Machine learning rivals human skills in cancer detection
April 22, 2016
Two announcements yesterday (April 21) suggest that deep learning algorithms rival human skills in detecting cancer from ultrasound images and in identifying cancer in pathology reports.
Samsung Medison, a global medical equipment company and an affiliate of Samsung Electronics, has just updated its RS80A ultrasound imaging system with a deep learning algorithm for breast-lesion analysis.
The “S-Detect for Breast” feature uses big data collected from breast-exam cases and recommends whether the selected lesion is benign or malignant. It’s used in in lesion segmentation, characteristic analysis, and assessment processes, providing “more accurate results.”
“We saw a high level of conformity from analyzing and detecting lesion in various cases by using the S-Detect,” said professor Han Boo Kyung, a radiologist at Samsung Medical Center.
“Users can reduce taking unnecessary biopsies and doctors-in-training will likely have more reliable support in accurately detecting malignant and suspicious lesions.”
Deep learning is better than humans in extracting meaning from cancer pathology reports
Meanwhile, researchers from the Regenstrief Institute and Indiana University School of Informatics and Computing at Indiana University-Purdue University Indianapolis say they’ve found that open-source machine learning tools are as good as — or better than — humans in extracting crucial meaning from free-text (unstructured) pathology reports and detecting cancer cases. The computer tools are also faster and less resource-intensive.
(U.S. states require cancer cases to be reported to statewide cancer registries for disease tracking, identification of at-risk populations, and recognition of unusual trends or clusters. This free-text information can be difficult for health officials to interpret, which can further delay health department action, when action is needed.)
“We think that its no longer necessary for humans to spend time reviewing text reports to determine if cancer is present or not,” said study senior author Shaun Grannis*, M.D., M.S., interim director of the Regenstrief Center of Biomedical Informatics.
Awash in oceans of data
“We have come to the point in time that technology can handle this. A human’s time is better spent helping other humans by providing them with better clinical care. Everything — physician practices, health care systems, health information exchanges, insurers, as well as public health departments — are awash in oceans of data. How can we hope to make sense of this deluge of data? Humans can’t do it — but computers can.”
This is especially relevant for underserved nations, where a majority of clinical data is collected in the form of unstructured free text, he said.
The researchers sampled 7,000 free-text pathology reports from over 30 hospitals that participate in the Indiana Health Information Exchange and used open source tools, classification algorithms, and varying feature selection approaches to predict if a report was positive or negative for cancer. The results indicated that a fully automated review yielded results similar or better than those of trained human reviewers, saving both time and money.
Major infrastructure advance
“We found that artificial intelligence was as least as accurate as humans in identifying cancer cases from free-text clinical data. For example the computer ‘learned’ that the word ‘sheet’ or ‘sheets’ signified cancer as ‘sheet’ or ‘sheets of cells’ are used in pathology reports to indicate malignancy.
“This is not an advance in ideas; it’s a major infrastructure advance — we have the technology, we have the data, we have the software from which we saw accurate, rapid review of vast amounts of data without human oversight or supervision.”
The study was published in the April 2016 issue of the Journal of Biomedical Informatics. It was conducted with support from the Centers for Disease Control and Prevention.
Co-authors of the study include researchers at the IU Fairbanks School of Public Health, the IU School of Medicine and the School of Science at IUPUI.
* Grannis, a Regenstrief Institute investigator and an associate professor of family medicine at the IU School of Medicine, is the architect of the Regenstrief syndromic surveillance detector for communicable diseases and led the technical implementation of Indiana’s Public Health Emergency Surveillance System — one of the nation’s largest. Studies over the past decade have shown that this system detects outbreaks of communicable diseases seven to nine days earlier and finds four times as many cases as human reporting while providing more complete data.
Yann Lecun is Director of AI Research, Facebook and a noted deep-learning expert.