Breast Imaging AI Research
Research by Gopal Vijayaraghavan, MD, MPH, Division Chief of Breast Imaging, using artificial intelligence to improve detection of breast cancer was recently featured in a publication in Nature Medicine, and an article in UMassMed News.
Excerpt from UMassMed News article, by Bryan Goodchild and Sandra Gray, UMass Chan Medical School Communications, January 11, 2021.
An artificial intelligence model for computer-aided reading of mammograms may improve the detection of breast cancer, according to a study co-authored by UMass Chan Medical School breast imaging expert Gopal Vijayaraghavan, MD, MPH, and published Jan. 11 in the journal Nature Medicine.
“Mammograms are currently the best screening tool to detect breast cancer early but reading and interpreting them is a visually challenging task, error prone for even experienced radiologists,” said Dr. Vijayaraghavan, associate professor of radiology, who co-authored the retrospective study with lead author Bill Lotter, PhD, chief technology officer and co-founder of DeepHealth. “We want to improve the health of women in Massachusetts with reliable tools that assist clinicians.”
The study compared the performance of five fellowship-trained radiologists and the deep-learning AI model developed by DeepHealth. The AI model uses a complex pattern recognition algorithm to detect and classify areas of concern.
The retrospective analysis was conducted on screening mammograms, known as index exams, which identified cancer in 131 patients. Of these patients, 120 had a prior mammogram within the past two years in which cancer was not identified, known as preindex exams. Readings of these exams were compared with reading of 154 age- and density-matched confirmed negative screenings conducted during the same period. All exams were for patients at UMass Memorial Medical Center, where Vijayaraghavan is chief of the Division of Breast Imaging.