Olympus Corporation

Om Digital Solutions Corporation

Seiichiro Sakaguchi

Osamu Nonaka

Kazuhiko Osa

Kenji Murakami

Toshikazu Hayashi

Hisayuki Harada

Zhen Li

Hiroyuki Mino

Makoto Ishikake

Anthony R Pirozzi

Gloria Yee

Systems, devices, and methods for planning an endoscopic tissue acquisition procedure for acquiring tissue from an anatomical target are disclosed. An endoscopic system comprises a steerable elongate instrument and a processor. The steerable elongate instrument can be positioned and navigated in a patient anatomy and acquire tissue from an anatomical target via a biopsy tool associated with the steerable elongate instrument. The processor can receive an image of the anatomical target, apply the received image to a trained machine-learning (ML) model to determine a tissue acquisition plan that includes a recommended biopsy tool and operational parameters for navigating the steerable elongate instrument or maneuvering the recommended biopsy tool. The tissue acquisition plan can be presented to a user, or used to facilitate a robot-assisted tissue acquisition procedure.

AI-based endoscopic tissue acquisition planning

A learning support system includes a storage configured to store an endoscope image and an annotation image generated in a first network, a processor acquiring the annotation image from the storage, using the annotation image to perform machine learning, and generating a trained model, and a server system performing communications with the processor, and being uploaded with a trained model. The storage and the processor each serve as a node constituting the first network, which is an intra-hospital network. The server system serves as a node constituting a second network, which is an extra-hospital network.

Learning support system and learning support method

An annotation device, comprising: a display that performs sequential playback display of a plurality of images that may contain physical objects that are the subject of annotation, and a processor that acquires specific portions that have been designated within the images displayed on the display as annotation information, sets operation time or data amount for designating the specific portions, and at a point in time where designation of the specific portions has been completed for the operation time, a time based on data amount, or data amount, that have been set, requests learning to an inference engine that creates an inference model by learning, using annotation information that has been acquired up to the time of completion as training data representing a relationship between the physical object and the specific portions.

Annotation device and annotation method

Growing community of inventors

Akishima, Japan

Seiichiro Sakaguchi