Verily Life Sciences LLC

Lin Yang

Joëlle K Barral

Xing Jin

Martin Habbecke

Sam Kavusi

Eliezer Glik

Peter Wubbels

Gianni Campion

A system includes a display, and a database including surgical videos, images of organs in a human body obtained from a medical imaging device, and images of disease in a human body obtained from the medical imaging device. A controller including a processor is coupled to memory, the database, and the display, and the memory stores information that when executed by the processor causes the system to perform operations. For example, the processor may determine first organ information from the images of the organs, and first disease information from the images of the disease. The processor my calculate a similarity score between the first organ information and the first disease information and second disease information and second organ information indexed to the surgical videos. The processor selects one or more of the surgical videos based on the similarity score, and displays the surgical videos on the display.

Surgical video retrieval based on preoperative images

A surgical simulator for simulating a surgical scenario comprises a display system, a user interface, and a controller. The controller includes one or more processors coupled to memory that stores instructions that when executed cause the system to perform operations. The operations include generating simulated surgical videos, each representative of the surgical scenario. The operations further include associating simulated ground truth data from the simulation with the simulated surgical videos. The ground truth data corresponds to context information of at least one of a simulated surgical instrument, a simulated anatomical region, a simulated surgical task, or a simulated action. The operations further include annotating features of the simulated surgical videos based, at least in part, on the simulated ground truth data for training a machine learning model.

Surgical simulator providing labeled data

Introduced here are diagnostic platforms able to optimize computer-aided diagnostic (CADx) models by simulating the optical performance of an imaging device based on its mechanical components. For example, a diagnostic platform may acquire a source image associated with a confirmed diagnosis of a medical condition, simulate optical performance based on design data corresponding to a virtual prototype of the imaging device, generate a training image by altering the source image based on the optical performance, apply a diagnostic model to the training image, and then determine whether the performance of the diagnostic model meets a specified performance threshold. If the diagnostic model fails to meet the specified performance threshold, the diagnostic platform can automatically optimize the diagnostic model for the imaging device by altering its underlying algorithm(s).

Diagnostic model optimization for variations in mechanical components of imaging devices

A system for robot-assisted surgery includes an image sensor and a display. The system further includes a controller coupled to the image sensor and the display, where the controller includes logic that when executed by the controller causes the system to perform operations. The operations may include acquiring first images of a surgical procedure with the image sensor, and analyzing the first images with the controller to identify a surgical step in the surgical procedure. The operations may further include displaying second images on the display in response to identifying the surgical step; the second images may include at least one of a diagram of human anatomy, a preoperative image, an intraoperative image, or an annotated image of one of the first images.

Step-based system for providing surgical intraoperative cues

One example method for identifying useful segments in surgical videos includes accessing a video of a surgical procedure and user activities of a plurality of users who have watched the video of the surgical procedure. The user activities include operations performed during playback of the video. The method further includes dividing the video into multiple segments and determining a popularity score for each of the multiple segments based on the operations. Useful segments are identified from the segments based on the popularity scores. The method further includes generating metadata for the video of the surgical procedure to include an indication of the identified useful segments and associating the metadata with the video of the surgical procedure.

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Sunnyvale, CA, United States of America

Lin Yang