Original article was published by on AI Magazine
AI shows promise for breast cancer screening, says QF researcher
18 Oct 2020 – 9:05
Dr. Halima Bensmail, Principal Scientist and Associate Professor at Qatar Computing Research Institute, HBKU.
Doha: Artificial Intelligence (AI) models are being developed and used to predict breast cancer in mammography scans with more accuracy than radiologists, thereby reducing false positives and false negatives.
AI will become more common in breast cancer screening within the next ten years, said a Qatar Foundation (QF) researcher.
“When using the naked eye to define abnormalities in image data or while analysing tissue, one could go wrong in the analysis. However, with artificial intelligence, classification of abnormal or normal tissue is more accurate,” said Dr. Halima Bensmail, the Principal Scientist and Associate Professor at Qatar Computing Research Institute, part of QF’s Hamad Bin Khalifa University.
“Due to the extensive variation from patient to patient data, traditional learning methods are not reliable, and machine learning has evolved over the last few years with its ability to sift through complex and big data to be able to detect abnormalities,” she told The Peninsula.
Similar to other countries in the region, Qatar has one of the highest breast cancer incidence and mortality rates. Regular screening and early detection are crucial for breast cancer. There are different breast diagnostic approaches, such as mammography, magnetic resonance imaging (MRI), and ultrasound among others. And the use of Artificial Intelligence (AI) in these diagnostic technologies is becoming increasingly popular. Currently, digital mammography is used as the standard method for early breast cancer detection, but it appears to have its limitations, and AI is coming to its rescue.
According to Dr. Bensmail, in the area of digital imaging, Qatar’s mammography image quality is deemed adequate, as per a study titled ‘Breast Cancer Detection in Qatar: Evaluation of Mammography Image Quality Using A Standardized Assessment Tool’, funded by QF’s Qatar National Research Fund. But the study also notes that as the country develops additional capacity and awareness for mammography screening, it will be important to continuously monitor image quality.
“People will always ask, what is the accuracy of your prediction, or what is more important when designing a machine learning model: model performance or model accuracy. This answer depends on the application and the field. But so far, we don’t have any machine learning algorithm or artificial intelligence model that gives us 100 percent accuracy of the prediction,” said Dr. Bensmail.
A lot of AI is built on machine learning. In machine learning, scientists train the system to learn something very specific, such as bad breast tissue versus good breast tissue through images. By training the system with massive amounts of data, it learns to differentiate between bad tissue and good tissue. Over time, the algorithm learns to predict with great accuracy.
Dr. Bensmail said AI algorithms such as deep learning and neural network-based algorithm offer extremely good results in breast cancer detection — they provide 90 to 97 percent accuracy of image data, such as in mammograms. However, when enough data isn’t available, machine learning or AI models cannot be effectively built and this is a challenge in the region.
With AI advancing rapidly, Dr. Bensmail predicts that within the next 10 years, AI will become even more common in clinical practice. She said that predicting a disease, particularly classifying breast cancer in the radiology department, is something that is happening rapidly, specifically in the area of image data analysis.