Goto

Collaborating Authors

Computers Match Accuracy of Radiologists in Screening for Breast Cancer Risk

IEEE Spectrum Robotics

Women with dense breasts have a greater risk of undergoing mammogram screenings that miss signs of breast cancer. That's why 30 U.S. states legally require that women receive some notification about their breast density. A new study suggests that commercial software for automatically classifying breast density can perform on par with human radiologists: a finding that could encourage wider use of automated breast density assessments. Increased breast density represents "one of the strongest risk factors for breast cancer," because it makes it more difficult to detect the disease in its early stages, explained Karla Kerlikowske, a physician and breast cancer researcher at the University of California, San Francisco. Dense breast tissue may also carry a higher risk of developing breast cancer.


Study finds Google system could improve breast cancer detection - Reuters

#artificialintelligence

CHICAGO (Reuters) - A Google artificial intelligence system proved as good as expert radiologists at detecting which women had breast cancer based on screening mammograms and showed promise at reducing errors, researchers in the United States and Britain reported. The study, published in the journal Nature on Wednesday, is the latest to show that artificial intelligence (AI) has the potential to improve the accuracy of screening for breast cancer, which affects one in eight women globally. Radiologists miss about 20% of breast cancers in mammograms, the American Cancer Society says, and half of all women who get the screenings over a 10-year period have a false positive result. The findings of the study, developed with Alphabet Inc's (GOOGL.O) DeepMind AI unit, which merged with Google Health in September, represent a major advance in the potential for the early detection of breast cancer, Mozziyar Etemadi, one of its co-authors from Northwestern Medicine in Chicago, said. The team, which included researchers at Imperial College London and Britain's National Health Service, trained the system to identify breast cancers on tens of thousands of mammograms.


Google system could improve breast cancer detection - study

#artificialintelligence

In the United States, only one radiologist reads the results and the tests are done every one to two years. In Britain, the tests are done every three years, and each is read by two radiologists. When they disagree, a third is consulted.'SUBTLE CUES'In a separate test, the group pitted the AI system against six radiologists and found it outperformed them at accurately detecting breast cancers.Connie Lehman, chief of the breast imaging department at Harvard's Massachusetts General Hospital, said the results are in line with findings from several groups using AI to improve cancer detection in mammograms, including her own work.The notion of using computers to improve cancer diagnostics is decades old, and computer-aided detection (CAD) systems are commonplace in mammography clinics, yet CAD programs have not improved performance in clinical practice.The issue, Lehman said, is that current CAD programs were trained to identify things human radiologists can see, whereas with AI, computers learn to spot cancers based on the actual results of thousands of mammograms.This has the potential to "exceed human capacity to identify subtle cues that the human eye and brain aren't able to perceive," Lehman added.Although computers have not been "super helpful" so far, "what we've shown at least in tens of thousands of mammograms is the tool can actually make a very well-informed decision," Etemadi said.The study has some limitations. Most of the tests were done using the same type of imaging equipment, and the U.S. group contained a lot of patients with confirmed breast cancers.Crucially, the team has yet to show the tool improves patient care, said Dr Lisa Watanabe, chief medical officer of CureMetrix, whose AI mammogram program won U.S. approval last year."AI


Study finds Google system could improve breast cancer detection

The Japan Times

CHICAGO – A Google artificial intelligence system proved as good as expert radiologists at predicting which women would develop breast cancer based on screening mammograms and showed promise at reducing errors, researchers in the United States and Britain reported. The study, published in the journal Nature on Wednesday, is the latest to show that artificial intelligence (AI) has the potential to improve the accuracy of screening for breast cancer, which affects one in eight women globally. Radiologists miss about 20 percent of breast cancers in mammograms, the American Cancer Society says, and half of all women who get the screenings over a 10-year period have a false positive result. The findings of the study, developed with Alphabet's DeepMind AI unit, which merged with Google Health in September, represent a major advance in the potential for the early detection of breast cancer, said Mozziyar Etemadi, one of its co-authors from Northwestern Medicine in Chicago. The team, which included researchers at Imperial College London and Britain's National Health Service, trained the system to identify breast cancers on tens of thousands of mammograms.


Artificial intelligence versus 101 radiologists – Physics World

#artificialintelligence

A commercial artificial intelligence (AI) system matched the accuracy of over 28,000 interpretations of breast cancer screening mammograms by 101 radiologists. Although the most accurate mammographers outperformed the AI system, it achieved a higher performance than the majority of radiologists (JNCI: J. Natl. With the addition of deep-learning convolutional neural networks, new AI systems for breast cancer screening improve upon the computer-aided detection (CAD) systems that radiologists have used since the 1990s. The AI system evaluated in this study -- conducted by radiologists and medical physicists at Radboud University Medical Centre -- has a feature classifier and image analysis algorithms to detect soft-tissue lesions and calcifications, and generates a "cancer suspicion" ranking of 1 to 10. The researchers examined unrelated datasets of images from nine previous clinical studies.