Deep Learning vs Traditional Breast Cancer Risk Models to Support Risk-Based Mammography Screening
Deep learning breast cancer risk models demonstrate improved accuracy compared to traditional risk models. This research highlights the …
Impact of a Deep Learning Model for Predicting Mammographic Breast Density in Routine Clinical Practice
Accurate density assessments could help health care systems more appropriately use limited supplemental screening resources and help …
Screening Mammography Recovery After COVID-19 Pandemic Facility Closures: Associations of Facility Access and Racial and Ethnic Screening Disparities
The COVID-19 pandemic took a toll on mammography screening volumes among women of color, slashing exam uptake by more than 50% between …
Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation
Inconsistency in density assessment of mammograms has been widely recognized for the potential to cause patient anxiety and result in …
A Deep Learning Model to Triage Screening Mammograms: A Simulation Study
Recent deep learning (DL) approaches have shown promise in improving sensitivity but have not addressed limitations in radiologist …
A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction
Deep learning models that use full-field mammograms yield substantially improved risk discrimination compared with the Tyrer-Cuzick …
Towards Robust Mammography-based Models for Breast Cancer Risk
Improved breast cancer risk models enable targeted screening strategies that achieve earlier detection and less screening harm than …
Hidden in Plain Sight — Reconsidering the Use of Race Correction in Clinical Algorithms
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 …
