Abstract: Methods, apparatuses, and systems for edge computing for robotic telesurgery using artificial intelligence are disclosed. A robotic surgical system includes surgery equipment and can communicate via a cloud network. The system can include operation room (OR) equipment and a surgical computer. The surgical computer transfers data between a remote surgeon, the OR equipment, and the surgery equipment. The surgical computer receives, using the OR equipment and the surgery equipment, data related to a surgical procedure. The data is related to the surgical procedure and is computed using artificial intelligence (AI). The surgical computer determines a risk assessment based on data related to the surgery equipment. The surgical computer sends an indication of the determined risk assessment to the remote surgeon.

Abstract: Methods, apparatuses, and systems performing robotic arthroscopic surgery for extensor retinaculum are disclosed. The disclosed surgical robot receives user inputs, workflow objects, and data files containing surgical actions for robotic movements from a surgery network. The surgical tools required for performing the robotic arthroscopic surgery are displayed on a user interface for the surgical tools to be enabled or disabled. The robotic arthroscopic surgical steps are displayed on the user interface in a sequence to enable execution of the data files containing the robotic movements. The robotic movements are used to perform surgical steps or assist a surgeon in performing surgical steps.

Abstract: Methods, apparatuses, and systems for transcribing and performing analysis on patient data are disclosed. Data is collected from one or more medical professionals as well as sensors and imaging devices positioned on or oriented towards a patient. An analysis is performed on the patient data and the data is presented to a medical professional via a verbal interface in a conversational manner, allowing the medical professional to provide additional data such as observations or instructions which may be used for further analysis or to perform actions related to the patient's care.

Abstract: Methods, apparatuses, and systems for performing machine learning (ML) for robotic arthroscopic surgery are disclosed. The disclosed systems use ML to provide recommendations and methods for automated robotic ankle arthroscopic surgery. Historical patient data is filtered to match particular parameters of a patient. The parameters are correlated to the patient. A robotic surgical system or a surgeon reviews the historical patient data to select or adjust the historical patient data to generate a surgical workflow for a surgical robot to perform the robotic arthroscopic surgery.

Abstract: Disclosed are systems and methods that provide a framework for performing advanced image stabilization. The disclosed framework can operate to capture imagery used and/or relied upon for the preoperative (pre-op), operative and/or postoperative (post-op) stages of a medical procedure. The framework can function by collecting and monitoring sensor data related to a device of a user (e.g., a patient). The sensor data can be analyzed, whereby the framework can determine the most opportune time to capture an image(s)—for example, a computed tomography (CT) scan or magnetic resonance imaging (MRI) scan, and the like. Accordingly, the framework can leverage the determined capture opportunity to perform the image capture and storage of the image data and metadata, as well as perform a confidence score computation that enables validation or verification of the image's authenticity and quality.

Abstract: Haptic feedback from a robotic surgical tool can be adjusted based on intra-operative assessment of the accuracy of a pre-operative surgical navigational plans. Navigational reference points are identified in at least one pre-operative image. At least one haptic response is identified for interactions between at least one robotic surgical tool and at least one navigational reference point. At least one intra-operative image is compared to the pre-operative image to determine the relative position of at least two corresponding navigational reference points in the images. The reference points' relative position determines a confidence level in the accuracy of the pre-operative navigational reference point. The haptic response is adjusted in timing, location, type, or amplitude based upon the confidence level. Tolerances and surgical navigation plan may also be updated and altered based on the confidence level.

Abstract: A system for using robotic telesurgery to implant a smart implant is disclosed. The system comprises a remote doctor module communicatively coupled with an operating room module over a cloud network. The operating room module comprises a robotic arm, a processor, and communication interface which communicates with the smart implant during the surgical procedure. The Operating Room Module testing the smart implant prior to implantation surgery; correlating data collected during the implantation surgery against past data from previous surgeries; determining whether the plurality of sensors is working properly; and transferring communication with the smart implant to a user device receive and monitor data from the plurality of sensors integrated into the smart implant to determine the smart implant is operating according as expected and to monitor the patient's condition.

