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Using artificial intelligence to predict which women will develop breast cancer

Micrograph showing a lymph node invaded by ductal breast carcinoma, with extension of the tumour beyond the lymph node. Credit: Nephron/Wikipedia

A team of researchers with members from institutions in the U.S., Sweden and Taiwan has developed an artificial intelligence system for predicting breast cancer years before tumors appear. In their paper published in the journal Science Translational Medicine, the group describes how they developed and trained their system and how well it worked when tested.

Artificial intelligence applications have made inroads to medical diagnostics in recent years—they can be trained to look for or other conditions by training them on thousands of examples. Once trained, many have demonstrated good performance in real-world applications. In this new effort, the researchers have taken the science further by building an AI application to detect conditions in tissue that may lead to cancer in the future.

For many years, medical scientists have been looking for ways to determine breast cancer risk. Some DNA studies have led to the discovery of variants that increase the risk of developing the disease. Other studies have used a variety of factors along with analytics to predict risk, but thus far, they have not proven to be accurate enough. In this new effort, the researchers used mammogram data for women who had been screened multiple times over several years. They trained the system to study the mammogram histories of women that had eventually developed breast cancer and then used the system to predict cancer risk of women in the future based on their own mammogram historical data.

Testing of the system, which the team named Miria, occurred over five years—enough time to determine if women tested at the beginning of the study developed or not. The researchers found that Mirai had correctly predicted

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Mugwort | NCCIH

What Do We Know About Safety?

  • Little is known about whether it’s safe to take mugwort orally or to use it topically.
  • Mugwort should not be used during pregnancy because it may start menstruation and cause the uterus to contract. Little is known about whether it’s safe to use mugwort while breastfeeding.

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Automated AI algorithm uses routine imaging to predict cardiovascular risk

Credit: CC0 Public Domain

Coronary artery calcification—the buildup of calcified plaque in the walls of the heart’s arteries—is an important predictor of adverse cardiovascular events like heart attacks. Coronary calcium can be detected by computed tomography (CT) scans, but quantifying the amount of plaque requires radiological expertise, time and specialized equipment. In practice, even though chest CT scans are fairly common, calcium score CTs are not. Investigators from Brigham and Women’s Hospital’s Artificial Intelligence in Medicine (AIM) Program and the Massachusetts General Hospital’s Cardiovascular Imaging Research Center (CIRC) teamed up to develop and evaluate a deep learning system that may help change this. The system automatically measures coronary artery calcium from CT scans to help physicians and patients make more informed decisions about cardiovascular prevention. The team validated the system using data from more than 20,000 individuals with promising results. Their findings are published in Nature Communications.

“Coronary artery information could be available for almost every patient who gets a chest CT scan, but it isn’t quantified simply because it takes too much time to do this for every patient,” said corresponding author Hugo Aerts, Ph.D., director of the Artificial Intelligence in Medicine (AIM) Program at the Brigham and Harvard Medical School. “We’ve developed an algorithm that can identify high-risk individuals in an automated manner.”

Working with colleagues, lead author Roman Zeleznik, MSc, a data scientist in AIM, developed the deep learning system described in the paper to automatically and accurately predict cardiovascular events by scoring coronary calcium. While the tool is currently only for research purposes, Zeleznik and co-authors have made it open source and freely available for anyone to use.

“In theory, the deep learning system does a lot of what a human would do to quantify calcium,” said Zeleznik. “Our paper shows that it may