Patents by Inventor Imanol Luengo Muntion
Imanol Luengo Muntion has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20240156547Abstract: A surgical action to be performed during a surgical procedure is predicted using machine learning based on images and surgical instrumentation data. An image/video capture device such as an endoscope, a wearable camera, a stationary camera, etc., can be used to capture the image(s). A surgeon can be provided an augmented visualization of the surgical procedure by displaying one or more graphical overlays based on the findings of the machine learning to enhance the surgeon's information.Type: ApplicationFiled: March 18, 2022Publication date: May 16, 2024Inventors: Imanol Luengo Muntion, Petros Giataganas, Danail V. Stoyanov
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Publication number: 20240161497Abstract: An aspect includes a computer-implemented method that accesses input data including spatial data and/or sensor data temporally associated with a video stream of a surgical procedure. One or more machine-learning models predict a state of the surgical procedure based on the input data. The one or more machine-learning models detect one or more surgical instruments at least partially depicted in the video stream based on the input data. A state indicator and one or more surgical instrument indicators temporally correlated with the video stream are output.Type: ApplicationFiled: March 18, 2022Publication date: May 16, 2024Inventors: Imanol Luengo Muntion, Danail V. Stoyanov, Andre Chow, Petros Giataganas, David P. Owen, Maria Grammatikopoulou, Ricardo Sanchez-Matilla, Maria Ruxandra Robu
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Publication number: 20240122734Abstract: The present disclosure relates to systems and methods that use computer-vision processing systems to improve patient safety during surgical procedures. Computer-vision processing systems may train machine-learning models using machine-learning techniques. The machine-learning techniques can be executed to train the machine-learning models to recognize, classify, and interpret objects within a live video feed. Certain embodiments of the present disclosure can control (or facilitate control of) surgical tools during surgical procedures using the trained machine-learning models.Type: ApplicationFiled: December 22, 2023Publication date: April 18, 2024Inventors: Andre Chow, Danail V. Stoyanov, Imanol Luengo Muntion, Petros Giataganas, Jean Nehme
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Patent number: 11883312Abstract: The present disclosure relates to systems and methods that use computer-vision processing systems to improve patient safety during surgical procedures. Computer-vision processing systems may train machine-learning models using machine-learning techniques. The machine-learning techniques can be executed to train the machine-learning models to recognize, classify, and interpret objects within a live video feed. Certain embodiments of the present disclosure can control (or facilitate control of) surgical tools during surgical procedures using the trained machine-learning models.Type: GrantFiled: August 31, 2022Date of Patent: January 30, 2024Assignee: DIGITAL SURGERY LIMITEDInventors: Andre Chow, Danail Stoyanov, Imanol Luengo Muntion, Petros Giataganas, Jean Nehme
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Publication number: 20230326207Abstract: Techniques are described for improving computer-assisted surgical (CAS) systems, particularly, to recognize surgical phases in a video of a surgical procedure. A CAS system includes cameras that provide video stream of a surgical procedure. According to one or more aspects the surgical phases are automatically detected in the video stream using a machine learning model. Particularly, the machine learning model includes a boundary aware cascade stage network to perform surgical phase recognition.Type: ApplicationFiled: April 4, 2023Publication date: October 12, 2023Inventors: Jinglu Zhang, Abdolrahim Kadkhodamohammadi, Imanol Luengo Muntion, Danail V. Stoyanov, Santiago Barbarisi
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Patent number: 11615884Abstract: A computer implemented method is provided for a virtual training system. A virtual surgical simulation associated with a type of surgical procedure is accessed. Image data associated with a controller and a workspace is received. Controller data corresponding to a controller interaction is received. A first interaction of the controller within the workspace based on at least one of the image data and the controller data is determined. Using the set of one or more transformation rules, the first interaction of the controller is transformed to a manipulation of a virtualized surgical tool in the virtual surgical simulation. A representation is output of the manipulation of the virtualized surgical tool.Type: GrantFiled: March 6, 2019Date of Patent: March 28, 2023Assignee: DIGITAL SURGERY LIMITEDInventors: Danail Stoyanov, Petros Giataganas, Piyamate Wisanuvej, Paul Riordan, Imanol Luengo Muntion, Jean Nehme
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Publication number: 20220409285Abstract: The present disclosure relates to systems and methods that use computer-vision processing systems to improve patient safety during surgical procedures. Computer-vision processing systems may train machine-learning models using machine-learning techniques. The machine-learning techniques can be executed to train the machine-learning models to recognize, classify, and interpret objects within a live video feed. Certain embodiments of the present disclosure can control (or facilitate control of) surgical tools during surgical procedures using the trained machine-learning models.Type: ApplicationFiled: August 31, 2022Publication date: December 29, 2022Inventors: Andre Chow, Danail Stoyanov, Imanol Luengo Muntion, Petros Giataganas, Jean Nehme
