Systems, methods, and computer programs for predictive maintenance

Abstract

To provide a method, system and computer program for predictive maintenance.SOLUTION: The method may include: receiving operation data associated with one or more injection systems, where the operation data includes one or more operation parameters associated with one or more operations of the one or more injection systems; determining one or more prediction scores for the one or more injection systems based on the operation data, where the one or more prediction scores include one or more predictions of one or more operation failures or misuses for the one or more injection systems; and providing maintenance data associated with the one or more operation failures or misuses, where the maintenance data is based on the one or more prediction scores.SELECTED DRAWING: Figure 1A

Description

Technical Field

[0001] Cross - Reference to Related Applications This application claims the benefit of U.S. Provisional Application No. 62 / 547,300, filed Aug. 18, 2017, the entire disclosure of which is hereby incorporated by reference in its entirety.

[0002] This disclosure generally relates to systems, devices, products, apparatuses, and methods used for predictive maintenance, and in one particular embodiment, to systems, products, and methods for predictive maintenance of an infusion system.

Background Art

[0003] Usability (e.g., uptime for performing operations, proper functioning and / or performance of operations, components, devices, functions, and / or non - malfunction of operations, etc.), and proper use of an infusion system (e.g., by a user or operator, etc.) as part of, for example, an imaging room can affect life - saving diagnosis and monitoring of a patient's illness or medical condition for medical treatment. If the infusion system (e.g., one or more components or devices of the infusion system, etc.) malfunctions or is used inappropriately, imaging may be interrupted, and / or the patient's medical treatment and / or therapy may be delayed and / or performed inappropriately. Therefore, in the art, there is a need to improve the usability and usage method of the infusion system (e.g., to reduce or prevent downtime, inappropriate functions, malfunctions, and / or inappropriate use by the user or operator of the infusion system, etc.).

Summary of the Invention

Means for Solving the Problems

[0004] Accordingly, systems, devices, products, apparatus, and / or methods are provided for a maintenance prediction system that improves the availability and / or use of an injection system by predicting malfunctions and / or misuse of the injection system before the injection system fails and / or is misused, and by providing maintenance data related to the predicted malfunctions and / or misuse. For example, conventional injection systems do not have a mechanism for predicting malfunctions and / or misuse of the injection system (e.g., one or more components or devices of the injection system, one or more operations of the injection system, etc.) before the injection system fails and / or is misused. Thus, conventional injection systems may not provide the following: (iv) automatic assurance of regulatory compliance of the injection system before the injection system fails and / or is misused (e.g., scheduling and / or execution of repairs, inspections, updates and / or replacements of the injection system (e.g., one or more components or devices of the injection system); (v) inspection benchmarks of the inventory system and / or similar; (iv) automatic assurance of regulatory compliance of the injection system before the injection system fails and / or is misused; (v) inspection benchmarks of the inventory system and / or similar.

[0005] In non-limiting embodiments or aspects, a computer-based method for predictive maintenance is provided, the method comprising: a computer system having one or more processors receiving operational data associated with one or more injection systems, wherein the operational data includes one or more operational parameters relating to one or more operations of the one or more injection systems; the computer system determining one or more predictive scores for the one or more injection systems based on the operational data, wherein the one or more predictive scores include one or more predictions of one or more operational failures or misuses relating to the one or more injection systems; and the computer system providing maintenance data relating to one or more operational failures or misuses, wherein the maintenance data is based on the one or more predictive scores.

[0006] In a non-limiting embodiment or aspect, a predictive maintenance system is provided comprising a computer system having one or more processors, the one or more processors being programmed or configured to receive operational data associated with one or more injection systems, the operational data comprising one or more operational parameters relating to one or more operations of the one or more injection systems, and to determine one or more predictive scores for the one or more injection systems based on the operational data, the one or more predictive scores comprising one or more predictions of one or more operational failures or misuses relating to the one or more injection systems, and to provide maintenance data relating to one or more operational failures or misuses, the maintenance data being based on the one or more predictive scores.

