A new study provides key insights in what causes Alzheimer's: a breakdown in the brain’s system for clearing waste.
A new study provides key insights into what’s causing the disease. The research, published in Nature Communications, points to a breakdown over time in the brain’s system for clearing waste, an issue that seems to happen in some people as they get older.
Michael Glickman, a biologist at Technion – Israel Institute of Technology, helped lead this research. I asked him to tell me about his approach to studying how this breakdown occurs in the brain, and how he tested a treatment that has potential to fix the problem at its earliest stages.
Dr. Michael Glickman is internationally renowned for his research on the ubiquitin-proteasome system (UPS), the brain's system for clearing the waste that is involved in diseases such as Huntington's, Alzheimer's, and Parkinson's. He is the head of the Lab for Protein Characterization in the Faculty of Biology at the Technion – Israel Institute of Technology. In the lab, Michael and his team focus on protein recycling and the ubiquitin-proteasome system, which protects against serious diseases like Alzheimer’s, Parkinson’s, cystic fibrosis, and diabetes. After earning his PhD at the University of California at Berkeley in 1994, Michael joined the Technion as a Senior Lecturer in 1998 and has served as a full professor since 2009.
Dr. Michael Glickman
A recent study in The Lancet Oncology showed that AI found 20 percent more cancers on mammogram screens than radiologists alone.
Published in The Lancet Oncology, the study analyzed the scans of 80,000 Swedish women with a moderate hereditary risk of breast cancer who had undergone a mammogram between April 2021 and July 2022. Half of these scans were read by AI and then a radiologist to double-check the findings. The second group of scans was read by two researchers without the help of AI. (Currently, the standard of care across Europe is to have two radiologists analyze a scan before diagnosing a patient with breast cancer.)
The study showed that the AI group detected cancer in 6 out of every 1,000 scans, while the radiologists detected cancer in 5 per 1,000 scans. In other words, AI found 20 percent more cancers than the highly-trained radiologists.
Scientists have been using MRI images (like the ones pictured here) to train artificial intelligence to detect cancers earlier and with more accuracy. Here, MIT's AI system, MIRAI, looks for patterns in a patient's mammograms to detect breast cancer earlier than ever before. news.mit.edu
But even though the AI was better able to pinpoint cancer on an image, it doesn’t mean radiologists will soon be out of a job. Dr. Laura Heacock, a breast radiologist at NYU, said in an interview with CNN that radiologists do much more than simply screening mammograms, and that even well-trained technology can make errors. “These tools work best when paired with highly-trained radiologists who make the final call on your mammogram. Think of it as a tool like a stethoscope for a cardiologist.”
AI is still an emerging technology, but more and more doctors are using them to detect different cancers. For example, researchers at MIT have developed a program called MIRAI, which looks at patterns in patient mammograms across a series of scans and uses an algorithm to model a patient's risk of developing breast cancer over time. The program was "trained" with more than 200,000 breast imaging scans from Massachusetts General Hospital and has been tested on over 100,000 women in different hospitals across the world. According to MIT, MIRAI "has been shown to be more accurate in predicting the risk for developing breast cancer in the short term (over a 3-year period) compared to traditional tools." It has also been able to detect breast cancer up to five years before a patient receives a diagnosis.
The challenges for cancer-detecting AI tools now is not just accuracy. AI tools are also being challenged to perform consistently well across different ages, races, and breast density profiles, particularly given the increased risks that different women face. For example, Black women are 42 percent more likely than white women to die from breast cancer, despite having nearly the same rates of breast cancer as white women. Recently, an FDA-approved AI device for screening breast cancer has come under fire for wrongly detecting cancer in Black patients significantly more often than white patients.
As AI technology improves, radiologists will be able to accurately scan a more diverse set of patients at a larger volume than ever before, potentially saving more lives than ever.