Robot-assisted drug delivery

Abstract

A system includes a processor and a memory having instructions stored thereon. The instructions, when executed by the processor, cause the processor to: infuse a substance into a target object via an infusion device according to a first flow rate; setting a second flow rate associated with infusion of the substance into the target object based on an extraction rate of the infusion device from the target object; and infusing the substance into the target object based on the second flow rate.

Description

Technical Field

[0001] The present disclosure relates generally to drug delivery, and more particularly, to robotic-assisted drug delivery. Background Art

[0002] The robot may assist a surgeon or other medical provider in performing a medical procedure (eg, surgical procedure, treatment, etc.), or may autonomously complete one or more medical procedures. Imaging may be used by a medical provider for diagnostic and / or therapeutic purposes. Summary of the invention

[0003] Example aspects of the present disclosure include:

[0004] A system includes: a processor; and a memory having instructions stored thereon, which, when executed by the processor, cause the processor to: infuse a substance into a target object via an infusion device according to a first flow rate; set a second flow rate associated with infusing the substance into the target object based on a withdrawal rate of the infusion device from the target object; and infuse the substance into the target object based on the second flow rate.

[0005] A system comprising: an infusion device; a processor; and a memory having data stored thereon, the data causing the processor, when processed by the processor, to: infuse a substance into a target object via the infusion device according to a first flow rate; set a second flow rate associated with infusing the substance into the target object based on a withdrawal rate of the infusion device from the target object; and infuse the substance into the target object based on the second flow rate.

[0006] A method comprising: infusing a substance into a target object via an infusion set according to a first flow rate; setting a second flow rate associated with infusing the substance into the target object in response to determining a withdrawal rate associated with withdrawing the infusion set from the target object; and infusing the substance into the target object based on the second flow rate.

[0007] A system comprising: an infusion device; a robotic device; and an infusion management circuit system, the infusion management circuit system controlling the infusion of a substance by: infusing the substance into a target object via the robotic device and the infusion device according to a first flow rate; setting a second flow rate associated with infusing the substance into the target object based on a withdrawal rate of the infusion device from the target object; and infusing the substance into the target object based on the second flow rate.

[0008] A non-transitory computer-readable medium comprising instructions which, when executed by a processor, infuse a substance into a target object via an infusion device according to a first flow rate; set a second flow rate associated with infusing the substance into the target object based on a withdrawal rate of the infusion device from the target object; and infuse the substance into the target object based on the second flow rate.

[0009] Any aspect may be combined with any one or more other aspects.

[0010] Any one or more of the features disclosed herein.

[0011] Any one or more of the features is generally disclosed herein.

[0012] Any one or more of the features generally disclosed herein may be combined with any one or more of the other features generally disclosed herein.

[0013] Any aspect / feature / implementation may be combined with any one or more other aspects / features / implementations.

[0014] Use any one or more of the aspects or features disclosed herein.

[0015] It should be understood that any feature described herein may be claimed in combination with any other feature described herein, regardless of whether the features are from the same described implementation.

[0016] The details of one or more aspects of the present disclosure are set forth in the following drawings and description. Other features, objectives, and advantages of the techniques described in the present disclosure will be apparent from the description and drawings, and from the claims.

[0017] The foregoing is a simplified overview of the present disclosure to provide an understanding of some aspects of the present disclosure. The present disclosure is neither a broad overview nor an exhaustive overview of the present disclosure and its various aspects, specific implementations, and configurations. It is neither intended to determine the key or important elements of the present disclosure nor to delimit the scope of the present disclosure, but rather to present the selected concepts of the present disclosure in a simplified form as an introduction to the more detailed description presented below. As should be understood, other aspects, specific implementations, and configurations of the present disclosure may utilize one or more of the features set forth above or described in detail below, either individually or in combination.

[0018] Numerous additional features and advantages of the present disclosure will become apparent to those skilled in the art after considering the detailed description of implementations provided below. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings are incorporated into and form a part of this specification to illustrate several examples of the present disclosure. These drawings, together with the description, explain the principles of the present disclosure. The accompanying drawings illustrate only preferred and alternative examples of how to implement and use the present disclosure, and these examples should not be interpreted as limiting the present disclosure to only the illustrated and described examples. Additional features and advantages will become apparent from the following more detailed description of various aspects, specific implementations and configurations of the present disclosure, as illustrated by the accompanying drawings referenced below.

[0020] Figure 1A and Figure 1B Examples of systems supporting aspects of the present disclosure are illustrated.

[0021] Figure 2 An example of a process flow supporting various aspects of the present disclosure is illustrated.

[0022] Figure 3 An example of a process flow supporting various aspects of the present disclosure is illustrated. DETAILED DESCRIPTION

[0023] It should be understood that the various aspects disclosed herein may be combined in combinations different from those specifically presented in the specification and drawings. It should also be understood that, depending on the example or specific implementation, certain actions or events of any process or method described herein may be performed in a different order, and / or may be added, combined, or omitted entirely (e.g., not all described actions or events may be required to practice the disclosed technology, depending on different specific implementations of the present disclosure). In addition, although for clarity, certain aspects of the present disclosure are described as being performed by a single module or unit, it should be understood that the technology of the present disclosure may be performed by a combination of units or modules associated with, for example, a computing device and / or a medical device.

[0024] In one or more examples, the described methods, processes, and techniques may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored as one or more instructions or codes on a computer-readable medium and executed by a hardware-based processing unit. Alternatively or additionally, the functions may be implemented using a machine learning model, a neural network, an artificial neural network, or a combination thereof (alone or in combination with instructions). A computer-readable medium may include a non-transitory computer-readable medium corresponding to a tangible medium, such as a data storage medium (e.g., a RAM, a ROM, an EEPROM, a flash memory, or any other medium that may be used to store desired program code in the form of instructions or data structures and that may be accessed by a computer).

[0025] Instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), general-purpose microprocessors (e.g., Intel Core i3, i5, i7, or i9 processors; Intel Celeron processors; Intel Xeon processors; Intel Pentium processors; AMD Ryzen processors; AMD Athlon processors; AMD Phenom processors; Apple A10 or 10X Fusion processors; Apple A11, A12, A12X, A12Z, or A13 Bionic processors; or any other general-purpose microprocessor), graphics processing units (e.g., Nvidia GeForce RTX 2000 series processors, Nvidia GeForce RTX 3000 series processors, AMD Radeon RX 5000 series processors, AMD Radeon RX 6000 series processors, or any other graphics processing unit), application-specific integrated circuits (ASICs), field-programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuit systems. Thus, the term "processor" as used herein may refer to any of the foregoing structures or any other physical structure suitable for implementing the described techniques. Additionally, these techniques may be fully implemented in one or more circuits or logic elements.

[0026] Before explaining any specific implementation of the present disclosure in detail, it should be understood that the present disclosure is not limited to the construction details and component arrangements set forth in the following description or illustrated in the accompanying drawings in terms of its application. The present disclosure can have other specific implementations and can be practiced or implemented in various ways. In addition, it should be understood that the wording and terminology used herein are for the purpose of description and should not be considered as restrictive. The use of "comprises", "including" or "having" and its variations herein is intended to cover the items listed thereafter and their equivalents, as well as additional items. In addition, the present disclosure can use examples to illustrate one or more aspects thereof. Unless otherwise expressly stated, the use or listing of one or more examples (which can be indicated by "for example", "by way of example", "such as" or similar language) is not intended to and does not limit the scope of the present disclosure.

[0027] The terms proximal and distal are used in this disclosure in their conventional medical sense, with proximal being closer to an operator or user of the system and farther away from an area of ​​surgical interest in or on the patient's body, and distal being closer to the area of ​​surgical interest in or on the patient's body and farther away from an operator or user of the system.

[0028] In targeted drug delivery of therapeutic agents, the location of drug delivery and coverage of the target object is critical. For example, in targeted drug delivery of small molecules, proteins, gene therapy, therapeutic antibodies, nucleic acid therapeutics, and cell-based therapies in the brain, and for tumors such as glioblastoma, location and drug delivery and coverage are critical. Some technologies utilize magnetic resonance imaging (MRI) and manual guidance of therapy deployment by specialized surgeons using MRI contrast agents (e.g., gadolinium-based contrast agents) in combination with therapeutic agents.

