Using Machine Learning To Cut Down Surgical Videos

Recording video of medical surgeries is a great way to both educate doctors in training and identify process improvements. However, surgeries can be very time consuming, and it can be a gargantuan task to sort through endless hours of video searching for relevant points where the action happens. To tackle this issue, researchers at MIT have used machine learning techniques to analyse videos of surgical procedures.

There’s some fairly serious mathematics behind this sort of videographic analysis.

The machine learning algorithm needed to be trained to identify the relevant parts of surgical videos. To do this, the laparoscopic surgeries being investigated were split up into distinct stages, each relating to a different part of the surgical process. Researchers would then watch recordings of prior surgeries and mark the start of each stage. This data was used to train the model which was then used to sift through other recordings to capture the key moments of each surgery.

The time-saving advantages of such technology could be applied to a great many fields – such an algorithm could be put to great use to sort through hours of uneventful security footage looking for anomalies, or rapidly cut together holiday footage so you only have to see the good parts. We’d love to see the researchers release footage showing the algorithm’s work – thus far, all we have to go off is the project paper.

If you’re thirsty for more machine learning knowledge, read up on the state of working with neural networks in 2017.

Lockpicking operation game

img_8708

[Moritz Waldemeyer], a favorite artist of ours, has a brand new project. He went wanting to design a 3D version of the game Operation. The piece he ended up with is called Keyhole Surgery. It’s essentially the laparoscopic version of operation. The player guides a metal key through the passages of a translucent block while attempting not to touch the walls. A counter on the side displays how many hits it has detected. The player with the smallest number wins. We love the modular potential of this project: the number of layers could be increased, the order could be changed, and more.