High-quality images. Precise detection. Enhanced patient care.

Patients are more informed than ever and are actively seeking breast health care providers who use cutting-edge technology to help improve the quality of mammography and the effectiveness of breast cancer screening.

As patient volumes surge and healthcare facilities face a critical shortage of radiologists and technologists, imaging centers are increasingly challenged with managing growing workloads without compromising quality. Leading mammography facilities and clinicians are using AI to streamline workflows, reduce the need for repeat scans and readings, and detect breast cancer faster, earlier, and more accurately, regardless of breast density.

Not all mammograms are created equal

When a woman goes in for her screening mammogram, she assumes that the technologist can take a high-quality image for the radiologist to review. However, taking a mammogram is often a technically challenging task in which positioning errors can frequently occur. Improving clinical image quality by using AI to assist technologists in evaluating positioning criteria provides higher-quality images for radiologists and AI-based detection support solutions to more effectively detect breast cancer. The result is fewer unnecessary patient recalls due to unmet positioning criteria and inadequate clinical image quality.

How does a high-quality image improve efficiency and compliance?

High-quality screening images result in higher mammography sensitivity and specificity, lower costs, MQSA compliance, and ultimately better patient outcomes.

Quality First: Better Image Quality Leads to Better Diagnoses

The IntelliMammo® AI Platform, powered by Densitas®, provides tools for continuous quality improvement, offering real-time and retrospective feedback to radiologic technologists through synchronous real-time feedback at the point of care and asynchronous retrospective feedback during scheduled reviews. This continuous feedback helps technologists improve their positioning and capture screening mammograms accurately, ensuring higher clinical image quality from the start.

This improved clinical image quality means that radiologists have greater confidence in their diagnostic assessments, and that resources can be directed toward patients who truly require further evaluation and treatment, rather than those with false-positive results.

Operational Efficiency: Continuous Improvement and Cost Reduction

The IntelliMammo® AI Platform also offers a comprehensive knowledge base on mammography positioning techniques, featuring content prepared by experts for technologists. This facilitates continuous learning and professional development within the organization. Advanced analytics enable facilities to identify areas for improvement at both the technologist and facility levels, allowing lower-performing technologists to be paired with higher-performing staff for timely and cost-effective training. Evidence-based standardized performance indicators support unbiased, efficient, regular, and cost-effective communication with technologists.

MQSA/ACR Compliance: Automate Tedious and Labor-Intensive Audit Preparation

The IntelliMammo® AI Platform automates the generation of mammography quality reports, freeing up healthcare professionals to focus more on patient care rather than sifting through countless images to identify ACR-compliant studies. Advanced analytics track and assess technologist performance, allowing facilities to easily flag substandard mammograms and initiate corrective actions that can be tracked until completion.

How can AI help detect breast cancer earlier and save lives?

Early detection of breast cancer is vital; the earlier it is detected while it is still localized, the more treatment options are available, which significantly improves patient outcomes.

The ProFound AI® Breast Health Suite, powered by iCAD, helps radiologists review high-quality screening images simultaneously, enabling them to quickly focus their attention on areas of potential concern. It compares findings across multiple slices and views and assigns a lesion score to each area it flags for further evaluation by the radiologist.

How does the AI identify a problem area?

The ProFound AI algorithm has been extensively trained using one of the most comprehensive 3D image datasets available. Developed using over 6 million training images, approximately 8,000 biopsy-confirmed cancers, and contributions from over 100 centers to ensure data diversity (as of June 2024), the AI algorithm provides radiologists with evidence-based recommendations to inform their treatment plans.

The Art of Medicine & the next generation Science of Radiology

Radiologists are trained experts in interpreting mammograms, and AI is their trusted partner in improving the specificity and sensitivity of their interpretations and the overall patient experience. By combining the detailed analysis from ProFound AI with the expertise of radiologists, the accuracy of cancer detection is significantly enhanced.5,6 This synergy improves patient outcomes, reduces the likelihood of missed diagnoses, and ensures timely treatment for those with breast cancer.

ProFound AI evaluates lesion scores and breast density and provides an overall case score indicating the level of suspicion for breast cancer based on the screening population. These scores help radiologists develop more accurate treatment plans by combining AI-driven analysis with their clinical expertise.

Elevating Patient Care through High-Quality Images & Accurate Detection

The IntelliMammo® AI Platform and ProFound AI® Breast Health Suite work seamlessly with the imaging center’s existing equipment to provide a comprehensive solution for improving mammography quality. Together, they enhance workflow efficiency, image quality, and diagnostic accuracy, leading to better patient outcomes and more efficient use of resources for cost-effective and sustainable breast screening services.

The combined effect of improved image quality and enhanced detection capabilities leads to better patient care by reducing false positives and false negatives and increasing the sensitivity and specificity of mammography. This results in fewer unnecessary follow-ups, reduced unnecessary radiation exposure, and fewer invasive procedures.

As a result, patient satisfaction is higher, and patients have a better experience and greater confidence in breast screening programs, leading to higher participation rates in breast screening—which is crucial for early detection and treatment—ultimately saving more lives.

References:

1.https://www.fda.gov/radiation-emitting-products/mqsa-insights/poor-positioning-responsible-most-clinical-image-deficiencies-failures
2. Densitas customer profile: Mammography facility processing 50,000 mammograms annually.
3. Guertin MH, et al. Clinical image quality in daily practice of breast cancer mammography screening. Can Assoc Radiol J. 2014 Aug;65(3):199-206. doi: 10.1016/j.carj.2014.02.001. Epub 2014 Jun 16. PMID: 24947189
4.https://www.nationalbreastcancer.org/breast-cancer-facts
5. Conant EF, Toledano AY, Periaswamy S, Fotin SV, Go J, Boatsman JE, Hoffmeister JW. Improving Accuracy and Efficiency with Concurrent Use of Artificial Intelligence for Digital Breast Tomosynthesis. Radiol Artif Intell. 2019 Jul 31;1(4):e180096. doi: 10.1148/ryai.2019180096. PMID: 32076660; PMCID: PMC6677281.
6. Schilling, K. Real-World breast cancer screening performance with digital breast tomosynthesis before and after implementation of an artificial intelligence detection system. Research presentation presented at the European Congress of Radiology (ECR) 2023; March 1–5, 2023; Vienna, Austria.