Google has announced that it has licensed its AI technology to iCAD, a company whose services are primarily established around detecting breast cancer in healthcare facilities. iCAD has already included its AI strategies for its cancer screening services, but from now on, it will also be including Google’s algorithm that Google has built with a team of researchers at Northern University. Google’s mammogram algorithm outperformed radiologists in terms of logging fewer false positives and false negatives when reading the images.
The study included mammograms from over 91,000 women in the United States and the United Kingdom. The machine learning algorithm also reduced false positives by 9% in the United States and nearly 3% in the United Kingdom. It has been mentioned that Google’s algorithm incorporates a huge number of images beyond breast tissues for the refinement of the Machine Learning process. This facility is now commercially available globally for the 7500 mammography sites whose services are iCAD based. As part of the partnership agreement, The technology will be further developed and refined by iCAD and Google.
This algorithm can lower the burden on radiologists; however, it cannot replace radiologists, at least not in the near future. It is also anticipated that the AI-based system will make mammography available to a greater number of people worldwide, particularly in low-resource areas that cannot support the infrastructure required to host mammography image storage hardware.
iCAD has planned to incorporate Google’s AI-based mammography research model into the existing tools. The first is its “ProFound AI” tool, which analyses images from Digital Breast Tomosynthesis (DBT) (3D mammography ). The tool looks for malignant soft tissue densities and calcifications in DBT images. This tool can also provide personalized breast cancer estimation to each individual.
The more data from mammograms fed into the algorithm, as with any machine learning system, the more refined it becomes at detecting minute Differences that distinguish between normal and potentially cancerous tissue. Women who undergo mammograms using the AI-based system will have their data returned to the algorithm without any identifying information. At the moment, most women getting mammograms are likely unaware that an AI system may be working in the background, supplementing the radiologist, because no regulatory agencies have approved an entirely AI-based interpretation of mammograms.
These kinds of machine-based systems can detect patterns that are invisible to the human eye. The current AI algorithm used by iCAD can detect even minute calcifications in breast tissues that might lead to an increased risk of heart disease. If that link is confirmed, mammograms could be used to assess women’s risk of heart disease. Including an AI perspective in mammograms could help to improve how women’s breast cancer risk is determined.
AI systems, for example, can better distinguish differences that are specific to specific racial and ethnic groups; for example, African-American women, the women of the United States are more probable than other women to develop more aggressive types of breast cancer and to die from the disease, so training an AI system to detect and early detection of these cancers may result in better outcomes. If an AI algorithm detects these differences, the women may be referred for further testing to determine whether they are at a higher risk of developing cancer. This could help them get treatment sooner, giving them a better chance of survival. This could also mean less expensive medical services, resulting in healthcare cost savings.
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Avanthy Yeluri is a Dual Degree student at IIT Kharagpur. She has a strong interest in Data Science because of its numerous applications across a variety of industries, as well as its cutting-edge technological advancements and how they are employed in daily life.