25% of cancers in the gullet are missed using standard endoscopy
Medical experts would like to improve early cancer detection in the oesophagus (gullet). The cancer lesions are found using endoscopy pushed down the gullet, but these lesions are very hard to spot and can be life threatening if overlooked. Cancer Research UK gave funding to a young team to fulfill this request using machine learning.
They started to train an algorithm from video footage with lots of cancer/non cancer examples.
By using machine learning to identify the cancers in real time by highlighting the cancer lesion when the clinician takes a video down the gullet, early screening can be improved
Doctors/Medical Experts can use the solution in conjunction with Amethyst annotation tool to rapidly train AI algorithms to detect cancerous lesions and non cancerous lesions in the gullet. It will allow to detect lesions early using endoscope with inbuild algorithm in it, detecting lesions when they can hardly be seen by a human eye. Historically 25% of squamous cancer in oesophagus is missed, and the new solution will allow to identify patients with cancerous lesions faster, with more accuracy, and less discomfort for the patients.
Making sure, data is anonymized to avoid privacy issues as well as getting enough examples.
Just looking for lesions by eye.
That the algorithm can be trained quickly and accurately to ensure building the best model.
It is hoped the tool can quickly mark up the images for training data much faster than traditional methods.
Doctors can carry on using endoscope as usual, which will show up/highlight lesions on the screen. No specific training will be required, as the algorithms will be built into the endoscope. Provided the doctor has domain knowledge of what a cancerous lesion looks like.
Still under testing and development. Technology is React (Jacascript) and Python.
There is a client called Amethyst that does the making up of the cancer lesions.
Where is the data stored?
Locally and in the cloud. All data is anonymized and put in secure cloud environment.