetics and could be clinically useful. Others held that any small benefit was outweighed by potential harms that arose from the long, rotten history of racism in medicine.
