AI Tools Used in the Early Detection of Breast Cancer
Breast Cancer Awareness Month may be coming to a close, but the early detection of breast cancer is always a priority. When breast cancer is detected early, before it has had the opportunity to spread to the lymph nodes or other parts of the body, the 5-year survival rate is much higher. Information found on Cancer.net indicates:
- The average 5-year survival rate for women in the United States with non-metastatic (cancer that has not spread) breast cancer is 90%
- If cancer has spread to the regional lymph nodes, the 5-year survival rate is 86%
- When cancer has spread to distant parts of the body, the 5-year survival rate is 29%
This week’s Domain Knowledge reviews AI tools used in the early detection of breast cancer.
Case Study: AI Detects Subtle Lesions
In a clinical study, cancer was detected utilizing AI technology one year earlier than by an unassisted radiologist reading. These findings suggest the use of AI supports the timely detection of breast cancer.
Case Study: AI Improves Radiologist Performance in Breast Cancer Detection
A clinical study compared radiologists’ performance reading with and without AI technology. In this case study, after a false negative case, the original reader found cancer a year after the initial mammogram. The lesion was flagged by AI via tomosynthesis a year earlier. These findings suggest that AI-guided decision-making support is reliable in assisting in the early detection of breast cancer.
Case Study: AI Can Alleviate Breast Ultrasound Workload
With the large volume of imaging studies performed daily, it is essential to have a second set of eyes to assist radiologists. AI technology can improve diagnosis performance and speed up a cancer diagnosis. In this case study, the radiologists and breast surgeons improved their diagnostic performance in detecting and diagnosing breast lesions on breast ultrasound images with the assistance an AI system.
Case Study: AI Confirms Radiologist’s Suspicion
A clinical study compared the performance of radiologist readings with and without AI technology. In this case study, all radiologists in the reader study found cancer when assisted by AI technology. Five radiologists missed cancer when unassisted by AI technology. The AI-guided detection support can confirm radiologists’ suspicion and assist in making an accurate, timely breast cancer diagnosis.
That’s a wrap for this week’s review of news and happenings in the healthcare AI space. In closing, I’ll leave you with an invitation to learn more about the benefits of using an AI Hub to manage multiple applications across your clinical needs and offer you a personal demo of Ferrum’s platform and growing AI catalog.
If you have AI tips, suggestions, or resources you’d like to share, leave us a note below, and please feel free to suggest topics you would like to see covered in future posts.