OPTiM®

Launch of AMIAS, an Integrated Open AI Platform
that Facilitates Medical Image Diagnosis

TOKYO, Japan (April 11, 2019) - OPTiM Corporation (TSE: 3694), a leading provider of platform solutions utilizing AI, IoT, and big data, announced the launch of AMIAS (AI Medical Image Analysis Suite), an integrated open AI platform that facilitates medical image diagnosis. AMIAS enables users to use various AI programs that OPTiM and other domestic and overseas AI program developers provide to facilitate medical image diagnosis and support other in-hospital systems such as PACSs (picture archiving and communication systems) as well as other imaging modalities (CT, MRI, etc.) in a coordinated manner. AMIAS will be on display at our booth at the International Technical Exhibition of Medical Imaging 2019 that will be held at Pacifico Yokohama from April 12 to 14, 2019.^※1

■Background

The Ministry of Health, Labour and Welfare of Japan has devised a healthcare system adaptable to changes in the environment, including a declining birth rate, an aging population, and the advancement of medical technology, and publicized it under the name Japan Vision: Health Care 2035.^※2 In light of its content, the Ministry thought it is necessary to continue to actively promote and incorporate social innovations in order to find solutions to a broad range of medical issues. This led to the Ministry to establish the “Panel for the Promotion of AI Utilization in Health Care” in January 2017 and to initiate the consideration of utilization of AI, the heart of the Fourth Industrial Revolution.^※3

In June 2017, the Panel defined six fields for the development of AI, one of which is image diagnosis support. The Panel stated, “Utilizing deep learning in image diagnosis support (performing double checking of diagnosis) will help reduce abnormalities and other risks from being overlooked during image diagnosis. In addition, deep-learning-powered medical image screening can identify images that require only brief examination, thereby reducing workloads and enabling specialists to focus on images that require closer inspection, and consequently improve the accuracy of diagnosis based on image analysis.”^※4 Thus, there are high expectations around the active utilization of AI in the medical field.

■Advantages of AMIAS in Medical Image Diagnosis

AMIAS, which is based on OPTiM Cloud IoT OS, a platform for AI and IoT, is an integrated open AI platform that facilitates medical image diagnosis. The advantages of this platform are as follows.

• Vendor-neutral device connectivity
  

  AMIAS can be connected to any manufacturers’ in-hospital system, such as PACSs (picture archiving and communication systems) as well as other imaging modalities (CT, MRI, etc.), thereby making it possible to analyze image data obtained from existing in-hospital systems using various AI programs for medical image diagnosis. In addition, the results of the analysis can be checked in AMIAS and in the backbone system of the hospital or institution.

• Wide variety of AI programs
  

  AMIAS makes it possible to use various AI programs that OPTiM and other domestic and overseas AI program developers provide to facilitate medical image diagnosis, on an open platform.

• Automatic selection of optimal AI programs
  

  AMIAS makes it possible to analyze image data obtained from an in-hospital system with optimal AI programs based on predefined conditions. (Patented: patent no. 6404529)

■Overview of AMIAS

■Flow of Medical Image Data in AMIAS

■Display of Analysis Results

■AMIAS Partners

OPTiM is seeking partnerships with AI program developers, PACS and imaging modality manufacturers, universities, and research institutions. For details, please make inquiries through the website below.

AMIAS service website:
https://www.optim.co.jp/medical/amias/

■Future Developments

OPTiM is now promoting the establishment of partnerships with AI program developers, PACS and imaging modality manufacturers, universities, and research institutions in order to expedite the development and utilization of AI programs for medical equipment. Furthermore, our aim is to support AI-powered open image diagnosis and contribute to streamlining hospital task and improve the quality of healthcare.