Hoffmann-La Roche Inc.

Genentech, Inc.

F. Hoffmann-La Roche Ag

Andreas Thalhammer

Andreas Maunz

Thomas Felix Albrecht

Yun Li

Fethallah Benmansour

Fillippo Arcadu

Benjamin Gutierrez-becker

Filippo Arcadu

Marco Prunotto

Jayashree Sahni

Yan-Ping Zhang Schaerer

Young Suk Oh

Methods and systems disclosed herein relate generally to systems and methods for predicting a future visual acuity of a subject by using machine-learning models. An image of at least part of a retina of a subject can be processed by one or more first machine-learning models to detect a set of retina-related segments. Segment-specific metrics that characterize a retina-related segment of the set of retina-related segments can be generated. The segment-specific metrics can be processed by using a second machine-learning model to generate a result corresponding to a prediction corresponding to a future visual acuity of the subject.

Machine-learning techniques for prediction of future visual acuity

The application relates to devices and methods for analysing a colonoscopy video or a portion thereof, and for assessing the severity of ulcerative colitis in a subject by analysing a colonoscopy video obtained from the subject. Analysing a colonoscopy video comprises using a first deep neural network classifier to classify image data from the subject colonoscopy video or portion thereof into at least a first severity class (more severe endoscopic lesions) and a second severity class (less severe endoscopic lesions), wherein the first deep neural network has been trained at least in part in a weakly supervised manner using training image data from a plurality of training colonoscopy videos, the training image data comprising multiple sets of consecutive frames from the plurality of training colonoscopy videos, wherein frames in a set have the same severity class label. Devices and methods for providing a tool for analysing colonoscopy videos are also described.

Automated assessment of endoscopic disease

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Basel, Switzerland

Andreas Thalhammer