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Patent number: 11446092Abstract: The present disclosure relates to systems and methods that use computer-vision processing systems to improve patient safety during surgical procedures. Computer-vision processing systems may train machine-learning models using machine-learning techniques. The machine-learning techniques can be executed to train the machine-learning models to recognize, classify, and interpret objects within a live video feed. Certain embodiments of the present disclosure can control (or facilitate control of) surgical tools during surgical procedures using the trained machine-learning models.Type: GrantFiled: July 20, 2020Date of Patent: September 20, 2022Assignee: DIGITAL SURGERY LIMITEDInventors: Andre Chow, Danail Stoyanov, Imanol Luengo Muntion, Petros Giataganas, Jean Nehme
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Publication number: 20220020486Abstract: The present disclosure relates to processing data streams from a surgical procedure using multiple interconnected data structures to generate and/or continuously update an electronic output. Each surgical data structure is used to determine a current node associated with a characteristic of a surgical procedure and present relevant metadata associated with the surgical procedure. Each surgical data structure includes at least one node interconnected to one or more nodes of another data structure. The interconnected nodes between one or more data structures includes relational metadata associated with the surgical procedure.Type: ApplicationFiled: October 1, 2021Publication date: January 20, 2022Inventors: Petros Giataganas, Imanol Luengo Muntion, Andre Chow, Jean Nehme, Danail Stoyanov
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Patent number: 11189379Abstract: The present disclosure relates to processing data streams from a surgical procedure using multiple interconnected data structures to generate and/or continuously update an electronic output. Each surgical data structure is used to determine a current node associated with a characteristic of a surgical procedure and present relevant metadata associated with the surgical procedure. Each surgical data structure includes at least one node interconnected to one or more nodes of another data structure. The interconnected nodes between one or more data structures includes relational metadata associated with the surgical procedure.Type: GrantFiled: March 4, 2019Date of Patent: November 30, 2021Assignee: DIGITAL SURGERY LIMITEDInventors: Petros Giataganas, Imanol Luengo Muntion, Andre Chow, Jean Nehme, Danail Stoyanov
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Publication number: 20210015554Abstract: The present disclosure relates to systems and methods that use computer-vision processing systems to improve patient safety during surgical procedures. Computer-vision processing systems may train machine-learning models using machine-learning techniques. The machine-learning techniques can be executed to train the machine-learning models to recognize, classify, and interpret objects within a live video feed. Certain embodiments of the present disclosure can control (or facilitate control of) surgical tools during surgical procedures using the trained machine-learning models.Type: ApplicationFiled: July 20, 2020Publication date: January 21, 2021Inventors: Andre Chow, Danail Stoyanov, Imanol Luengo Muntion, Petros Giataganas, Jean Nehme
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Patent number: 10758309Abstract: The present disclosure relates to systems and methods that use computer-vision processing systems to improve patient safety during surgical procedures. Computer-vision processing systems may train machine-learning models using machine-learning techniques. The machine-learning techniques can be executed to train the machine-learning models to recognize, classify, and interpret objects within a live video feed. Certain embodiments of the present disclosure can control (or facilitate control of) surgical tools during surgical procedures using the trained machine-learning models.Type: GrantFiled: July 15, 2019Date of Patent: September 1, 2020Assignee: DIGITAL SURGERY LIMITEDInventors: Andre Chow, Danail Stoyanov, Imanol Luengo Muntion, Petros Giataganas, Jean Nehme
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Publication number: 20200118677Abstract: The present disclosure relates to processing data streams from a surgical procedure using multiple interconnected data structures to generate and/or continuously update an electronic output. Each surgical data structure is used to determine a current node associated with a characteristic of a surgical procedure and present relevant metadata associated with the surgical procedure. Each surgical data structure includes at least one node interconnected to one or more nodes of another data structure. The interconnected nodes between one or more data structures includes relational metadata associated with the surgical procedure.Type: ApplicationFiled: December 13, 2019Publication date: April 16, 2020Applicant: Digital Surgery LimitedInventors: Petros Giataganas, Imanol Luengo Muntion, Andre Chow, Jean Nehme, Danail Stoyanov