Abstract: Methods, apparatuses, and systems for edge computing in surgical robotics is provided. The system comprises a surgical robot communicatively coupled to a 3rd party operating room equipment system over a cloud network. The surgical robot includes an operating room hardware configured to perform a surgical procedure on a patient. A memory is communicatively coupled to operating room hardware and user interface, and the memory comprises an equipment database and a threshold database to store parameters related to the operating room hardware. A processor, coupled to the operating room hardware via the network interface, is configured to establish a connection between the surgical robot and the 3rd party operating room equipment system, and monitor events being performed by the operating room and then store the monitored data within the threshold database. The threshold database stores trigger values for each piece of equipment for taking actions, based on data collected by sensors.

Abstract: Methods, apparatuses, and systems for automated disease detection using multiple-wavelength imaging are disclosed. The disclosed system uses multiple imaging modalities for assessing a medical condition. Data collected from multiple cameras and imaging modalities is processed to identify common structures. The common structures are used to scale and align images, which are analyzed to detect one or more medical conditions. Each acquired image is assessed, and the resulting probabilities are consolidated. The images can be assessed together by using artificial intelligence and machine learning.

Abstract: Computer-implemented digital image analysis methods, apparatuses, and systems for robotic installation of surgical implants are disclosed. A disclosed apparatus plans a route within an anatomy of a patient from an incision site to a surgical implant site for robotic installation of a surgical implant. The apparatus uses digital imaging data to identify less-invasive installation paths and determine the dimensions of the surgical implant components being used. The apparatus segments the surgical implant into surgical implant subcomponents and modifies the surgical implant subcomponents, such that they can be inserted using the identified less-invasive installation paths.

Abstract: A system for using robotic telesurgery to implant a smart implant is disclosed. The system comprises a remote doctor module communicatively coupled with an operating room module over a cloud network. The operating room module comprises a robotic arm, a processor, and communication interface which communicates with the smart implant during the surgical procedure. The Operating Room Module testing the smart implant prior to implantation surgery; correlating data collected during the implantation surgery against past data from previous surgeries; determining whether the plurality of sensors is working properly; and transferring communication with the smart implant to a user device receive and monitor data from the plurality of sensors integrated into the smart implant to determine the smart implant is operating according as expected and to monitor the patient's condition.

Abstract: Apparatuses, systems, and methods for performing real-time analysis of bone tissue during a surgical procedure are disclosed herein. In some embodiments, the method includes receiving at least one measurement of at least one tissue sample from a hybrid multi-wavelength photoacoustic measurements (MWPM) component. The method can also include identifying one or more reference cases, from a plurality of reference cases, based on correlations between the at least one measurement and previous measurements in each of the plurality of reference cases. Once the reference cases are identified, the method can include determining at least one bone condition of the patient and sending the at least one determined bone condition to a computing device accessible by a surgeon. In some embodiments, the method also includes creating a three-dimensional (3D) map the tissue sample using the at least one measurement and sending the 3D map to the computing device.

Abstract: Systems, methods, and techniques for customizing connectors for connecting standardized implant components together or to a part of a patient. The connectors are customized based on medical imagery of the patient and/or other patient data and may use additive or subtractive manufacturing techniques. The connectors can be manufactured at the point of use and used with standardized components.

Abstract: Methods, apparatuses, and systems for edge computing for robotic telesurgery using artificial intelligence are disclosed. A robotic surgical system includes surgery equipment and can communicate via a cloud network. The system can include operation room (OR) equipment and a surgical computer. The surgical computer transfers data between a remote surgeon, the OR equipment, and the surgery equipment. The surgical computer receives, using the OR equipment and the surgery equipment, data related to a surgical procedure. The data is related to the surgical procedure and is computed using artificial intelligence (AI). The surgical computer determines a risk assessment based on data related to the surgery equipment. The surgical computer sends an indication of the determined risk assessment to the remote surgeon.

