Pan and co-investigators reached their conclusions by using 48 submissions in the 2017 bone-age competition. In it, RSNA shared more than 12,600 pediatric hand x-rays, with bone ages determined by a radiologist, challenging teams to create their own prediction models. Researchers for this recent study evaluated numerous possible model combinations—from two, up to 10 of the 48 submissions—using the mean absolute deviation.
A key takeaway from the study is the need for practitioners who are incorporating AI algorithms into their own workflows to seek out predictions from other similar models, the authors noted. They compared this practice to a radiologist seeking out a second opinion in the reading room.
The authors also believe that such AI competitions are fertile ground for further development ensemble prediction methods.
“Machine learning competitions within radiology should be encouraged to spur development of heterogeneous models whose predictions can be combined to achieve optimal performance,” Pan added.
Eliot Siegel, MD, a radiologist and professor at the University of Maryland, further explored the ensemble model and Pan’s research in a corresponding commentary published Nov. 20.
Credit: Google News