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Patent number: 10496898Abstract: A set of virtual images can be generated based on one or more real images and target rendering specifications, such that the set of virtual images correspond to (for example) different rendering specifications (or combinations thereof) than do the real images. An image style can be transferred to the at least some of the virtual images of the set of virtual images to generate a stylized virtual image. A machine-learning model can be trained using a plurality of stylized virtual images. Another real image can then be processed using the trained machine-learning model. The processing can include segmenting the other real image to detect whether and/or which objects are represented (and/or a state of the object). The object data can then be used to identify (for example) a state of a procedure.Type: GrantFiled: February 1, 2019Date of Patent: December 3, 2019Assignee: Digital Surgery LimitedInventors: Odysseas Zisimopoulos, Evangello Flouty, Imanol Luengo Muntion, Mark Stacey, Sam Muscroft, Petros Giataganas, Andre Chow, Jean Nehme, Danail Stoyanov
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Publication number: 20190279524Abstract: A computer implemented method is provided for a virtual training system. A virtual surgical simulation associated with a type of surgical procedure is accessed. Image data associated with a controller and a workspace is received. Controller data corresponding to a controller interaction is received. A first interaction of the controller within the workspace based on at least one of the image data and the controller data is determined. Using the set of one or more transformation rules, the first interaction of the controller is transformed to a manipulation of the virtualized surgical tool in the virtual surgical simulation. A representation is output of the manipulation of the virtualized surgical tool.Type: ApplicationFiled: March 6, 2019Publication date: September 12, 2019Applicant: Digital Surgery LimitedInventors: Danail Stoyanov, Petros Giataganas, Piyamate Wisanuvej, Paul Riordan, Imanol Luengo Muntion, Jean Nehme
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Publication number: 20190279765Abstract: The present disclosure relates to processing data streams from a surgical procedure using multiple interconnected data structures to generate and/or continuously update an electronic output. Each surgical data structure is used to determine a current node associated with a characteristic of a surgical procedure and present relevant metadata associated with the surgical procedure. Each surgical data structure includes at least one node interconnected to one or more nodes of another data structure. The interconnected nodes between one or more data structures includes relational metadata associated with the surgical procedure.Type: ApplicationFiled: March 4, 2019Publication date: September 12, 2019Applicant: Digital Surgery LimitedInventors: Petros Giataganas, Imanol Luengo Muntion, Andre Chow, Jean Nehme, Danail Stoyanov
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Publication number: 20190164012Abstract: A set of virtual images can be generated based on one or more real images and target rendering specifications, such that the set of virtual images correspond to (for example) different rendering specifications (or combinations thereof) than do the real images. An image style can be transferred to the at least some of the virtual images of the set of virtual images to generate a stylized virtual image. A machine-learning model can be trained using a plurality of stylized virtual images. Another real image can then be processed using the trained machine-learning model. The processing can include segmenting the other real image to detect whether and/or which objects are represented (and/or a state of the object). The object data can then be used to identify (for example) a state of a procedure.Type: ApplicationFiled: February 1, 2019Publication date: May 30, 2019Applicant: Digital Surgery LimitedInventors: Odysseas Zisimopoulos, Evangello Flouty, Imanol Luengo Muntion, Mark Stacey, Sam Muscroft, Petros Giataganas, Andre Chow, Jean Nehme, Danail Stoyanov
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Patent number: 10242292Abstract: A set of virtual images can be generated based on one or more real images and target rendering specifications, such that the set of virtual images correspond to (for example) different rendering specifications (or combinations thereof) than do the real images. A machine-learning model can be trained using the set of virtual images. Another real image can then be processed using the trained machine-learning model. The processing can include segmenting the other real image to detect whether and/or which objects are represented (and/or a state of the object). The object data can then be used to identify (for example) a state of a procedure.Type: GrantFiled: June 4, 2018Date of Patent: March 26, 2019Assignee: Digital Surgery LimitedInventors: Odysseas Zisimopoulos, Evangello Flouty, Imanol Luengo Muntion, Mark Stacey, Sam Muscroft, Petros Giataganas, Andre Chow, Jean Nehme, Danail Stoyanov
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Publication number: 20180357514Abstract: A set of virtual images can be generated based on one or more real images and target rendering specifications, such that the set of virtual images correspond to (for example) different rendering specifications (or combinations thereof) than do the real images. A machine-learning model can be trained using the set of virtual images. Another real image can then be processed using the trained machine-learning model. The processing can include segmenting the other real image to detect whether and/or which objects are represented (and/or a state of the object). The object data can then be used to identify (for example) a state of a procedure.Type: ApplicationFiled: June 4, 2018Publication date: December 13, 2018Applicant: Digital Surgery LimitedInventors: Odysseas Zisimopoulos, Evangello Flouty, Imanol Luengo Muntion, Mark Stacey, Sam Muscroft, Petros Giataganas, Andre Chow, Jean Nehme, Danail Stoyanov