[0029] Aspects of the present disclosure include support for increasing the robot-assisted deployment of the opportunity for patients to obtain targeted drug delivery outside highly specialized academic institutions. In some specific implementations, the patient is imaged with MRI-computed tomography (CT) before surgery to visualize the region of interest (ROI). Using visualization, the robot system can identify the navigation coordinates of the target structure or mass and its size and shape. The robot system can provide the patient with an automated robot deployment (e.g., calculating and implementing the best infusion rate, cannula movement and placement) of infusion rate, cannula movement and placement to give maximum structural coverage. The robot system can support robot-assisted drug delivery, and in some examples, support for implementing robot-assisted brain drug delivery algorithms for robot-assisted drug delivery. In some embodiments, the various aspects of robot-assisted drug delivery described herein support drug delivery associated with any combination of therapeutic approaches (e.g., drug delivery of small molecules and proteins, therapeutic antibodies, etc.).

[0030] When targeting multiple structures with two or more cannulas, aspects of the robotic deployment described herein can provide increased benefits. For example, for effective coverage, drug delivery using cannulas can involve different placement and infusion rates, respectively, which can be very challenging in the case of manual administration. In an example, according to some other techniques, a physician can initiate delivery of a drug (e.g., via a cannula) and attempt to manually withdraw the cannula while the drug is being dispensed. However, this manual approach relies on the physician's respective experience and ability. Aspects of the systems described herein can provide calculated infusion rates that support increased coverage associated with drug delivery.

[0031] According to example aspects of the present disclosure, a robotic device can provide improved accuracy in controlling infusion rates and extracting an infusion device. In some aspects, the robotic device can obtain coordinate information associated with guiding and extracting an infusion device from a neuronavigation system. For example, using the coordinate information, the robotic device can deliver a medical substance to a target object (e.g., a target tissue) with improved accuracy, thereby reducing the potential penetration of the medical substance into other tissues. Thus, for example, the robotic device described herein may be able to provide effective drug delivery customized for a patient, reducing reliance on medical experts. The terms "robotic device" and "robot" may be used interchangeably herein.

[0032] Figure 1A An example of a system 100 that supports aspects of the present disclosure is illustrated.

[0033] The system 100 includes a computing device 102, one or more imaging devices 112, a robot 114, a navigation system 118, an infusion device 126, a database 130, and / or a cloud network 134 (or other network). Systems according to other implementations of the present disclosure may include more or fewer components than the system 100. For example, the system 100 may omit and / or include additional instances of one or more components of the computing device 102, the imaging device 112, the robot 114, the navigation system 118, the infusion device 126, the database 130, and / or the cloud network 134. In an example, the system 100 may omit any instance of the computing device 102, the imaging device 112, the robot 114, the navigation system 118, the database 130, and / or the cloud network 134. The system 100 may support implementations of one or more other aspects of one or more of the methods disclosed herein.

[0034] The computing device 102 includes a processor 104, a memory 106, a communication interface 108, and a user interface 110. Computing devices according to other implementations of the present disclosure may include more or fewer components than the computing device 102. The computing device 102 may be, for example, a control device that includes electronic circuitry associated with controlling the robot 114, the robotic arm 116, and / or the infusion device 126.

[0035] The processor 104 of the computing device 102 may be any processor described herein or any similar processor. The processor 104 may be configured to execute instructions stored in the memory 106, which may cause the processor 104 to perform one or more computing operations using or based on data received from the imaging device 112, the robot 114, the navigation system 118, the infusion device 126, the database 130, and / or the cloud network 134.

[0036] The memory 106 may be or include RAM, DRAM, SDRAM, other solid-state memory, any memory described herein, or any other tangible, non-transitory memory for storing computer-readable data and / or instructions. The memory 106 may store information or data associated with completing any operation of, for example, the methods described herein or any other method. The memory 106 may store, for example, instructions and / or machine learning models that support one or more functions of the computing device 102, the imaging device 112, the robot 114, the navigation system 118, or the infusion device 126. For example, the memory 106 may store content (e.g., instructions and / or machine learning models) that, when executed by the processor 104, enable image processing 120, segmentation 122, transformation 124, registration 128, infusion engine 138, machine learning model 138, and / or processing algorithm 142. In some implementations, if provided as instructions, such content may be organized into one or more applications, modules, packages, layers, or engines. Alternatively or additionally, the memory 106 may store other types of content or data (e.g., machine learning models, artificial neural networks, deep neural networks, etc.) that may be processed by the processor 104 to perform the various methods and features described herein. Thus, while the various contents of the memory 106 may be described as instructions, it should be understood that the functionality described herein may be implemented using instructions, algorithms, and / or machine learning models. The data, algorithms, and / or instructions may cause the processor 104 to manipulate data stored in the memory 106 and / or received from or via the imaging device 112, the robot 114, the navigation system 118, the infusion device 126, the database 130, and / or the cloud network 134.

[0037] The computing device 102 may also include a communication interface 108. The communication interface 108 may be used to receive data or other information from an external source (e.g., an imaging device 112, a robot 114, a navigation system 118, a database 130, a cloud network 134, and / or any other system or component separate from the system 100) and / or to transmit instructions, data (e.g., image data, measurements, insertion depth of the infusion set 126, a target flow rate associated with the infusion set 126, a target withdrawal rate associated with the infusion set 126, etc.), or other information to an external system or device (e.g., another computing device 102, an imaging device 112, a robot 114, a navigation system 118, a database 130, a cloud network 134, and / or any other system or component that is not part of the system 100). The communication interface 108 may include one or more wired interfaces (e.g., USB ports, Ethernet ports, FireWire ports) and / or one or more wireless transceivers or interfaces (configured to transmit and / or receive information via one or more wireless communication protocols such as 802.11a / b / g / n, Bluetooth, NFC, ZigBee, etc.) In some implementations, the communication interface 108 may support communication between the device 102 and one or more other processors 104 or computing devices 102, whether to reduce the time required to complete computationally intensive tasks or for any other reason.

[0038] The computing device 102 may also include one or more user interfaces 110. The user interface 110 may be or include a keyboard, a mouse, a trackball, a monitor, a television, a screen, a touch screen and / or any other device for receiving information from a user and / or for providing information to a user. The user interface 110 may be used, for example, to receive a user selection or other user input for any operation of any method described herein. Nevertheless, any required input for any operation of any method described herein may be automatically generated by the system 100 (e.g., by another component of the processor 104 or the system 100) or received by the system 100 from a source external to the system 100. In some specific implementations, the user interface 110 may support user modifications (e.g., by surgeons, medical staff, patients, etc.) of instructions executed by the processor 104 according to one or more specific implementations of the present disclosure, and / or support user modifications or adjustments to the settings of other information displayed on or corresponding to the user interface 110.

[0039] In some implementations, the computing device 102 can utilize a user interface 110 that is housed separately from one or more remaining components of the computing device 102. In some implementations, the user interface 110 can be located near one or more other components of the computing device 102, while in other implementations, the user interface 110 can be located remote from one or more other components of the computing device 102.

[0040] The imaging device 112 can be used to image anatomical features (e.g., bones, veins, tissues, etc.) and / or other aspects of the patient's anatomy to generate image data (e.g., image data depicting or corresponding to bones, veins, tissues, etc.). As used herein, "image data" refers to data generated or captured by the imaging device 112, including data in machine-readable form, graphical / visual form, and in any other form. In various examples, the image data may include data corresponding to an anatomical feature of the patient or a portion thereof. The image data may be or include preoperative images, intraoperative images, postoperative images, or images taken independently of any surgical procedure. In some specific implementations, the first imaging device 112 can be used to obtain first image data (e.g., a first image) at a first time, and the second imaging device 112 can be used to obtain second image data (e.g., a second image) at a second time after the first time. The imaging device 112 may be capable of taking 2D images or 3D images to generate image data. The imaging device 112 may be or include, for example, an ultrasound scanner (which may include, for example, physically separate transducers and receivers, or a single ultrasound transceiver), an O-arm, C-arm, G-arm, or any other device that utilizes X-ray based imaging (e.g., a fluoroscope, a CT scanner or other X-ray machine), a magnetic resonance imaging (MRI) scanner, an optical coherence tomography (OCT) scanner, an endoscope, a microscope, an optical camera, a thermal imaging camera (e.g., an infrared camera), a radar system (which may include, for example, a transmitter, a receiver, a processor, and one or more antennas), or any other imaging device 112 suitable for obtaining images of anatomical features of a patient. The imaging device 112 may be entirely contained within a single housing, or may include a transmitter / transmitter and a receiver / detector that are in separate housings or otherwise physically separated.

