'Unsupervised' AI may allow for more accurate cancer recurrence predictions

A team of researchers from the RIKEN Center for Advanced Intelligence Project (Saitama, Japan) has developed a machine-learning platform capable of identifying features associated with prostate cancer recurrence in pathology images that were previously unknown to clinicians. In combination with pathologist-developed criteria, the technology may allow for more accurate cancer recurrence predictions. Conventionally, when clinicians and/or researchers train artificial intelligence (AI) systems, the technology is only able to learn and make predictions based on the information that has been inputted – there is no scope for the system to learn outside of what is currently known. In this study, no medical knowledge was inputted into the platform, rather, investigators employed'unsupervised' deep neural networks, called autoencoders, and utilized a subset of 13,188 non-annotated, whole-mount, diagnostic prostate pathology slide images from the Nippon Medical School Hospital (Tokyo, Japan), allowing the AI system to learn and make predictions independently. The team developed a method for translating the features identified by the machine-learning platform into high-resolution images that could be understood by clinicians.