[0007] In non-limiting embodiments or aspects, a computer program product for predictive maintenance is provided, the computer program product comprising at least one non-temporary computer-readable medium containing one or more instructions, the instructions causing at least one processor to receive operational data associated with one or more injection systems when executed by at least one processor, the operational data containing one or more operational parameters relating to one or more operations of the one or more injection systems, the instructions causing the processor to determine one or more predictive scores for the one or more injection systems based on the operational data, the one or more predictive scores containing one or more predictions of one or more operational failures or misuses relating to the one or more injection systems, the instructions causing the processor to provide maintenance data related to one or more operational failures or misuses, and the maintenance data based on the one or more predictive scores.

[0008] In some non-limiting embodiments or aspects, maintenance data includes a prompt that prompts the user to initiate at least one maintenance action related to one or more injection systems.

[0009] In some non-limiting embodiments or aspects, at least one maintenance action includes scheduling inspections for one or more injection systems, operating one or more injection systems in a particular manner indicated by maintenance data, or any combination thereof.

[0010] In some non-limiting embodiments or configurations, maintenance data includes instructions to one or more injection systems that cause one or more injection systems to automatically initiate at least one maintenance action.

[0011] In some non-limiting embodiments or aspects, at least one maintenance activity includes at least one of scheduling inspections for one or more injection systems, performing specific operations on one or more injection systems, or any combination thereof.

[0012] In some non-limiting embodiments or configurations, one or more operating parameters include a cleanliness evaluation related to the cleanliness of one or more injection systems.

[0013] In some non-limiting embodiments or aspects, the method further includes the step of determining a cleanliness rating based on at least one of the following: one or more images of one or more injection systems, one or more force measurements of the injector motors of one or more injection systems, or any combination thereof, using a computer system.

[0014] In some non-limiting embodiments or aspects, one or more processors are further programmed or configured to determine a cleanliness rating based on at least one of one or more images of one or more injection systems, one or more force measurements of an injector motor of one or more injection systems, or any combination thereof.

[0015] In some non-limiting embodiments or aspects, the instruction further causes at least one processor to determine a cleanliness assessment based on at least one of one or more images of one or more injection systems, one or more force measurements of an injector motor of one or more injection systems, or any combination thereof.

[0016] In some non-limiting embodiments or aspects, one or more operational failures or misuses relating to one or more injection systems include at least one of the following: failure of an electrical component, failure of a software component, failure of a mechanical component, receiving user input from a user of one or more injection systems that causes one or more injection systems to operate contrary to one or more predetermined operational thresholds, or any combination thereof.

[0017] Further non-limiting embodiments or aspects are described in the following numbered sections.

[0018] Item 1. A computer-based method for predictive maintenance, comprising: a computer system having one or more processors receiving operational data associated with one or more injection systems, wherein the operational data includes one or more operational parameters relating to one or more operations of the one or more injection systems; the computer system determining one or more predictive scores for the one or more injection systems based on the operational data, wherein the one or more predictive scores include one or more predictions of one or more operational failures or misuses relating to the one or more injection systems; and the computer system providing maintenance data relating to one or more operational failures or misuses, wherein the maintenance data is based on the one or more predictive scores.

[0019] Item 2. Maintenance data is a method performed by the computer described in Item 1, which includes a prompt to the user to initiate at least one maintenance action related to one or more injection systems.

[0020] Item 3. A method performed by a computer as described in Item 1 or 2, wherein at least one maintenance action includes scheduling inspections for one or more injection systems, operating the one or more injection systems in a particular manner indicated by maintenance data, or any combination thereof.

[0021] Item 4. Maintenance data is a method performed by a computer as described in any one of items 1 to 3, which includes instructions to one or more injection systems that cause one or more injection systems to automatically initiate at least one maintenance action.

[0022] Item 5. A method performed by a computer as described in any one of items 1 to 4, wherein at least one maintenance action includes scheduling inspections for one or more injection systems, performing specific operations by one or more injection systems, or any combination thereof.