[0041] In some implementations, the imaging device 112 may include more than one imaging device 112. For example, a first imaging device may provide first image data and / or a first image, and a second imaging device may provide second image data and / or a second image. In yet other implementations, the same imaging device may be used to provide both the first image data and the second image data and / or any other image data described herein. The imaging device 112 may be used to generate an image data stream. For example, the imaging device 112 may be configured to operate with an open shutter, or with a shutter that continuously alternates between open and closed, in order to capture continuous images. For purposes of this disclosure, unless otherwise specified, if the image data represents two or more frames per second, the image data may be considered continuous and / or provided as an image data stream.

[0042] Robot 114 may be any surgical robot or surgical robotic system. Robot 114 may be or include, for example, a Mazor XTM Stealth Edition robotic guidance system. The robot 114 may be configured to position the imaging device 112 at one or more precise positions and orientations, and / or return the imaging device 112 to the same position and orientation at a later point in time. The robot 114 may be additionally or alternatively configured to manipulate surgical tools (whether or not based on guidance from the navigation system 118) to complete or assist in surgical tasks. In some implementations, the robot 114 may be configured to hold and / or manipulate anatomical elements during or in conjunction with a surgical procedure. The robot 114 may include one or more robotic arms 116. In some implementations, the robotic arm 116 may include a first robotic arm and a second robotic arm, but the robot 114 may include more than two robotic arms. In some implementations, one or more of the robotic arms 116 may be used to hold and / or manipulate the imaging device 112. In an implementation where the imaging device 112 includes two or more physically separate components (e.g., a transmitter and a receiver), one robotic arm 116 may hold one such component, and another robotic arm 116 may hold another such component. Each robotic arm 116 can be positioned independently of the other robotic arms.The robotic arms 116 can be controlled in a single shared coordinate space or in separate coordinate spaces.

[0043] The robot 114, together with the robotic arm 116, may have, for example, one, two, three, four, five, six, seven or more degrees of freedom. Furthermore, the robotic arm 116 may be positioned or positionable in any posture, plane and / or focus. The posture includes a position and an orientation. Thus, an imaging device 112, a surgical tool or other object held by the robot 114 (or more specifically, by the robotic arm 116) may be able to be precisely positioned in one or more desired and specific positions and orientations.

[0044] The robotic arm 116 may include one or more sensors that enable the processor 104 (or a processor of the robot 114) to determine the precise pose of the robotic arm (and any object or element held by or secured to the robotic arm) in space.

[0045] In some implementations, reference markers (ie, navigational markers) may be placed on the robot 114 (including, for example, on the robot arm 116), the imaging device 112, the infusion device 126, and the like for later reference. Figure 1BThe reference markers may be tracked by the navigation system 118, and the results of the tracking may be used by the robot 114 and / or by an operator of the system 100 or any of its components. In some implementations, the navigation system 118 may be used to track other components of the system (e.g., the imaging device 112), and the system may be operated without the use of the robot 114 (e.g., the surgeon and / or medical technician manually manipulates the imaging device 112, the infusion device 126, and / or one or more surgical tools, for example based on information and / or instructions generated by the navigation system 118).

[0046] During the operation, the navigation system 118 can provide navigation for the surgeon and / or the surgical robot. The navigation system 118 can be any navigation system now known or developed in the future, including, for example, the Medtronic StealthStation TM S8 surgical navigation system or any successor thereof. The navigation system 118 may include one or more cameras or other sensors for tracking one or more reference markers, navigation trackers, or other objects within the operating room or other room where part or all of the system 100 is located. The one or more cameras may be optical cameras, infrared cameras, or other cameras. In some embodiments, the navigation system 118 may include one or more electromagnetic sensors. In various embodiments, the navigation system 118 may be used to track the imaging device 112, the robot 114, the robotic arm 116, the target object (e.g., referenced later) Figure 1B The navigation system 118 may include a display for displaying one or more images from an external source (e.g., the computing device 102, the imaging device 112, or other source) or for displaying images and / or video streams from one or more cameras or other sensors of the navigation system 118. In some implementations, the system 100 may operate without the use of the navigation system 118. The navigation system 118 may be configured to provide guidance to a surgeon or other user of the system 100 or its components, to the robot 114, or to any other element of the system 100 regarding, for example, the pose of one or more anatomical elements (e.g., tissue), whether a tool (e.g., the infusion device 126) is in an appropriate trajectory, and / or how to move the tool into an appropriate trajectory to perform a surgical task according to a preoperative or other surgical plan.

[0047] The infusion device 126 may be a device that supports the infusion of a substance (e.g., a medical substance, a drug, etc.). In some aspects, the infusion device 126 may be a device that is capable of delivering or directing a fluid (e.g., a liquid, a gas) to a later referenced Figure 1A The tubular system of the target object 149 described herein. In some examples, the infusion device 126 can be a catheter or cannula. In the context of the present disclosure, the term "infusion" can encompass the term "injection". In some aspects, the fluid can be referred to as a perfusate. The terms "medical substance", "substance", "drug", and "perfusate" can be used interchangeably herein.

[0048] The infusion engine 128 may control the infusion of a substance (e.g., a medical substance, a drug, etc.). For example, the infusion engine 128 may control the infusion of the substance into the target object via the robot 114 and the infusion set 126 according to a first flow rate. The infusion engine 128 may set a second flow rate associated with infusing the substance into the target object based on a withdrawal rate of the infusion set 126 from the target object. The infusion engine 128 may control the infusion of the substance into the target object based on the second flow rate.

[0049] The processor 104 may utilize the data stored in the memory 106 as a neural network. The neural network may include a machine learning architecture. In some aspects, the neural network may be or include one or more classifiers. In some other aspects, the neural network may be or include any machine learning network, such as, for example, a deep learning network, a convolutional neural network, a reconstruction neural network, a generative adversarial neural network, or any other neural network capable of implementing the functions of the computing device 102 described herein. Some elements stored in the memory 106 may be described or referred to as instructions or instruction sets, and some functions of the computing device 102 and / or the robot 114 may be implemented using machine learning techniques.

[0050] For example, the processor 104 may support a machine learning model 138 that may be trained and / or updated based on data (e.g., training data 146) provided or accessed by any of the computing device 102, the imaging device 112, the robot 114, the navigation system 118, the infusion device 126, the database 130, and / or the cloud network 134. The machine learning model 138 may be constructed and updated by the monitoring engine 126 based on the training data 146 (also referred to herein as training data and feedback).

[0051] For example, the machine learning model 138 may be trained with one or more training sets included in the training data 146. In some aspects, the training data 146 may include multiple training sets. In an example, the training data 146 may include a first training set that includes infusion rates associated with delivering a target volume of a medical substance (e.g., a drug) via various types of infusion devices 126 (e.g., infusion devices 126 with different corresponding sizes). The training data 146 may include a second training set that includes infusion rates associated with delivering a target volume of a medical substance, the infusion rates being associated with various approach angles of the infusion device 126 relative to the target object. The training data 146 may include a third training set that includes a withdrawal rate associated with withdrawing the infusion device 126, the withdrawal rate being associated with a characteristic (e.g., viscosity) of the medical substance.

[0052] The training data 146 may include a fourth training set including withdrawal rates (and variations therein) associated with the withdrawal infusion device 126, the withdrawal rates (and variations therein) being associated with infusion duration and / or delivered volume of the medical substance.

[0053] In some aspects, the neural network can generate additional training sets based on any of the training sets described herein. In some examples, based on the training data 146, the neural network can generate one or more algorithms (e.g., processing algorithms 142) that support the robotic-assisted drug delivery described herein.

[0054] References later in this article Figure 1B , Figure 2 and Figure 3 Detailed description of the invention is given below with reference to FIG. 1 .

[0055] The database 130 may store information relating one coordinate system to another coordinate system (e.g., relating one or more robot coordinate systems to a patient coordinate system and / or a navigation coordinate system). The database 130 may additionally or alternatively store, for example, one or more surgical plans for use by the robot 114, the navigation system 118, and / or the computing device 102, or a user of the system 100 (including, for example, pose information about the target and / or image information about the patient's anatomical structures at and / or near the surgical site); one or more images that may be used in conjunction with a surgical procedure to be performed by or with the assistance of one or more other components of the system 100; and / or any other useful information. The database 130 may additionally or alternatively store, for example, characteristics of the infusion device 126 (e.g., size, outer diameter, inner diameter, length, etc.) and characteristics of the medical substance to be delivered to the patient (e.g., viscosity, prescribed volume, etc.).

