Abstract: A surgical computer-implement surgical system may include a surgical computing system (e.g., a surgical hub), one or more surgical data sources in communication with the surgical computing system, a surgical device in communication with the surgical computing system, and a processor. Data generated by the one or more surgical data sources may be received by the processor. Such data may be used, by the processor, to train a machine learning (ML) model (e.g., a neural network). The ML model may be deployed to affect an operation of the surgical device. For example, the ML model may be deployed to the surgical hub to affect an operation of the surgical device.

Abstract: A surgical computer-implement surgical system may include a surgical computing system (e.g., a surgical hub), one or more surgical data sources in communication with the surgical computing system, a surgical device in communication with the surgical computing system, and a processor. Data generated by the one or more surgical data sources may be received by the processor. Such data may be used, by the processor, to train a machine learning (ML) model (e.g., a neural network). ML model may be deployed to affect an operation of the surgical device. For example, the ML model may be deployed to the surgical hub to affect an operation of the surgical device.

Abstract: A surgical computer-implement surgical system may include a surgical computing system (e.g., a surgical hub), one or more surgical data sources in communication with the surgical computing system, a surgical device in communication with the surgical computing system, and a processor. Data generated by the one or more surgical data sources may be received by the processor. Such data may be used, by the processor, to train a machine learning (ML) model (e.g., a neural network). ML model may be deployed to affect an operation of the surgical device. For example, the ML model may be deployed to the surgical hub to affect an operation of the surgical device.

Abstract: A device, such as a computing device or a surgical device, may receive surgical operation data associated with a surgical operation. Based on the surgical operation data, a device may identify a surgical device to be used for a surgical operation and/or one or more surgical steps associated with a surgical operation. Based on the identified surgical device, the one or more surgical steps, and/or the surgical operation data, a device may determine an allowable operation range controlling a surgical device for a surgical procedure. A device may receive an adjustment input configuration to control a surgical device for a surgical step. A device may determine whether the adjustment input configuration is outside of the determined allowable operation range. Based on a determination that the adjustment input configuration is outside of the determined allowable operation range, a device may block the adjustment input configuration to control a surgical device.

Abstract: Systems, methods, and instrumentalities may be described herein associated with data processing associated with multiple system hierarchical levels and compartmentalizing a machine learning algorithm into a plurality of parts. A surgical hub/edge device may obtain surgical data associated with a surgical task and determine sets of parameters associated with various surgical data subblocks. The surgical hub/edge device may determine processing levels to be used for processing each of the surgical data subblocks based on a capability associated with a processing device that is located in a computational hierarchy of the healthcare provider's network. The surgical hub/edge device may send the surgical data subblocks to the respective processing devices based on the parameters associated with the surgical data subblocks and the processing levels. Scaling of a surgical data attribute to be analyzed by a machine learning algorithm may be adjusted based on a resource-time relationship of a surgical hub/edge device.

Abstract: A system for designing three-dimensional (3-D) structures that includes a central artificial intelligence (AI) system with a computer processor, a communications module, and memory for storing a central training database and AI models. Also included is a software application run on a computing device, the app including a training database and AI models. The central AI system receives structural data from a fabricating device or a testing apparatus, populates a training database using the structural data, and trains the AI models using the training database. A computer-implemented method for designing 3-D structures that includes receiving an external geometry and a set of design parameters for a structure, selecting a shape for a volumetric unit, creating a render of the structure, solving for the performance of the render, determining if the render meets the design parameters, determining if the render is optimized, and generating a solution for the structure.

Abstract: A firearm suppressor having a core, a first end, a second end, a hollow bore extending along a longitudinal axis from the first to the second end, and an energy management structure. The first end of the suppressor includes a set of threads for connecting the suppressor to a firearm, and the bore includes a plurality of openings into the energy manager. The energy manager includes a plurality of chambers extending from the bore. A method of designing a firearm suppressor including setting a pressure range to be contained by the suppressor, establishing certain manufacturing parameters, choosing a structure for the core, modeling performance values for the core, selecting a geometry of the structure, and selecting a sleeve for the core.

Abstract: A computing device, such as a surgical hub, may obtain performance data associated with a device, such as a surgical device in an operation room. Based on the performance data, a computing device may identify a performance signature associated with the surgical device. A computing device may determine whether the surgical device is an original equipment manufacturer (OEM) device or a counterfeit device. A computing device may compare the operation data and/or the performance signature to data (e.g., data from a ML trained model) associated with OEM devices. Based on the comparison, a computing device may determine whether the operation data associated with the surgical device is within a normal operation parameter. If a computing device determines that the operation data outside of the normal operation parameter, the computing device may send a message to a health care professional.

Abstract: A computing device, such as a surgical hub, may obtain performance data associated with a device, such as a surgical device in an operation room. Based on the performance data, a computing device may identify a performance signature associated with the surgical device. A computing device may determine whether the surgical device is an original equipment manufacturer (OEM) device or a counterfeit device. A computing device may compare the operation data and/or the performance signature to data (e.g., data from a ML trained model) associated with OEM devices. Based on the comparison, a computing device may determine whether the operation data associated with the surgical device is within a normal operation parameter. If a computing device determines that the operation data outside of the normal operation parameter, the computing device may send a message to a health care professional.