[0023] 6. A computer-operated method according to any one of sections 1 to 5, wherein one or more operating parameters include a cleanliness assessment relating to the cleanliness of one or more injection systems, and the method further includes the step of a computer system determining a cleanliness assessment based on at least one of the following: one or more images of one or more injection systems, one or more force measurements of the injector motors of one or more injection systems, or any combination thereof.

[0024] Item 7. One or more malfunctions or misuses relating to one or more injection systems include at least one of the following: a failure of an electrical component, a failure of a software component, a failure of a mechanical component, receiving user input from a user of one or more injection systems causing one or more injection systems to operate contrary to one or more predetermined operating thresholds, or any combination thereof, performed by a computer as described in any one of items 1 to 6.

[0025] Item 8. A predictive maintenance system comprising a computer system having one or more processors programmed or configured to receive operational data associated with one or more injection systems, wherein the operational data includes one or more operational parameters relating to one or more operations of the one or more injection systems, and to determine one or more predictive scores for the one or more injection systems based on the operational data, wherein the one or more predictive scores include one or more predictions of one or more operational failures or misuses relating to the one or more injection systems, and to provide maintenance data relating to one or more operational failures or misuses, wherein the maintenance data is based on one or more predictive scores.

[0026] Item 9. Maintenance data includes a prompt that prompts the user to initiate at least one maintenance action related to one or more injection systems, as described in Item 8.

[0027] Item 10. The at least one maintenance action includes at least one of scheduling an inspection for one or more injection systems, operating one or more injection systems in a particular manner indicated by maintenance data, or any combination thereof, for the system according to item 8 or 9.

[0028] Item 11. The maintenance data includes instructions to one or more injection systems to automatically initiate at least one maintenance action on one or more injection systems, for the system according to any one of items 8 to 10.

[0029] Item 12. The at least one maintenance action includes at least one of scheduling an inspection for one or more injection systems, performing a particular operation by one or more injection systems, or any combination thereof, for the system according to any one of items 8 to 11.

[0030] Item 13. The one or more operating parameters include a cleanliness evaluation related to the cleanliness of one or more injection systems, and the one or more processors are further programmed or configured to determine the cleanliness evaluation based on at least one of one or more images of one or more injection systems, one or more force measurement values of the injector motors of one or more injection systems, or any combination thereof, for the system according to any one of items 8 to 12.

[0031] Item 14. The one or more operating failures or misuses related to one or more injection systems include at least one of a failure of an electrical component, a failure of a software component, a failure of a mechanical component, receiving user input from a user of one or more injection systems to operate one or more injection systems contrary to one or more predetermined operating thresholds, or any combination thereof, for the system according to any one of items 8 to 13.

[0032] Item 15. A computer program product for predictive maintenance, the computer program product comprising at least one non-temporary computer-readable medium containing one or more instructions, the instructions, when executed by at least one processor, cause the at least one processor to receive operational data associated with one or more injection systems, the operational data containing one or more operational parameters relating to one or more operations of one or more injection systems, the instructions causing the at least one processor to determine one or more predictive scores for one or more injection systems based on the operational data, the one or more predictive scores containing one or more predictions of one or more operational failures or misuses relating to one or more injection systems, the instructions causing the at least one processor to provide maintenance data relating to one or more operational failures or misuses, and the maintenance data based on one or more predictive scores.

[0033] Item 16. Maintenance data is a computer program product as described in Item 15, which includes a prompt that encourages the user to initiate at least one maintenance action related to one or more injection systems.

[0034] Item 17. A computer program product as described in Item 15 or 16, wherein at least one maintenance action includes scheduling inspections for one or more injection systems, operating one or more injection systems in a particular manner indicated by maintenance data, or any combination thereof.

[0035] Item 18. A computer program product as described in any one of items 15 to 17, wherein maintenance data includes instructions to one or more injection systems that cause one or more injection systems to automatically initiate at least one maintenance action.

[0036] Item 19. A computer program product as described in any one of items 15 to 18, wherein at least one maintenance action includes scheduling inspections for one or more injection systems, performing specific operations by one or more injection systems, or any combination thereof.

