公开(公告)号：US20230316756A1

公开(公告)日：2023-10-05

申请号：US18035078

申请日：2021-11-18

Applicant: Intuitive Surgical Operations, Inc.

Inventor： Ziheng Wang ,  Kiran Bhattacharyya ,  Samuel Bretz ,  Anthony Jarc ,  Xi Liu ,  Andrea Villa ,  Aneeq Zia

IPC: G06V20/50 ,  G06V20/40 ,  G06V10/82 ,  G06V10/80 ,  G06V10/77

CPC classification number: G06V20/50 ,  G06V20/41 ,  G06V20/46 ,  G06V10/82 ,  G06V10/809 ,  G06V10/7715 ,  G06V2201/034 ,  G16H30/40

Abstract: Various of the disclosed embodiments relate to systems and methods for processing surgical data to facilitate further downstream operations. For example, some embodiments may include machine learning systems trained to recognize whether video from surgical visualization tools, such as endoscopes, depicts a field of view inside or outside the patient body. The system may excise or whiteout frames of video appearing outside the patient so as to remove potentially compromising personal information, such as the identities of members of the surgical team, the patients identity, configurations of the surgical theater, etc. Appropriate removal of such non-surgical data may facilitate downstream processing, e.g., by complying with regulatory requirements as well as by removing extraneous data potentially inimical to further downstream processing, such as training a downstream classifier.

公开(公告)号：US20250006372A1

公开(公告)日：2025-01-02

申请号：US18756906

申请日：2024-06-27

Applicant: Intuitive Surgical Operations, Inc.

Inventor： Conor Perreault ,  Aneeq Zia

IPC: G16H50/20 ,  G06V10/70 ,  G06V20/40

Abstract: The solution for an ML-based medical procedure analysis with interpretable model confidence rankings is disclosed. The solution can include a system having one or more processors, coupled with memory. The system can receive a plurality of input features associated with a prediction for a video stream that captures a procedure performed with a robotic medical system. The prediction can be made via a first model trained with machine learning. The system can determine, via a second model trained with machine learning, a level of confidence in the prediction made via the first model. The system can attribute the level of confidence among at least two input features of the plurality of input features. The system can provide, for display via a display device, an indication overlaid on the video stream of the attribution of the level of confidence among the at least two input features.

公开(公告)号：US20230326193A1

公开(公告)日：2023-10-12

申请号：US18035095

申请日：2021-11-18

Applicant: Intuitive Surgical Operations, Inc.

Inventor： Aneeq Zia ,  Kiran Bhattacharyya ,  Anthony Jarc

IPC: G06V10/80 ,  G06V10/764 ,  G06V10/82 ,  G16H40/63 ,  A61B34/37

CPC classification number: G06V10/811 ,  G06V10/764 ,  G06V10/82 ,  G16H40/63 ,  A61B34/37 ,  G06V2201/03

Abstract: Various of the disclosed embodiments are directed to computer-implemented systems and methods for recognizing surgical tasks from surgical data, In some embodiments an ensemble model configured to receive video data, kinematics data, and system event data from the surgical theater may be implemented. The ensemble model may implement modular streams for processing the data, facilitating predictions even when less than all the data types are available. In some embodiments, smoothing operations may help facilitate more accurate prediction results, Various of the embodiments may be employed in real-time during surgery, providing predictions at per-second intervals.

公开(公告)号：US20230317258A1

公开(公告)日：2023-10-05

申请号：US18037976

申请日：2021-11-26

Applicant: Intuitive Surgical Operations, Inc.

Inventor： Kristen Brown ,  Kiran Bhattacharyya ,  Anthony Michael Jarc ,  Sue Kulason ,  Linlin Zhou ,  Aneeq Zia

IPC: G16H40/20

CPC classification number: G16H40/20

Abstract: Various of the disclosed embodiments relate to computer systems and computer-implemented methods for measuring and monitoring surgical performance, For example, the system may receive raw data acquired from the surgical theater, generate and select features from the data amenable to analysis, and then train a machine learning classifier using the selected features to facilitate subsequent assessment of other surgeons' performances. Generation and selection of the features may itself involve application of a machine learning classifier in some embodiments. While some embodiments contemplate raw data acquired from surgical robotic systems, some embodiments facilitate assessments upon data acquired from non-robotic surgical theaters.