[0056] The database 130 may be configured to provide any such information to the computing device 102 or any other device of the system 100 or any other device external to the system 100, whether directly or via the cloud network 134. In some implementations, the database 130 may include treatment information associated with the patient (e.g., medication delivery plan, medication dosage information, etc.). In some implementations, the database 130 may be or include a part of a hospital image storage system, such as a picture archiving and communication system (PACS), a health information system (HIS), and / or another system for collecting, storing, managing, and / or transmitting electronic medical records including image data.

[0057] In some aspects, computing device 102 may communicate with a server and / or database (e.g., database 130) directly or indirectly through a communication network (e.g., cloud network 134). The communication network may include any type of known communication medium or collection of communication mediums and may use any type of protocol to transfer data between endpoints. The communication network may include wired communication technology, wireless communication technology, or any combination thereof.

[0058] Wired communication technologies may include, for example, Ethernet-based wired local area network (LAN) connections using a physical transmission medium (e.g., coaxial cable, copper cable / wire, fiber optic cable, etc.). Wireless communication technologies may include, for example, cellular or cellular data connections and protocols (e.g., digital cellular, personal communications service (PCS), cellular digital packet data (CDPD), general packet radio service (GPRS), enhanced data rates for global system for mobile communications (GSM) evolution (EDGE), code division multiple access (CDMA), single carrier radio transmission technology (1×RTT), evolution data optimized (EVDO), high speed packet access (HSPA), universal mobile telecommunications service (UMTS), 3G, long term evolution (LTE), 4G and / or 5G, etc.), Low power, Wi-Fi, radio, satellite, infrared connections and / or Communication protocol.

[0059] The Internet is an example of a communication network that constitutes an Internet Protocol (IP) network, which is composed of multiple computers, computing networks and other communication devices located in multiple locations, and the components in the communication network (e.g., computers, computing networks, communication devices) can be connected through one or more telephone systems and other means. Other examples of communication networks may include, but are not limited to, standard plain old telephone system (POTS), integrated services digital network (ISDN), public switched telephone network (PSTN), local area network (LAN), wide area network (WAN), wireless LAN (WLAN), session initiation protocol (SIP) network, voice over Internet protocol (VoIP) network, cellular network, and any other type of packet switching or circuit switching network known in the art. In some cases, the communication network 120 may include any combination of networks or network types. In some aspects, the communication network may include any combination of communication media, such as coaxial cable, copper cable / wire, fiber optic cable, or antennas for communicating data (e.g., transmitting / receiving data).

[0060] The computing device 102 can connect to the cloud network 134 via the communication interface 108 using a wired connection, a wireless connection, or both. In some implementations, the computing device 102 can communicate with the database 130 and / or an external device (e.g., another computing device, the robot 114, etc.) via the cloud network 134. In some aspects, the computing device 102 can be electronically coupled to the robot 114 or integrated with the robot.

[0061] System 100 or a similar system may be used, for example, to implement one or more aspects of any of process flows 200 and 300 described herein. System 100 or a similar system may also be used for other purposes.

[0062] Figure 1B An example 101 of a system 100 that supports aspects of the present disclosure is illustrated. As described herein, the system 100 supports robotic-assisted drug delivery to a patient.

[0063] In an example, the imaging device 112 may provide data 125-a associated with a subject 148 (e.g., a patient) to the computing device 102. The data 125-a may include, for example, image data (also referred to herein as "imaging data") of a region of interest of the subject 148. For example, the region of interest may include the target object 149 (or a portion thereof) and the infusion device 126 (or a portion thereof).

[0064] In another example, navigation system 118 may provide computing device 102 with data 125-b associated with subject 148. Data 125-b may include, for example, sensor information, coordinate information, pose data, guidance data, and / or tracking data, as described herein with reference to navigation system 118. Data 125-a and data 125-b may be collectively referred to herein as data 125.

[0065] The target object 149 may be an anatomical element of the subject 149 (e.g., a brain, brain tissue, other cellular tissue, etc.). Based on the data 125 (e.g., image data, pose data, etc.), the computing device 102 may calculate coordinate information 150 (e.g., real-time or near real-time coordinates) associated with the infusion device 126 and / or the target object 149. Additionally or alternatively, the navigation system 118 may calculate the coordinate information 150 and provide the coordinate information to the computing device 102 and / or the robot 114. In some aspects, using the data 125 (e.g., image data), the computing device 102 may calculate volume information 151 (e.g., volume) of the target object 149.

[0066] Using the data 125, the computing device 102 may calculate parameters associated with inserting the infusion set 126 into the target object 149. For example, the parameters may include a target position (e.g., coordinates) at the target object 149 for inserting the infusion set 126. In some other aspects, the parameters may include a target approach angle of the infusion set 126 relative to the target object 149. For example, the computing device 102 may calculate an approach angle for inserting the infusion set 126 into the target object 149. In an example, the computing device 102 may calculate a target position (e.g., coordinates) and a target approach angle associated with achieving a target fill (e.g., optimal fill) of the target object 149.

[0067] The computing device 102 may calculate a target insertion depth 152 for inserting the infusion device 126 relative to the target object 149. For example, using the data 125, the computing device 102 may determine the target insertion depth 152 for the infusion device 126 relative to the target object 149. Additionally or alternatively, using the data 125 (e.g., image data, tracking data, etc.), the computing device 102 may determine the actual insertion depth of the infusion device 126 at any instance in time.

[0068] In another example, the computing device 102 may set a flow rate 153 associated with infusing a substance (e.g., a drug, etc.) into a target object 149. For example, the computing device 102 may set a flow rate (e.g., a first flow rate) based on an insertion depth of the infusion device 126. Additionally or alternatively, the computing device 102 may set a flow rate (e.g., a first flow rate) based on one or more dimensions of the infusion device 126. For example, the infusion device 126 may be a cannula, and the computing device 102 may set a flow rate based on an outer diameter and / or an inner diameter of the infusion device 126. In some cases, the computing device 102 may set a flow rate (e.g., a first flow rate) based on a target approach angle. The terms "infusion rate" and "flow rate" may be used interchangeably herein.

[0069] Thus, for example, the computing device 102 may set the flow rate based on the insertion depth of the infusion set 126, one or more dimensions of the infusion set 126, and / or the target approach angle. The computing device 102 may further adjust the flow rate at any time instance, examples of which are described herein.

[0070] According to example aspects of the present disclosure, the computing device 102 may infuse a substance into a target object 149 via an infusion device 126 based on a flow rate 153 and a withdrawal rate 154. The computing device 102 may calculate the withdrawal rate 154 based on one or more properties associated with the substance (e.g., viscosity, diffusion coefficient, concentration, etc.). For example, the computing device 102 may set a relatively high withdrawal rate 154 for a substance with a relatively low viscosity. In another example, the computing device 102 may set a relatively low withdrawal rate 154 for a substance with a relatively high viscosity. Other example properties of a substance based on which the computing device 102 may calculate the withdrawal rate 154 include, but are not limited to, the pressure, fluid resistance, and density of the substance.

[0071] The computing device 102 may calculate the remaining volume 155 included in the volume of the target object 149. The computing device 102 may calculate the remaining volume 155 based on the volume information 151, the withdrawal rate 154, the time information (e.g., the elapsed duration of the procedure), and / or one or more dimensions of the infusion set 126 (e.g., outer diameter, inner diameter, length, etc.).

[0072] Computing device 102 may adjust flow rate 153 (e.g., set a second flow rate) at any time instance. For example, computing device 102 may adjust flow rate 153 based on withdrawal rate 154 and remaining capacity 155. Computing device 102 may infuse the substance into target object 149 based on adjusting flow rate 153.

[0073] The computing device 102 may electronically transmit the data 129 to the robot 114 in a data format compatible with the robot 114. The data 129 may include information described herein (e.g., coordinate information 150, volume information 151, target insertion depth 152, flow rate 153, withdrawal rate 154, remaining capacity 155, etc.) and changes thereto. The data 129 may include instructions associated with delivering a medical substance to a subject 148. Using the data 129, the robot 114 may control the robotic arm 116 in association with delivering a medical substance to a subject 148. For example, based on the data 129, the robot 114 may control the robotic arm 116 (e.g., control speed, rotation, etc.) in association with inserting the infusion set 126 into the target object 149 and withdrawing the infusion set 126 from the target object.

[0074] According to example aspects of the present disclosure, computing device 102 may calculate and / or set any parameter value described herein (e.g., coordinate information 150, volume information 151, target insertion depth 152, flow rate 153, withdrawal rate 154, etc.) at any time instance. For example, computing device 102 may calculate, set, and adjust the value of any parameter described herein (e.g., coordinate information 150, volume information 151, target insertion depth 152, flow rate 153, withdrawal rate 154, remaining capacity 155, etc.) before and / or during a medical procedure. Reference will be made later to Figure 2 Examples describing these parameters and their corresponding values.