[0037] Item 20. A computer program product as described in any one of items 15 to 19, wherein one or more operating parameters include a cleanliness evaluation related to the cleanliness of one or more injection systems, and the instruction causes at least one processor to determine a cleanliness evaluation based on at least one of the following: one or more images of one or more injection systems, one or more force measurements of the injector motors of one or more injection systems, or any combination thereof.

[0038] Item 21. A computer program product as described in any one of items 15 to 20, wherein one or more malfunctions or misuses relating to one or more injection systems include at least one of: a failure of an electrical component, a failure of a software component, a failure of a mechanical component, or the reception by one or more injection systems of user input from a user of one or more injection systems that causes one or more injection systems to operate in violation of one or more predetermined operating thresholds.

[0039] These and other features and characteristics of the present invention, as well as the operation and function of combinations of related structural elements and components, and the economics of manufacture, will become more apparent with regard to the following description relating to the accompanying drawings and the accompanying claims, all of which form part of this specification, where similar reference numbers indicate corresponding parts in various figures. However, it should be clearly understood that the drawings are for illustrative and explanatory purposes only and are not intended to define any limitations of the present invention. The singular forms “a,” “an,” and “the” used in the specification and claims refer to multiple objects unless the context otherwise explicitly indicates.

[0040] The additional advantages and details of the present invention will be described in more detail below in relation to typical embodiments or aspects shown in the attached schematic diagrams. [Brief explanation of the drawing]

[0041] [Figure 1A]This figure shows non-limiting embodiments or aspects of environments in which systems, devices, products, apparatus, and / or methods described herein may be realized in accordance with the principles of the present invention. [Figure 1B] Figure 1A is a diagram of a non-limiting embodiment or aspect of the injection system shown. [Figure 1C] Figure 1A is a diagram of a non-limiting embodiment or aspect of the injection system shown. [Figure 2] Figures 1A to 1C are diagrams of non-limiting embodiments or aspects of components of one or more systems or devices. [Figure 3] This is a flowchart of a non-limiting embodiment or aspect of a process for predictive maintenance. [Modes for carrying out the invention]

[0042] For the purposes of the following description, the terms “end,” “upper,” “lower,” “right,” “left,” “vertical,” “horizontal,” “upper end,” “lower end,” “lateral,” and “vertical,” and their derivatives, shall be used in reference to the invention as it is oriented in the drawings. However, it should be understood that, unless otherwise clearly stated, the present invention may envision a variety of other variations and step sequences. It should also be understood that the specific devices and processes shown in the accompanying drawings and described in the following specification are merely typical embodiments or aspects of the present invention. Therefore, specific dimensions and other physical characteristics relating to embodiments or aspects of embodiments or aspects disclosed herein should not be considered limiting unless otherwise suggested.

[0043] Any aspects, components, elements, structures, actions, steps, functions, instructions, and / or similar items used herein should not be construed as important or essential unless expressly stated otherwise. Furthermore, the articles “a” and “an” used herein are intended to include one or more items and may be used interchangeably with “one or more” and “at least one.” Additionally, the term “set” used herein is intended to include one or more items (e.g., related items, unrelated items, combinations of related and unrelated items) and may be used interchangeably with “one or more” or “at least one.” When only one item is relevant, the term “one” or similar language is used. Furthermore, terms such as “has,” “have,” and “having” used herein are intended to be non-restrictive. Finally, the expression “based on” is intended to mean “at least partially based on” unless otherwise specified.

[0044] As used herein, the terms “communication” and “communicate” may mean the reception, acceptance, transmission, transfer, provision, etc., of information (e.g., data, signals, messages, instructions, commands, and / or similar). For one unit (e.g., a device, system, components of a device or system, combinations thereof, and / or similar) to communicate with another unit means that one unit can directly or indirectly receive information from and / or transmit information to the other unit. This may mean a direct or indirect connection that is essentially wired and / or wireless. Furthermore, two units may communicate with each other even if the transmitted information is modified, processed, relayed, and / or routed between the first and second units. For example, the first unit may communicate with the second unit even if the first unit passively receives information and does not actively transmit information to the second unit. As another example, the first unit may communicate with the second unit if at least one intermediate unit (e.g., a third unit located between the first and second units) processes information received from the first unit and communicates the processed information to the second unit. In some non-limiting embodiments or aspects, a message may refer to a network packet containing data (e.g., a data packet and / or similar). It should be understood that many other configurations are possible.