Abstract: Monitoring devices monitor physiological parameters of a patient undergoing surgery. The physiological parameters describe a physiological condition of the patient. A processor matches the physiological parameters to stored surgical data associated with adverse surgical events associated with surgical procedures matching the surgery. The processor determines a predicted time at which the physiological condition of the patient will meet a threshold physiological condition associated with the adverse surgical event based on a rate of change of the physiological parameters. Responsive to determining the predicted time, the processor transmits a first alert to robotic surgical controls to adjust the surgery prior to the predicted time. The processor determines that the physiological condition of the patient has met the threshold physiological condition. The processor transmits a second alert to the robotic surgical controls to terminate the surgery.

Abstract: Methods, apparatuses, and systems for providing real-time surgical assistance to a surgical robot using an imaging module, a context computer module, and a quantum analysis module are disclosed. The imaging module receives one or more data related to a patient from an intra-operative database and performs a first step analysis based on the received data. The context computer module determines a type of resource required for analysis based on data received from a historical database and the intra-operative database. The quantum analysis module receives data from the imaging module and the context computer module. The quantum analysis module receives real-time data from operation room (OR) equipment and performs quantum analysis on the received data and real-time data. The quantum analysis module facilitates real-time surgical assistance and recommendations to the surgical robot.

Abstract: Robotic system and method are described for performing latency managed telesurgery. The system comprises drone(s) to create a wireless network in one or more geographic areas between a surgical site and a remote surgeon. The system further comprises a computer that is configured to: receive a request that indicates that a telesurgery is to be performed between the first location and the second location at a scheduled time; determine that one or more equipment is available for use at the surgical site for the telesurgery at the scheduled time; send a first instruction that triggers measurement of a latency of the wireless network; determine, during the telesurgery, whether the latency of the wireless network is acceptable; and send a first message that indicates that the latency is not acceptable, where an operation of the drone(s) is adjusted or an additional drone is deployed in the one or more geographic areas.

Abstract: Methods, apparatuses, and systems for edge computing in surgical robotics is provided. The system comprises a surgical robot communicatively coupled to a 3rd party operating room equipment system over a cloud network. The surgical robot includes an operating room hardware configured to perform a surgical procedure on a patient. A memory is communicatively coupled to operating room hardware and user interface, and the memory comprises an equipment database and a threshold database to store parameters related to the operating room hardware. A processor, coupled to the operating room hardware via the network interface, is configured to establish a connection between the surgical robot and the 3rd party operating room equipment system, and monitor events being performed by the operating room and then store the monitored data within the threshold database. The threshold database stores trigger values for each piece of equipment for taking actions, based on data collected by sensors.

Abstract: A system for implanting surgical material in a patient’s body is disclosed. The system comprises a two-dimensional (2D) laser cutter network comprising a 2D laser cutter and a processor. The processor is configured to perform the steps of capturing an image of an open area of a patient’s body and processing a drug implant substrate to create a customized drug implant substrate. The customized drug implant may be created based on the captured image to correspond with the open area of the patient’s body. The drug implant substrate is shaped using the 2D laser cutter. The processor is further configured to perform the steps of placing the customized drug implant substrate in the open area of the patient’s body and sealing the open area after placing the customized drug implant substrate in the open area.

Abstract: A system for implanting surgical material in a patient’s body is disclosed. The system comprises a two-dimensional (2D) laser cutter network comprising a 2D laser cutter and a processor. The processor is configured to perform the steps of capturing an image of an open area of a patient’s body and processing a drug implant substrate to create a customized drug implant substrate. The customized drug implant may be created based on the captured image to correspond with the open area of the patient’s body. The drug implant substrate is shaped using the 2D laser cutter. The processor is further configured to perform the steps of placing the customized drug implant substrate in the open area of the patient’s body and sealing the open area after placing the customized drug implant substrate in the open area.