A research team from Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School (HMS) has developed an artificial intelligence (AI) method, aimed at training computers to interpret pathology images.
The team trained the computer to distinguish between cancerous tumor regions and normal regions based on a deep multi-layer convolutional network.
92 per cent accurate
In an objective evaluation in which researchers were given slides of lymph node cells and asked to determine whether or not they contained cancer, the team’s automated diagnostic method proved accurate approximately 92 per cent of the time.
One of the researchers, Aditya Khosla, said, “This nearly matched the success rate of a human pathologist, whose results were 96 percent accurate.”
“In our approach, we started with hundreds of training slides for which a pathologist has labeled regions of cancer and regions of normal cells,” said Dayong Wang. “We then extracted millions of these small training examples and used deep learning to build a computational model to classify them.”
The team then identified the specific training examples for which the computer is prone to making mistakes. And, worked on making the system more effective.
“There have been many reasons to think that digitizing images and using machine learning could help pathologists be faster, more accurate and make more accurate diagnoses for patients,” Andrew Beck added.
“This has been a big mission in the field of pathology for more than 30 years. But it’s been only recently that improved scanning, storage, processing and algorithms have made it possible to pursue this mission effectively. Our results in the ISBI competition show that what the computer is doing is genuinely intelligent and that the combination of human and computer interpretations will result in more precise and more clinically valuable diagnoses to guide treatment decisions.”
Breast cancer detection
In a similar breakthrough, researchers at Houston Methodist have developed an AI software that reliably interprets mammograms, assisting doctors with a quick and accurate prediction of breast cancer risk.
According to a new study published in Cancer (early online Aug. 29), the computer software intuitively translates patient charts into diagnostic information at 30 times human speed and with 99 percent accuracy.
“This software intelligently reviews millions of records in a short amount of time, enabling us to determine breast cancer risk more efficiently using a patient’s mammogram. This has the potential to decrease unnecessary biopsies,” says Stephen T. Wong, Ph.D., P.E., chair of the Department of Systems Medicine and Bioengineering at Houston Methodist Research Institute.
Manual review of 50 charts took two clinicians 50-70 hours. AI reviewed 500 charts in a few hours, saving over 500 physician hours.
The Houston Methodist team hopes this artificial intelligence software will help physicians better define the percent risk requiring a biopsy, equipping doctors with a tool to decrease unnecessary breast biopsies.