[0075] Aspects of the present disclosure support the use of (e.g., reference to Figure 1A The computing device 102 may implement aspects of the system 100 using a machine learning model 138 (e.g., data 125-a, data 125-b) to implement aspects of the system 100. For example, the computing device 102 may provide data 125 (e.g., data 125-a, data 125-b) and / or data 127 (e.g., one or more characteristics associated with a medical substance, one or more characteristics associated with an infusion device 126, etc.) to the machine learning model 138. The computing device 102 may receive an output from the machine learning model 128 in response to the machine learning model 128 processing the data 125 (or a portion thereof) and / or the data 127 (or a portion thereof). The output may include data 129 (or a portion thereof). For example, the output may include one or more values ​​of the parameters described herein (e.g., coordinate information 150, volume information 151, target insertion depth 152, flow rate 153, withdrawal rate 154, etc.).

[0076] Aspects of the present disclosure support processing data 125 and / or data 127 and generating data 129 based on one or more criteria. For example, the computing device 102 may process data 125 and / or data 127 and generate data 129 based on a schedule (e.g., periodically, semi-periodically). In some cases, the computing device 102 may process data 125 and / or data 127 and generate data 129 based on a trigger condition (e.g., an update to any of the data 125 and / or data 127). In some other cases, the computing device 102 may regenerate another portion of the data 129 (e.g., flow rate 153, withdrawal rate 154, etc.) based on a change in a portion of the data 129 (e.g., remaining capacity 155).

[0077] In some cases, computing device 102 may process data 125 and / or data 127 and generate data 129 until one or more criteria are met. For example, computing device 102 may adjust flow rate 153 and / or withdrawal rate 154 until the entire prescribed volume of the medical substance is delivered to target subject 149 (e.g., infused into target subject 149).

[0078] Figure 2 An example of a process flow 200 for supporting robotic-assisted drug delivery to a patient in accordance with aspects of the present disclosure is illustrated. In some examples, the process flow 200 may be referred to as Figure 1A and Figure 1B The system 100 is implemented.

[0079] Aspects of the process flow 200 support achieving optimal infusion via the system 100. For example, aspects of 200 support achieving a target infusion rate, a target infusion duration, a target infusion volume, a target coverage area, etc. for a target subject 149. Figure 1B In the example 101 of FIG. 1 , the process flow 200 may include receiving as input a volume of a target object 149 to be infused (eg, volume information 151 ). The terms “target object” and “target structure” may be used interchangeably herein.

[0080] The process flow 200 may include adjusting the infusion rate (e.g., flow rate 153) when withdrawing the infusion set 126 to match the infusion rate to the aspirated capacity for the medical substance (e.g., perfusate) at that volume. In some cases, the computing device 102 may automatically extract the volume of the target object 149 based on the imaging data provided by the imaging device 112. In an example, the computing device 102 may process the image data using one or more image processing techniques (e.g., standard image processing methods).

[0081] In some aspects, computing device 102 may receive input data from a user (e.g., via user interface 110, via another computing device 102, etc.), and the input data may include a volume of target object 149. Additionally or alternatively, computing device 102 may support a combination of extracting a volume based on processed data and receiving input data indicating a volume. For example, computing device 102 may automatically extract a volume. The extracted volume may be checked by a user (e.g., medical personnel) in association with ensuring the accuracy of the volume and / or adjusting its value.

[0082] The computing device 102 may extract and / or receive additional parameters associated with setting and adjusting the infusion rate (e.g., flow rate 153). For example, the computing device 102 may automatically extract parameters associated with adjusting the infusion rate to obtain optimal infusion from the system 100. Examples of extracted parameters include, but are not limited to, the outer and inner diameters of the infusion set 126, the withdrawal rate 154 of the infusion set 126, and the overall withdrawal length. Additionally or alternatively, the computing device 102 may receive input data including any combination of parameters. As described herein, aspects of the processing flow 200 support estimating a target infusion rate using the volume of the target object 149 and other system component variables as inputs.

[0083] An example implementation is now described with reference to process flow 200. In the following description of process flow 200, operations may be performed in a different order than shown, or may be performed in a different order or at a different time. Certain operations may also be omitted from process flow 200, or other operations may be added to process flow 200.

[0084] It should be understood that although the computing device 102 (e.g., the infusion engine 128) is described as performing several of the operations of the processing flow 200, any device (e.g., another computing device 102 in communication with the computing device 102, the robot 114, etc.) may perform the operations shown.

[0085] At 205 , the computing device 102 and / or the robot 114 may begin or initiate the process flow 200 .

[0086] At 210, the process flow 200 may include obtaining a volume of the target object 149 to be infused. For example, at 210, the computing device 102 may obtain as input the volume of the target object 149. The computing device 102 may automatically extract the volume and / or receive input data indicating the volume (e.g., input by a user).

[0087] In some alternative and / or additional aspects, at 210 , the computing device 102 may automatically obtain coordinate information 150 (e.g., coordinates, pose, etc.) of the target object 149 and / or receive input data indicating coordinate information 150 (e.g., from the navigation system 118 , from a user, etc.).

[0088] At 215, the process flow 200 may include calculating a target flow rate associated with infusing the substance into the target object 149 (e.g., estimating an optimal infusion rate associated with infusing the substance into the target object). For example, at 215, the computing device 102 may obtain one or more parameters associated with calculating or estimating the target flow rate. Example parameters include one or more characteristics of the infusion device 126 (e.g., an outer diameter of the infusion device 126, an inner diameter of the infusion device 126, a length of the infusion device 126, etc.) and an approach angle of the infusion device 126 relative to the target object 149, but are not limited to these parameters. The computing device 102 may calculate the target flow rate based on one or more of these parameters.

[0089] At 220, the process flow 200 may include setting the withdrawal rate 154 (e.g., a target or optimal withdrawal rate). For example, at 220, the computing device 102 may automatically set the withdrawal rate 154 based on properties of the substance (e.g., viscosity, etc.). Additionally or alternatively, at 220, the computing device 102 may estimate a total infusion time based on one or more properties of the substance (e.g., viscosity, etc.).

[0090] In some aspects, the system 100 may support user-based adjustments to the withdrawal rate 154. For example, the computing device 102 may receive user input for adjusting the withdrawal rate 154. In an example, if the user determines that the perfusion time is very long (e.g., exceeds a threshold duration), the computing device 102 may receive user input for adjusting the withdrawal rate 154. In some other cases, the computing device 102 may adjust the withdrawal rate 154 based on the threshold duration. For example, for the case where the computing device 102 determines that the elapsed perfusion duration exceeds the threshold duration, the computing device 102 may increase the withdrawal rate.

[0091] At 225, the process flow 200 may include starting an infusion process. For example, at 225, the computing device 102 may initiate the delivery (e.g., infusion) of the medical substance and the withdrawal of the infusion set 126. Additionally, at 225, the computing device 102 may continue the infusion process (e.g., continue the delivery of the medical substance, continue the withdrawal of the infusion set 126) in response to a 'no' decision (aspects of which are described later herein with reference to 235).

[0092] At 230, the process flow 200 may include setting and / or adjusting a flow rate associated with delivering the medical substance to the target object 149. For example, at 230, the computing device 102 may maintain or adjust the flow rate calculated at 215. In an example, the computing device 102 may adjust the flow rate based on one or more characteristics of the infusion set 126 (e.g., an outer diameter of the infusion set 126, an inner diameter of the infusion set 126, a length of the infusion set 126, etc.), the withdrawal rate set at 220, and / or a remaining volume included in the volume of the target object 149. The remaining volume may correspond to, for example, a cavity left by the infusion set 126 in the volume of the target object 149.

[0093] In some examples, the computing device 102 may calculate the remaining volume based on the withdrawal rate of the infusion set 126, the size (e.g., outer diameter) of the infusion set 126, and / or the elapsed infusion duration. Additionally or alternatively, the computing device 102 may calculate the remaining volume based on updated image data provided by the imaging device 112 (e.g., including image data of the infusion set 126 and / or the target object 149). Additionally or alternatively, the computing device 102 may determine the remaining volume based on user input.

[0094] Thus, for example, at 230, the computing device 102 may match the automatic infusion to fill the cavity left by the infusion set 126. In an example, where the withdrawal rate is 1 mm / minute and the outer diameter of the infusion set 126 is 500 μm, the computing device 102 may set the infusion rate to approximately 0.8 mm. 3 / minute flow rate.

