  Robotic surgery has been proven to offer clear advantages during surgical
procedures, however, one of the major limitations is obtaining haptic feedback.
Since it is often challenging to devise a hardware solution with accurate force
feedback, we propose the use of "visual cues" to infer forces from tissue
deformation. Endoscopic video is a passive sensor that is freely available, in
the sense that any minimally-invasive procedure already utilizes it. To this
end, we employ deep learning to infer forces from video as an attractive
low-cost and accurate alternative to typically complex and expensive hardware
solutions. First, we demonstrate our approach in a phantom setting using the da
Vinci Surgical System affixed with an OptoForce sensor. Second, we then
validate our method on an ex vivo liver organ. Our method results in a mean
absolute error of 0.814 N in the ex vivo study, suggesting that it may be a
promising alternative to hardware based surgical force feedback in endoscopic
procedures.
