Every year, roughly one out of eight U.S. deaths is caused at least in part by heart failure. One of acute heart failure’s most common warning signs is excess fluid in the lungs, a condition known as “pulmonary edema.”
A patient’s exact level of excess fluid often dictates the doctor’s course of action, but making such determinations is difficult and requires clinicians to rely on subtle features in X-rays that sometimes lead to inconsistent diagnoses and treatment plans.
To better handle that kind of nuance, a group led by researchers at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) has developed a machine learning model that can look at an X-ray to quantify how severe the edema is, on a four-level scale ranging from 0 (healthy) to 3 (very, very bad). The system determined the right level more than half of the time, and correctly diagnosed level 3 cases 90 percent of the time.
This project is meant to augment doctors’ workflow by providing additional information that can be used to inform their diagnoses as well as enable retrospective analyses.
The team says that better edema diagnosis would help doctors manage not only acute heart issues, but other conditions like sepsis and kidney failure that are strongly associated with edema.
An important aspect of the system is that it was trained not just on more than 300,000 X-ray images, but also on the corresponding text of reports about the X-rays that were written by radiologists. The team was pleasantly surprised that their system found such success using these reports, most of which didn’t have labels explaining the exact severity level of the edema.
By learning the association between images and their corresponding reports, the method has the potential for a new way of automatic report generation from the detection of image-driven findings.
News Source: MIT
