Medicine has always benefited from the forefront of technology. Technology advances like computers, lasers, ultrasonic imaging, etc. have boosted medicine to extraordinary levels of achievement. Artificial Neural Networks (ANN) is currently the next promising area of interest. It is believed that neural networks will have extensive application to biomedical problems in the next few years. Already, it has been successfully applied to various areas of medicine, such as diagnostic systems, biochemical analysis, image analysis, and drug development.

A rtificial neural networks (ANNs) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems “learn” (i.e. progressively improve performance on) tasks by considering examples, generally without task-specific programming. For example, in image recognition, they might learn to identify images that contain cats by analyzing example images that have been manually labeled as “cat” or “no cat” and using the results to identify cats in other images. They do this without any a priori knowledge about cats, e.g., that they have fur, tails, whiskers and cat-like faces. Instead, they evolve their own set of relevant characteristics from the learning material that they process. Wikipedia.org

Machine Learning in Healthcare explained by A.Criminisi from Microsoft Research team. Be aware — this video is an hour length.

The problem

We at Skychain aim to create the infrastructure which will help to make the preliminary diagnostics more accurate. This kind of diagnostics is mostly tied to image analysis — pathology (imaging technique in medicine which deals with the nature of disease (structural and functional changes in tissue).

To understand how it is important here are some statistics:

Doctors can miss the first stage of lung cancer in 70% of cases when examining the x-rays of lungs. And still this is not the only case. The same problem goes for stained breast cancer slides, for example.