5 Benefits of Integrating AI Breast Imaging Tools with MagView
Research has proven the value of AI-enhanced breast imaging across modalities and applications to enhance breast cancer screening, treatment, and the overall effectiveness of patient-centered care.
Modalities in which artificial intelligence (AI) can be used for breast imaging include mammography, ultrasound, and MRI. As noted in “Artificial Intelligence in Medical Imaging of the Breast,” the value of AI in breast imaging is due to its excellent image-recognition capabilities—which is why it can be applied to critical tasks such as “the identification, segmentation, and classification of lesions; breast density assessment; and breast cancer risk assessment.”
On a practical basis, AI supports the accuracy of image interpretation, since it aids what the authors of the article describe as a common scenario: “Under a large and long-term workload, radiologists are more likely to misjudge images due to fatigue, resulting in a misdiagnosis or missed diagnosis, which can be avoided with AI. To avoid human errors, computer-aided diagnosis (CAD) has been implemented.”
In a summary of research related to the use of AI in breast imaging, the authors concluded that “At present, the CAD system for breast cancer screening has been widely studied. In mammography, ultrasound, MRI and other imaging examinations, these systems can identify and segment breast lesions, extract features, classify them, estimate BD and the risk of breast cancer, and evaluate treatment effect and prognosis. These systems show great advantages and potential in relieving pressure on doctors, optimizing resource allocation and improving accuracy.”
In addition to research findings like these, breast imaging experts from around the world are touting “How AI is Changing the Game in Breast Imaging.” In a webinar of the Beyond Breast Imaging series hosted by GE Healthcare, one expert summed it up like this: “By combining the ability of the AI with the ability of the human radiologists, we can bring our ability to diagnose disease to the next level.”
But then, if your breast center is already using one of the popular AI tools on the market, you already know all of that.
However, if you’re one of our valuable MagView customers and aren’t integrating your AI tools with your MagView system, you’re missing out on some great benefits.
How MagView Makes the Most of AI
MagView ingests AI data and uses it in different areas of the workflow to ensure there are no gaps between systems. Specifically, MagView can integrate AI tools commonly found on the market for breast imaging, such as the following.
AI-CAD with case score
Case scores can be integrated into MagView worklists via Luminary Workflow Manager (LWM) to prioritize the most pressing studies based on the score from the AI system. LWM moves studies with higher case scores to the top of the reading worklist so cases with a higher likelihood of cancer get read first.
Breast density assessment software
Dense Breast-Info describes this type of software as that which “can calculate the relative amount of dense tissue in the breast. Some software also considers if particular areas of the breast are dense (ie, masking potential).” With integration, the breast density can automatically be added to the Tyrer-Cuzick 8 risk score, and the patient’s letter.
The Koios Smart Ultrasound® system uses artificial intelligence and machine learning to provide clinical decision support (“DS”) for physicians and technologists when using ultrasound to detect and diagnose cancer. With MagView’s Smart Ultrasound® integration, the information provided by the Koios system can be sent directly to a structured report. Here’s a little more about how the Koios DS™ system works.
“AI that is not embedded into a physician’s workflow is simply a model and lacks real clinical utility. Alternatively, and through our collaboration with MagView, we are achieving maximum clinical value for all of our joint customers. Physicians access Koios DS Breast “Smart Ultrasound” through their workstation viewer and export their BIRADS rating and AI-generated findings directly into MagView’s system and patients reports. This exciting integration gives physicians what they demand; accuracy and speed, saving precious time while ensuring the highest quality care is delivered consistently. Working closely with MagView has really allowed our physician customers to realize positive results quickly.” Says Chad McClennan, President & CEO Executive Owl at Koios.
5 Benefits of Integrating AI Breast Imaging Tools with MagView
MagView is committed to integrating the latest technologies to ensure the greatest value to our customers and the patients they serve. Here are five benefits of integrating AI tools with Magview:
- Enjoy increased workflow efficiency by eliminating the need to enter AI-CAD data manually.
- Reduce callbacks through case prioritization in the reading workflow.
- Build confidence in AI-based computer-aided diagnosis (AI-CAD) through integration with MagView’s ReView Tool.
- Track interpretations and outcomes of AI-CAD recommendations through MagView’s Quality Assurance reports.
- Gain a comprehensive view of your program and how AI is helping your center play a critical role in the prevention and treatment of breast cancer.
Magview remains on the cutting edge by engaging with vendors as they gain FDA clearance for new AI technologies because we understand the increasingly critical role of AI in the fight against breast cancer. In this way, MagView will continue to lead by integrating these technologies into our products so our customers can gain even more value from what MagView offers.
“The use of AI in the breast cancer screening space has the potential to find more cancers, reduce the time to breast cancer diagnosis, and increase workflow efficiency. With MagView at the center of the breast screening workflow, we look to offer customers added value by integrating these emerging technologies in ways that make sense for clinical stakeholders. With an integrated workflow, customers benefit from all that AI offers without imposing a burdensome workflow on radiology staff.”
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