Abstract: Systems, methods, and instrumentalities are disclosed for aggregating and/or apportioning available surgical data into a more usable dataset for machine learning (ML) model (e.g., algorithm) interaction. A ML model may be more accurate and/or reliable if using complete and/or regular data. Aggregating and/or apportioning available surgical data may enable a more complete and/or regular dataset for ML model analysis.

Abstract: Systems, methods, and instrumentalities may be described herein associated with data processing associated with multiple system hierarchical levels and compartmentalizing a machine learning algorithm into a plurality of parts. A surgical hub/edge device may obtain surgical data associated with a surgical task and determine sets of parameters associated with various surgical data subblocks. The surgical hub/edge device may determine processing levels to be used for processing each of the surgical data subblocks based on a capability associated with a processing device that is located in a computational hierarchy of the healthcare provider's network. The surgical hub/edge device may send the surgical data subblocks to the respective processing devices based on the parameters associated with the surgical data subblocks and the processing levels. Scaling of a surgical data attribute to be analyzed by a machine learning algorithm may be adjusted based on a resource-time relationship of a surgical hub/edge device.

Abstract: Systems, methods, and instrumentalities are provided where surgical data allometry is associated with a hierarchical level where the surgical data may be processed. A device may receive a plurality of surgical data parameters of a first surgical data individuality level and a first surgical data magnitude. The plurality of the surgical data parameters may be transformed based on the first surgical data individuality level and the first surgical data magnitude, the characteristics of the processing server where the plurality of surgical data parameters are sent for processing, and/or a rule set. The transformed plurality of surgical data parameters may be of a second surgical data individuality level and a second surgical data magnitude. The first surgical data individuality level may be different (e.g., higher) than the second surgical data individuality level. The transformed plurality of surgical data parameters may be sent for processing to a processing device.

Abstract: A device, such as a computing device or a surgical device, may receive surgical operation data associated with a surgical operation. Based on the surgical operation data, a device may identify a surgical device to be used for a surgical operation and/or one or more surgical steps associated with a surgical operation. Based on the identified surgical device, the one or more surgical steps, and/or the surgical operation data, a device may determine an allowable operation range controlling a surgical device for a surgical procedure. A device may receive an adjustment input configuration to control a surgical device for a surgical step. A device may determine whether the adjustment input configuration is outside of the determined allowable operation range. Based on a determination that the adjustment input configuration is outside of the determined allowable operation range, a device may block the adjustment input configuration to control a surgical device.

Abstract: A surgical computer-implement surgical system may include a surgical computing system (e.g., a surgical hub), one or more surgical data sources in communication with the surgical computing system, a surgical device in communication with the surgical computing system, and a processor. Data generated by the one or more surgical data sources may be received by the processor. Such data may be used, by the processor, to train a machine learning (ML) model (e.g., a neural network). ML model may be deployed to affect an operation of the surgical device. For example, the ML model may be deployed to the surgical hub to affect an operation of the surgical device.

Abstract: A surgical computer-implement surgical system may include a surgical computing system (e.g., a surgical hub), one or more surgical data sources in communication with the surgical computing system, a surgical device in communication with the surgical computing system, and a processor. Data generated by the one or more surgical data sources may be received by the processor. Such data may be used, by the processor, to train a machine learning (ML) model (e.g., a neural network). The ML model may be deployed to affect an operation of the surgical device. For example, the ML model may be deployed to the surgical hub to affect an operation of the surgical device.

Abstract: Systems, methods, and instrumentalities are disclosed for using interrelated machine learning (ML) models (e.g., algorithms). The interrelated ML models may act collectively to perform complimentary portions of a surgical analysis. The ML models may be used at various locations. For example, ML models may be implemented in a facility network, a cloud network, an edge network, and/or the like. The location of the ML models may influence the type of data the ML models process. For example, ML models used outside a HIPAA boundary (e.g., cloud network) may process non-private and/or non-confidential information. The ML models may be used to feed their respective results into other ML models to provide a more complete result.

Abstract: Systems, methods, and instrumentalities are described herein for autonomous operation of a surgical device within a predefined boundary. A discrete signal associated with clamping control (e.g., closure of a clamping jaw) may be received by the surgical device. The discrete signal may be triggered by a healthcare professional or autonomously activated. The surgical device, in response to the discrete signal and based on an algorithm, may generate a continuous signal to cause a continuous application of force or deployment of an operation. The surgical device, based at least on a measurement associated with one of tissue, inrush current, or the distance between the smart energy device and the smart grasper may determine a safety adjustment associated with the operation of the surgical device.

Abstract: Examples described herein may include a surgical computing system that determines an autonomous operation parameter and generates a control signal for an autonomous operation based on the autonomous operation parameter. The surgical computing system may obtain surgical data and determine the autonomous operation parameter based on the surgical data. The surgical computing system may obtain surgical data and determine the autonomous operation parameter based on the surgical data. The surgical computing system may send the control signal for the autonomous operation, for example, to one or more smart surgical devices.