[0095] At 235, the process flow 200 may include determining whether to continue or discontinue delivery of the medical substance to the target subject 149. For example, at 235, the process flow 200 may include identifying whether one or more criteria have been met. The process flow 200 may include returning to 225 (e.g., 'Continue Infusion') or proceeding to 240 (e.g., 'Stop') based on whether the one or more criteria have been met. The one or more criteria may be associated with the delivery of the medical substance to the target subject 149. Some examples of criteria are described below.

[0096] In an example, at 235, the computing device 102 (and / or the robot 114) may determine whether the entire dispensed volume of the medical substance has been completely delivered to the target object 149. For example, if the computing device 102 determines that the robot 114 has delivered the entire dispensed volume of the medical substance to the target object 149 (e.g., criteria satisfied = 'yes'), the computing device 102 may electronically transmit to the robot 114 instructions instructing the robot 114 to discontinue delivering the medical substance (e.g., the process flow 200 proceeds to 240 ('stop')). In some additional and / or alternative aspects, the robot 114 may determine that the robot 114 has delivered the entire dispensed volume of the medical substance to the target object 149 (e.g., criteria satisfied = 'yes'), and the robot 114 may discontinue delivering the medical substance (e.g., the process flow 200 proceeds to 240 ('stop')).

[0097] In another example, at 235, if the computing device 102 determines that the robot 114 has not delivered the entire dispensed volume of the medical substance to the target object 149 (e.g., criteria met = 'No'), the computing device 102 may return to 225 (e.g., 'Continue infusion'), 230 ('Adjust flow rate'), and 235 ('Criteria met?'). In some additional and / or alternative aspects, the robot 114 may determine that the robot 114 has not delivered the entire dispensed volume of the medical substance to the target object 149 (e.g., criteria met = 'No'), and the robot 114 may electronically transmit data to the computing device 102. In an example, the data may include an indication that the entire dispensed volume has not been delivered, and the computing device 102 may return to 225 (e.g., 'Continue infusion'), 230 ('Adjust flow rate'), and 235 ('Criteria met?'). In another example, the data may include an indication of the remaining volume of the medical substance to be delivered. Based on the data, the computing device 102 may return to 225 (eg, 'Continue infusion'), 230 ('Adjust flow rate'), and 235 ('Criteria met?').

[0098] In an example implementation, following a 'no' result at 235, the computing device 102 may return to 225 (e.g., 'continue infusion'). At 225, the computing device 102 may calculate the remaining volume included in the volume of the target object 149. In an example, the computing device 102 may calculate the remaining volume based on the withdrawal rate 154 of the infusion device 126, the size of the infusion device 126 (e.g., outer diameter, length, etc.), and / or the elapsed infusion duration. Additionally or alternatively, the computing device 102 may calculate the remaining volume based on updated image data provided by the imaging device 112 and / or updated coordinate information provided by the navigation system 118. Additionally or alternatively, the computing device 102 may determine the remaining volume based on user input.

[0099] Figure 3 An example of a process flow 300 for supporting robotic-assisted drug delivery to a patient in accordance with aspects of the present disclosure is illustrated. In some examples, the process flow 300 may implement a reference Figure 1A and Figure 1B Aspects of the system 100 are described.

[0100] In the following description of process flow 300, operations may be performed in a different order than shown, or may be performed in a different order or at a different time. Certain operations may be omitted from process flow 300, or other operations may be added to process flow 300.

[0101] It should be understood that while the computing device 102 and / or the robot 114 may perform many of the operations of the process flow 300 , any device (e.g., another computing device 102 in communication with the computing device 102 , the robot 114 , etc.) may perform the operations illustrated.

[0102] At 305 , process flow 300 can include calculating a volume of a target object based on image data including the target object.

[0103] At 310 , process flow 300 may include determining an insertion depth of an infusion set relative to a target object based on image data including the target object.

[0104] At 315 , process flow 300 may include determining a target approach angle of the infusion device relative to the target object based on the image data including the target object.

[0105] At 320, the process flow 300 may include setting the first flow rate based on the determined insertion depth. In some aspects, the process flow 300 may include setting the first flow rate based on one or more dimensions of the infusion set. Additionally or alternatively, at 320, the process flow 300 may include setting the first flow rate based on a target approach angle.

[0106] At 325, process flow 300 can include infusing the substance into the target subject via the infusion set according to the first flow rate.

[0107] At 330 , process flow 300 can include calculating a withdrawal rate based on one or more characteristics associated with the substance.

[0108] At 335, process flow 300 can include withdrawing the infusion set based on the withdrawal rate.

[0109] At 340, process flow 300 may include calculating a remaining volume included in the volume of the target object based on the withdrawal rate and one or more dimensions of the infusion set.

[0110] At 345, process flow 300 can include setting a second flow rate associated with infusing the substance into the target object based on a withdrawal rate of the infusion device from the target object. In some aspects, setting the second flow rate can be based on a remaining capacity included in calculating a volume of the target object.

[0111] At 350, process flow 300 can include injecting the substance into the target object based on the second flow rate.

[0112] In some aspects (not illustrated), process flow 300 may include: comparing an elapsed duration associated with an infused substance to a time threshold; and adjusting a withdrawal rate based on a result of the comparison.

[0113] In some example implementations (not illustrated), the process flow 300 may include providing at least one of: image data including a target object and an infusion device; one or more characteristics associated with a substance; and one or more characteristics associated with an infusion device to a machine learning model. The process flow 300 may include receiving an output from the machine learning model in response to the machine learning model processing at least one of the image data, the one or more characteristics associated with the substance, and the one or more characteristics associated with the infusion device, wherein the output includes one or more parameters associated with infusing the substance into the target object.

[0114] In some example implementations, the process flow 300 may include repeating one or more operations until one or more criteria are met. For example, the process flow 300 may include one or more iterations of any of 330 to 350 until the entire specified volume of the medical substance is delivered to the target object (e.g., infused into an anatomical element of the patient).

[0115] In the following description of process flow 300, operations may be performed in a different order than shown, or may be performed in a different order or at a different time. Certain operations may be omitted from process flow 300, or other operations may be added to process flow 300.

[0116] The processing flows 200 and 300 described herein (and / or one or more operations thereof) may be performed or otherwise executed, for example, by at least one processor. The at least one processor may be the same or similar to the processor 104 of the computing device 102 described above. The at least one processor may be part of a robot (such as the robot 114) or part of a navigation system (such as the navigation system 118). Processors other than any processor described herein may also be used to perform the processing flow 400. The at least one processor may perform the operations of the processing flow 400 by executing elements stored in a memory (such as the memory 106). The elements stored in the memory and executed by the processor may cause the processor to perform one or more operations of the functions shown in the processing flow 400. One or more portions of the processing flow 400 may be performed by a processor that executes any of the contents of the memory (such as the image processing 120, the segmentation 122, the transformation 124, the registration 128, and / or the infusion engine 138).

[0117] As mentioned above, the present disclosure covers Figure 2 and Figure 3 (and the corresponding descriptions of process flows 200 and 300) and including methods that have fewer operations than all operations identified in Figure 2 and Figure 3 (and the corresponding descriptions of process flows 200 and 300). The present disclosure also encompasses methods that include one or more operations from one method described herein and one or more operations from another method described herein. Any correlation described herein may be or include registration or any other correlation.

[0118] The foregoing is not intended to limit the present disclosure to one or more forms disclosed herein. In the aforementioned specific embodiments, for example, for the purpose of simplifying the present disclosure, the various features of the present disclosure are grouped together in one or more aspects, specific implementations and / or configurations. The features of the aspects, specific implementations and / or configurations of the present disclosure may be combined in alternative aspects, specific implementations and / or configurations other than those discussed above. The method of the present disclosure should not be interpreted as reflecting the following intention: the claims require more features than the features explicitly stated in each claim. On the contrary, as reflected in the following claims, aspects of the present invention are less than all the features of a single aforementioned disclosed aspect, specific implementation and / or configuration. Therefore, the following claims are hereby incorporated into this specific embodiment, wherein each claim exists independently as a separate preferred specific implementation of the present disclosure.

