Endowed Chair

Vision Informatics (TOPCON)

Development of ocular clinical data registration and collection system
  • Cloud
  • Big data construction
  • Machine learning
  • Artificial intelligence (AI)
  • Decision support system in ophthalmologic examination

Development of ophthalmologic clinical data registration and collection system, which automatically collects data in a format suitable for big data analysis

In recent years, the use of big data has become abundant in various fields, and the dramatic development of artificial intelligence (AI) has become an important topic. In the medical field, big data, AI and the combined utilization of these technologies have received a great deal of attention. It was recently reported that a special type of leukemia could be diagnosed efficiently by inputting genetic information into an AI system, which had received and processed an enormous amount of medical information within a short time.

In ophthalmologic clinical practice, enormous amounts of clinical information, image data and genetic information are accumulated every day. If we could analyze these large sets of data collectively as big data and make effective use of them, we can expect the further improvement of current medical treatment levels. However, most of these data can currently not be used for big data analysis or effectively utilized in an integrated manner.

Data suitable for big data analysis is supposed to be qualitatively homogeneous and free from fluctuations. We are currently developing an ophthalmologic clinical data registration and collection system that automatically collects data in a form suitable for big data analysis. In the future we are planning to utilize a cloud-based system, and we plan to develop it into a secure web based application. This system will make it possible to comprehensively collect medical information from different medical facilities, effectively resulting in the construction of a big data based ophthalmologic examination system.

Furthermore, by analyzing the established big data sets using machine learning such as deep learning, we will construct a decision support system for ophthalmologic examinations. This system is expected to provide an ophthalmologic diagnosis and treatment environment that will enable a more rapid and reliable diagnosis and determination of treatment policy.