[0045] As used herein, the term “server” may refer to one or more computer devices such as processors, storage devices, and / or similar computer components that communicate with client devices and / or other computer devices over a network such as the Internet or a private network, and in some examples may facilitate communication between other servers and / or client devices. It should be understood that various other configurations are possible. As used herein, the term “system” may refer to one or more computer devices or combinations of computer devices such as processors, servers, client devices, software applications, and / or other similar components, but is not limited to these. In addition, as used herein, references to “server” or “processor” may refer to already listed servers and / or processors, different servers and / or processors, and / or combinations of servers and / or processors listed to perform a previous step or function. For example, as used in the specification and claims, a first server and / or first processor listed to perform a first step or function may refer to the same or different servers and / or processors listed to perform a second step or function.

[0046] Non-limiting embodiments or aspects of the present invention relate to systems, devices, products, apparatus, and / or methods for a maintenance prediction system that improves the availability and / or use of an injection system by predicting malfunctions and / or misuse of an injection system before the injection system fails and / or is used improperly, and by providing maintenance data related to the predicted malfunctions and / or misuse.

[0047] Thus, embodiments or aspects of the present invention enable (I) repairing, inspecting, updating, and / or replacing an infusion system (e.g., one or more components or devices of the infusion system, one or more operations of the infusion system, etc.) before it malfunctions and / or is misused (e.g., scheduling and / or performing repairs, inspections, updates, and / or replacements), thereby enabling (a) reducing or preventing downtime of the infusion system, (b) extending the lifespan of the infusion system (e.g., reducing the time to failure requiring replacement of the infusion system), (c) increasing the number and / or likelihood of beneficial treatments and / or therapies for patients, and (d) improving the efficiency of scheduling and / or performing repairs, inspections, updates, and / or replacements (e.g., automatically scheduling and / or performing repairs, inspections, updates, and / or replacements related to the infusion system, automatically prompting the user to schedule and / or perform repairs, inspections, updates, and / or replacements in the infusion system). (i) or similarly possible, (ii) providing users or operators of the infusion system with maintenance data (e.g., maintenance actions, training information, etc.) that can be performed by the user or operator to reduce the risk of failure and / or misuse of the infusion system (e.g., probability, likelihood, etc.), thereby (a) reducing or preventing continued misuse of the infusion system, (b) improving image quality and / or patient care (e.g., reducing the occurrence of repeated infusions and / or scans, etc.), (c) reducing contrast waste and / or similarly possible, (iii) providing maintenance data (e.g., information on components and / or devices (e.g., replacement parts, inspection tools, etc.) and / or operation (e.g., inspection records, error codes, inspection procedures, etc.)) to inspection technicians and / or users or operators of the infusion system to repair, inspect, update and / or replace the infusion system, thereby (a) enabling inspection technicians to have information for specific repairs, inspections, updates and / or replacements, (b) multiple repairs, inspections, updates and / or(IV) It is possible to increase the efficiency of replacement scheduling and / or similarly, (a) to automatically ensure regulatory compliance of the infusion system before the infusion system becomes non-compliant with one or more regulations, thereby (a) ensuring the calibration settings of the infusion system, (b) reducing patient infections, (c) increasing the cleanliness of the infusion system and / or similarly, (V) to provide inspection criteria for the inventory system, thereby (a) providing information such as assurance forecasts, inspection inventory plans, inspection resource plans, etc., which can be used by users or customers to improve their knowledge base for preventive maintenance, (b) defining use cases for future products, (c) improving training focus areas and / or similarly, and / or similarly.

[0048] Referring here to Figure 1A, Figure 1A is a diagram of an example environment 100 in which the devices, systems, and / or methods described herein may be implemented. As shown in Figure 1A, the environment 100 includes a maintenance prediction system 102, an injection system 104, a remote system 106, and / or a network 108. The maintenance prediction system 102, the injection system 104, and / or the remote system 106 may be interconnected (e.g., by establishing a connection for communication) via wired connections, wireless connections, or a combination of wired and wireless connections.

