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NTT, Olympus Joint Demonstration Shows IOWN APN's Low-latency Capability
November 21, 2024 | JCN NewswireEstimated reading time: 1 minute
NTT Corporation and Olympus Corporation announced that, following the start of their joint experiment in March of the world’s first cloud endoscope system which processes endoscopic videos on the cloud, they jointly established a cloud endoscopy system utilizing the IOWN APN technology, and have demonstrated that the APN technology can solve network issues in realizing the cloud endoscopy system. Moving forward, the two companies will collaborate for the full-scale implementation of the cloud endoscopy system and enable performance and functional improvements of endoscopy systems through the cloud, contributing to society through improving access to advanced medical care.
Background
An endoscope is a medical device in which a flexible tube is inserted into the natural openings of the body to perform an examination and obtain tissue samples. Instances where endoscopes can be utilized are increasing year by year due to the equipment’s low level of invasiveness and high level of safety, and the technology is becoming increasingly sophisticated.
However, current endoscopes handle all its functions within the endoscope device, making performance limitations and maintainability an issue. In addition, it is expected that more cases in the future will require flexible feature improvements and updates based on new user needs, such as real-time remote diagnosis and treatment. Therefore, there is discussion on cloud computing endoscopes, in which some functions with a high processing load, such as image processing, can be done on the cloud.
To achieve image processing of endoscopes on the cloud, a network that connects the endoscope device and the cloud is vital. A lag in the network can lead to delay in the images in response to the endoscope operation, causing discomfort to the endoscopy operator. To prevent this, high-quality images captured by the endoscopy must be sent stably and with low-latency to an image processing system in the cloud, which has been a challenge.
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