[0119] In addition, although the foregoing has included descriptions of one or more aspects, specific implementations and / or configurations and certain variations and modifications, other variations, combinations and modifications are within the scope of the present disclosure, for example, within the skill and knowledge of those skilled in the art after understanding the present disclosure. It is intended to obtain rights to include alternative aspects, specific implementations and / or configurations, including alternative, interchangeable and / or equivalent structures, functions, scopes or operations of those claimed, regardless of whether these alternative, interchangeable and / or equivalent structures, functions, scopes or operations are disclosed herein, and it is not intended to disclose any patentable subject matter.

[0120] Example aspects of the present disclosure include:

[0121] A system includes: a processor; and a memory having instructions stored thereon, which, when executed by the processor, cause the processor to: infuse a substance into a target object via an infusion device according to a first flow rate; set a second flow rate associated with infusing the substance into the target object based on a withdrawal rate of the infusion device from the target object; and infuse the substance into the target object based on the second flow rate.

[0122] Any aspect herein, wherein setting the second flow rate is based on calculating a remaining capacity included in a volume of the target object.

[0123] Any aspect of the present invention, wherein the instructions are further executable by the processor to: calculate the remaining capacity based on the withdrawal rate and one or more dimensions of the infusion set.

[0124] Any aspect herein, wherein the instructions are further executable by the processor to: calculate the withdrawal rate based on one or more characteristics associated with the substance; and withdraw the infusion set based on the withdrawal rate.

[0125] Any aspect herein, wherein the instructions are further executable by the processor to: compare an elapsed duration associated with infusing the substance to a time threshold; and adjust the withdrawal rate based on a result of the comparison.

[0126] Any aspect of the present invention, wherein the instructions are further executable by the processor to: calculate a volume of the target object based on image data including the target object.

[0127] Any aspect of the present invention, wherein the instructions may be further executed by the processor to: determine an insertion depth of the infusion device relative to the target object based on image data including the target object; and set the first flow rate based on: determining the insertion depth; and one or more dimensions of the infusion device.

[0128] Any aspect herein, wherein the instructions are further executable by the processor to: determine a target approach angle of the infusion device relative to the target object based on image data including the target object; and set the first flow rate based on the target approach angle.

[0129] Any aspect of the present invention, wherein the instructions are further executable by the processor to: provide to the machine learning model at least one of: image data including the target object and the infusion device; one or more characteristics associated with the substance; and one or more characteristics associated with the infusion device; and receive an output from the machine learning model in response to the machine learning model processing at least one of the image data, the one or more characteristics associated with the substance, and the one or more characteristics associated with the infusion device, wherein the output includes one or more parameters associated with the infusion of the substance into the target object.

[0130] Any aspect herein, wherein the one or more parameters include: the withdrawal rate; the first flow rate; and the second flow rate.

[0131] A system comprising: an infusion device; a processor; and a memory having data stored thereon, the data causing the processor, when processed by the processor, to: infuse a substance into a target object via the infusion device according to a first flow rate; set a second flow rate associated with infusing the substance into the target object based on a withdrawal rate of the infusion device from the target object; and infuse the substance into the target object based on the second flow rate.

[0132] Any aspect herein, wherein setting the second flow rate is based on calculating a remaining capacity included in a volume of the target object.

[0133] Any aspect of the present invention, wherein the instructions are further executable by the processor to: calculate the remaining capacity based on the withdrawal rate and one or more dimensions of the infusion set.

[0134] Any aspect herein, wherein the instructions are further executable by the processor to: calculate the extraction rate based on one or more characteristics associated with the substance; and

[0135] The infusion set is withdrawn based on the withdrawal rate.

[0136] Any aspect herein, wherein the instructions are further executable by the processor to: compare an elapsed duration associated with infusing the substance to a time threshold; and adjust the withdrawal rate based on a result of the comparison.

[0137] Any aspect of the present invention, wherein the instructions are further executable by the processor to: calculate a volume of the target object based on image data including the target object.

[0138] Any aspect of the present invention, wherein the instructions may be further executed by the processor to: determine an insertion depth of the infusion device relative to the target object based on image data including the target object; and set the first flow rate based on: determining the insertion depth; and one or more dimensions of the infusion device.

[0139] Any aspect herein, wherein the instructions are further executable by the processor to: determine a target approach angle of the infusion device relative to the target object based on image data including the target object; and set the first flow rate based on the target approach angle.

[0140] Any aspect of the present invention, wherein the instructions are further executable by the processor to: provide to the machine learning model at least one of: image data including the target object and the infusion device; one or more characteristics associated with the substance; and one or more characteristics associated with the infusion device; and receive an output from the machine learning model in response to the machine learning model processing at least one of the image data, the one or more characteristics associated with the substance, and the one or more characteristics associated with the infusion device, wherein the output includes one or more parameters associated with the infusion of the substance into the target object.

[0141] A method comprising: infusing a substance into a target object via an infusion set according to a first flow rate; setting a second flow rate associated with infusing the substance into the target object in response to determining a withdrawal rate associated with withdrawing the infusion set from the target object; and infusing the substance into the target object based on the second flow rate.

[0142] Any aspect herein, wherein setting the second flow rate is based on calculating a remaining capacity included in a volume of the target object.

[0143] In any of the aspects herein, the method further comprises: calculating the remaining capacity based on the withdrawal rate and one or more dimensions of the infusion set.

[0144] In any of the aspects herein, the method further comprises: calculating the withdrawal rate based on one or more characteristics associated with the substance; and withdrawing the infusion set based on the withdrawal rate.

[0145] In any of the aspects herein, the method further comprises: comparing an elapsed duration associated with infusing the substance to a time threshold; and adjusting the withdrawal rate based on a result of the comparison.

[0146] In any aspect of the present disclosure, the method further comprises: calculating a volume of the target object based on image data including the target object.

[0147] In any aspect herein, the method further comprises: determining an insertion depth of the infusion device relative to the target object based on image data including the target object; and setting the first flow rate based on: determining the insertion depth; and one or more dimensions of the infusion device.

[0148] In any aspect herein, the method further comprises: determining a target approach angle of the infusion device relative to the target object based on image data including the target object; and setting the first flow rate based on the target approach angle.

[0149] In any aspect herein, the method further includes: providing at least one of the following to a machine learning model: image data including the target object and the infusion device; one or more characteristics associated with the substance; and one or more characteristics associated with the infusion device; and receiving an output from the machine learning model in response to the machine learning model processing at least one of the image data, the one or more characteristics associated with the substance, and the one or more characteristics associated with the infusion device, wherein the output includes one or more parameters associated with the infusion of the substance into the target object.

[0150] A system comprising: an infusion device; a robotic device; and an infusion management circuit system, the infusion management circuit system controlling the infusion of a substance by: infusing the substance into a target object via the robotic device and the infusion device according to a first flow rate; setting a second flow rate associated with infusing the substance into the target object based on a withdrawal rate of the infusion device from the target object; and infusing the substance into the target object based on the second flow rate.

[0151] Any aspect herein, wherein: the infusion management circuit system calculates a residual volume included in the volume of the target object; and setting the second flow rate is based on calculating the residual volume.

[0152] Any aspect herein, wherein: the infusion management circuit system calculates the remaining capacity based on the withdrawal rate and one or more dimensions of the infusion set.

[0153] Any aspect of the present invention, wherein: the infusion management circuit system calculates the withdrawal rate based on one or more characteristics associated with the substance; and the robotic device withdraws the infusion set based on the withdrawal rate.

[0154] A non-transitory computer-readable medium comprising instructions which, when executed by a processor, infuse a substance into a target object via an infusion device according to a first flow rate; set a second flow rate associated with infusing the substance into the target object based on a withdrawal rate of the infusion device from the target object; and infuse the substance into the target object based on the second flow rate.

[0155] Any aspect may be combined with any one or more other aspects.

[0156] Any one or more of the features disclosed herein.

[0157] Any one or more of the features is generally disclosed herein.

[0158] Any one or more of the features generally disclosed herein may be combined with any one or more of the other features generally disclosed herein.

[0159] Any aspect / feature / implementation may be combined with any one or more other aspects / features / implementations.

[0160] Use any one or more of the aspects or features disclosed herein.

[0161] It should be understood that any feature described herein may be claimed in combination with any other feature described herein, regardless of whether the features are from the same described implementation.

[0162] The phrases "at least one," "one or more," and "and / or" are open-ended expressions that are both connective and disjunctive in operation. For example, the expressions "at least one of A, B, and C," "at least one of A, B, or C," "one or more of A, B, and C," "one or more of A, B, or C," "A, B and / or C," and "A, B, or C" each mean A only, B only, C only, A and B together, A and C together, B and C together, or A, B, and C together.

