Machine Learning Can Predict Heart Failure from a Single X-Ray

To improve detection of pulmonary edema, a typical early symptom of heart failure that can be difficult to identify on x-rays, researchers from MIT have developed a machine learning tool that can determine the severity of excess fluid in patients’ lungs, reports Health IT Analytics.

Researchers set out to develop a machine learning model that can quantify the severity of edema on a four-level scale, ranging from 0 (healthy) to 3 (very bad). The team trained the system on more than 300,000 x-ray images, as well as on the corresponding text of reports about the x-rays written by radiologists.
The results showed that the system determined the right level of excess fluid more than half the time, and correctly diagnosed level 3 cases 90 percent of the time. The team was surprised that the system was so successful using the radiologists’ reports, most of which didn’t have labels explaining the exact severity of edema.