Save patients around the world
Treatises recognized one after another
The world's first paper on artificial intelligence for gastric cancer was published in Gastric Cancer, and the paper on AI diagnosis of Helicobacter pylori was published in EBioMedicine (a sister magazine of Lancet and Cell). Furthermore, we have submitted and published many the world's first papers by the topics such as colorectal inflammation and picking up of esophageal cancer.
Multiple patent applications
Not only domestic but global patents have been applied. We pride ourselves on being a world leader in the fierce competition of medical AI development around the world.
"Accuracy" beyond average doctors
Confrontation between AI and Shogi masters is famous. Similarly when our AI also confronted more than 20 endoscopists, we have achieved the higher accuracy than the average of doctors in the judgment of Helicobacter pylori gastritis. AI has proven to be a good support partner in the clinical setting of doctors.
"Speed" applied for realtime endoscopic exams
Real-time assessments are already feasible due to improved hardware performance and the evolution of CNN (convolutional neural network) models. In other words, we believe that AI has advanced to the point where it can support realtime detection of lesions during endoscopy, and will become an indispensable tool in clinical practice around the world.
Foundation of our technology
Make full use of deep learning
CNN in the field of image recognition is being researched all over the world and is evolving day by day. In particular, the level of image recognition by deep learning has surpassed conventional machine learning and even human capabilities. We are taking the latest AI knowledge as much as possible.
The quantity and quality of data are essential
In AI development, the quality and quantity of teacher data (data to be remembered by AI) is the key. Since we are cooperating with leading hospitals nationwide and dozens of endoscopists, we are continuously collecting a huge number of high-quality images, steadily sorting them and finally built the database for AI.
Operational platform developed in-house
In order to increase the development speed, we have developed the operational platform in-house and continue to improve it so that we can work efficiently. For example, we have developed "image anonymization processing software" and "image sorting WEB system" to maximize the work efficiency of cooperating doctors handling of medical data. We will continue to make speedy adjustments and improvements by taking advantage of our in-house development.