[0163] The term "an" entity refers to one or more of that entity. Therefore, the terms "a", "one or more", and "at least one" can be used interchangeably herein. It should also be noted that the terms "including", "comprising", and "having" can be used interchangeably.

[0164] As used herein, the term "automatic" and variations thereof refer to any process or operation, which is generally continuous or semi-continuous, that is performed without substantial human input when the process or operation is performed. However, a process or operation may be automatic even if the performance of the process or operation uses substantial or substantial human input if the input is received prior to the performance of the process or operation. Human input is considered substantial input if it affects the manner in which the process or operation is performed. Human input that consents to the performance of the process or operation is not considered "substantial."

[0165] Aspects of the present disclosure may take the form of an entirely hardware implementation, an entirely software implementation (including firmware, resident software, microcode, etc.), or an implementation combining software and hardware aspects, which may all be generally referred to herein as a "circuit," "module," or "system." Any combination of one or more computer-readable media may be utilized. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium.

[0166] A computer-readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) would include the following: an electrical connector having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer-readable storage medium may be any tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device, or apparatus.

[0167] A computer readable signal medium may include, for example, a propagated data signal in baseband or as part of a carrier wave, the propagated data signal having a computer readable program code embodied therein. Such a propagated signal may take any of a variety of forms, including but not limited to electromagnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that may convey, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or apparatus. The program code embodied in a computer readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

[0168] As used herein, the terms "determine," "calculate," "estimate," and variations thereof are used interchangeably and include any type of methodology, process, mathematical operation, or technique.

Claims

1. A system, comprising: processor; and A memory having instructions stored thereon, which, when executed by the processor, cause the processor to: infusing the substance into the target subject via the infusion device according to a first flow rate; setting a second flow rate associated with infusing the substance into the target subject based at least in part on a withdrawal rate of the infusion set from the target subject; as well as The substance is infused into the target subject based at least in part on the second flow rate.

2. The system of claim 1, wherein setting the second flow rate is based at least in part on calculating a remaining capacity included in a volume of the target object.

3. The system of claim 2, wherein the instructions are further executable by the processor to: The remaining capacity is calculated based at least in part on the withdrawal rate and one or more dimensions of the infusion set.

4. The system of claim 1, wherein the instructions are further executable by the processor to: calculating the withdrawal rate based at least in part on one or more characteristics associated with the substance; and The infusion set is withdrawn based at least in part on the withdrawal rate.

5. The system of claim 1, wherein the instructions are further executable by the processor to: comparing an elapsed duration associated with infusing the substance to a time threshold; and The withdrawal rate is adjusted based at least in part on a result of the comparison.

6. The system of claim 1, wherein the instructions are further executable by the processor to: determining an insertion depth of the infusion set relative to the target object based at least in part on image data including the target object; and The first flow rate is set based at least in part on: determining the insertion depth; and One or more dimensions of the infusion set.

7. The system of claim 1, wherein the instructions are further executable by the processor to: determining a target approach angle of the infusion device relative to the target object based at least in part on image data including the target object; and The first flow rate is set based at least in part on the target approach angle.

8. The system of claim 1, wherein the instructions are further executable by the processor to: Provide the machine learning model with at least one of the following: image data including the target object and the infusion device; one or more properties associated with the substance; and one or more characteristics associated with the infusion device; and Receiving an output from the machine learning model in response to the machine learning model processing the image data, the one or more characteristics associated with the substance, and at least one of the one or more characteristics associated with the infusion device, wherein the output includes one or more parameters associated with the infusion of the substance into the target object.

9. The system of claim 8, wherein the one or more parameters include: The withdrawal rate; the first flow rate; and The second flow rate.

10. A system, comprising: Infusion sets; processor; and a memory having data stored thereon, the data, when processed by the processor, causing the processor to: infusing a substance into a target subject via the infusion device according to a first flow rate; setting a second flow rate associated with infusing the substance into the target subject based at least in part on a withdrawal rate of the infusion set from the target subject; as well as The substance is infused into the target subject based at least in part on the second flow rate.

11. The system of claim 10, wherein setting the second flow rate is based at least in part on a remaining capacity included in calculating a volume of the target object.

12. The system of claim 11, wherein the instructions are further executable by the processor to: The remaining capacity is calculated based at least in part on the withdrawal rate and one or more dimensions of the infusion set.

13. The system of claim 10, wherein the instructions are further executable by the processor to: calculating the withdrawal rate based at least in part on one or more characteristics associated with the substance; and The infusion set is withdrawn based at least in part on the withdrawal rate.

14. The system of claim 10, wherein the instructions are further executable by the processor to: comparing an elapsed duration associated with infusing the substance to a time threshold; and The withdrawal rate is adjusted based at least in part on a result of the comparison.

15. The system of claim 10, wherein the instructions are further executable by the processor to: determining an insertion depth of the infusion set relative to the target object based at least in part on image data including the target object; and The first flow rate is set based at least in part on: determining the insertion depth; and One or more dimensions of the infusion set.

16. The system of claim 10, wherein the instructions are further executable by the processor to: determining a target approach angle of the infusion device relative to the target object based at least in part on image data including the target object; and The first flow rate is set based at least in part on the target approach angle.

17. The system of claim 10, wherein the instructions are further executable by the processor to: Provide the machine learning model with at least one of the following: image data including the target object and the infusion device; one or more properties associated with the substance; and one or more characteristics associated with the infusion device; and Receiving an output from the machine learning model in response to the machine learning model processing the image data, the one or more characteristics associated with the substance, and at least one of the one or more characteristics associated with the infusion device, wherein the output includes one or more parameters associated with the infusion of the substance into the target object.

18. A method comprising: infusing the substance into the target subject via the infusion device according to a first flow rate; setting a second flow rate associated with infusing the substance into the target subject in response to determining a withdrawal rate associated with withdrawing the infusion set from the target subject; as well as The substance is infused into the target subject based at least in part on the second flow rate.

19. The method of claim 18, wherein setting the second flow rate is based at least in part on a remaining capacity included in calculating a volume of the target object.

20. The method according to claim 19, further comprising: The remaining capacity is calculated based at least in part on the withdrawal rate and one or more dimensions of the infusion set.

21. The method according to claim 18, further comprising: calculating the withdrawal rate based at least in part on one or more characteristics associated with the substance; as well as The infusion set is withdrawn based at least in part on the withdrawal rate.

22. The method of claim 18, further comprising: comparing an elapsed duration associated with infusing the substance to a time threshold; as well as The withdrawal rate is adjusted based at least in part on a result of the comparison.

23. The method of claim 18, further comprising: determining an insertion depth of the infusion device relative to the target object based at least in part on image data including the target object; as well as The first flow rate is set based at least in part on: determining the insertion depth; and One or more dimensions of the infusion set.

24. The method of claim 18, further comprising: determining a target approach angle of the infusion device relative to the target object based at least in part on image data including the target object; as well as The first flow rate is set based at least in part on the target approach angle.

25. The method of claim 18, further comprising: Provide the machine learning model with at least one of the following: image data including the target object and the infusion device; one or more properties associated with the substance; and one or more characteristics associated with the infusion device; as well as Receiving an output from the machine learning model in response to the machine learning model processing the image data, the one or more characteristics associated with the substance, and at least one of the one or more characteristics associated with the infusion device, wherein the output includes one or more parameters associated with the infusion of the substance into the target object.

26. A system, comprising: Infusion sets; Robotic devices; Infusion management circuitry that controls the infusion of a substance by: infusing the substance into a target subject via the robotic device and the infusion device according to a first flow rate; setting a second flow rate associated with infusing the substance into the target subject based at least in part on a withdrawal rate of the infusion set from the target subject; as well as The substance is infused into the target subject based at least in part on the second flow rate.

27. The system of claim 26, wherein: The infusion management circuitry calculates a remaining volume included in the target subject's volume; and Setting the second flow rate is based at least in part on calculating the remaining capacity.

28. The system of claim 27, wherein: The infusion management circuitry calculates the remaining capacity based at least in part on the withdrawal rate and one or more dimensions of the infusion set.

29. The system of claim 26, wherein: The infusion management circuitry calculates the withdrawal rate based at least in part on one or more characteristics associated with the substance; and The robotic device withdraws the infusion set based at least in part on the withdrawal rate.

30. A non-transitory computer readable medium comprising instructions that, when executed by a processor: infusing the substance into the target subject via the infusion device according to a first flow rate; setting a second flow rate associated with infusing the substance into the target subject based at least in part on a withdrawal rate of the infusion set from the target subject; and The substance is infused into the target subject based at least in part on the second flow rate.

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