[0049] In some non-limiting embodiments or aspects, the maintenance prediction system 102 includes one or more devices capable of receiving data and / or information (e.g., operational data, maintenance data, etc.) from the injection system 104 and / or the remote system 106 via the network 108 and / or communicating data and / or information (e.g., operational data, maintenance data, etc.) to the injection system 104 and / or the remote system 106 via the network 108. For example, the maintenance prediction system 102 may include computer devices such as one or more computers, portable computers (e.g., tablet computers), mobile devices (e.g., mobile phones, smartphones, watches, glasses, lenses, and / or wearable devices such as clothing, PDAs, and / or similar), servers (e.g., transaction processing servers), groups of servers, and / or other similar devices. In some non-limiting embodiments or aspects, the maintenance prediction system 102 communicates with a data storage device which may be local or remote to the maintenance prediction system 102. In some non-limiting embodiments or aspects, the maintenance prediction system 102 can receive data and / or information from a data storage device, store data and / or information in the data storage device, communicate data and / or information to the data storage device, or retrieve data and / or information (e.g., operational data, maintenance data, etc.) stored in the data storage device. In some non-limiting embodiments or aspects, the maintenance prediction system 102 may be implemented within the injection system 104 and / or within the remote system 106.

[0050] In some non-limiting embodiments or aspects, the injection system 104 includes one or more devices capable of receiving data and / or information (e.g., operational data, maintenance data, etc.) from the maintenance prediction system 102 and / or the remote system 106 via the network 108 and / or communicating data and / or information (e.g., operational data, maintenance data, etc.) to the maintenance prediction system 102 and / or the remote system 106 via the network 108. For example, the injection system 104 may include computer devices such as one or more computers, portable computers (e.g., tablet computers), mobile devices (e.g., mobile phones, smartphones, watches, glasses, lenses, and / or wearable devices such as clothing, PDAs, and / or similar), servers (e.g., transaction processing servers), groups of servers, and / or other similar devices. In some non-limiting embodiments or aspects, the injection system 104 includes one or more devices (e.g., one or more sensors (e.g., flow sensors, temperature sensors, accelerometers, vibration monitoring sensors, strain gauges, motor current sensors, optical sensors (e.g., barcode scanners, etc.), image sensors (e.g., digital cameras, etc.), one or more input components, one or more communication interfaces, etc.)) that can receive, determine, measure, and / or sense operational data associated with the injection system 104. For example, the operational data may include one or more operational parameters associated with one or more operations of the injection system 104, and the injection system 104 may receive, determine, measure, and / or sense one or more operational parameters. In some non-limiting embodiments or aspects, the injection system 104 communicates with a data storage device that may be local or remote to the injection system 104. In some non-limiting embodiments or configurations, the injection system 104 can receive data and / or information from a data storage device, store data and / or information in the data storage device, communicate data and / or information to the data storage device, or retrieve data and / or information (e.g., operation data, maintenance data, etc.) stored in the data storage device.

[0051] In some non-limiting embodiments or aspects, the injection system 104 includes a plurality of injection systems 104. For example, the plurality of injection systems 104 can receive data and / or information (e.g., operational data, maintenance data, etc.) from the maintenance prediction system 102, the remote system 106, and / or each other via the network 108, and / or communicate data and / or information (e.g., operational data, maintenance data, etc.) with each other via the network 108. In some non-limiting embodiments or aspects, one or more of the plurality of injection systems 104 may differ from one or more of the other injection systems 104 (e.g., different types of injection systems such as injection systems from different manufacturers, different models of injection systems, different versions of the same model of injection system, injection systems for different types of injections (e.g., CT-based injection, MRI-based injection, etc.), and / or similar). In some non-limiting embodiments or embodiments, one or more of the multiple injection systems 104 may be identical to one or more other injection systems of the multiple injection systems 104 (e.g., injection systems of the same type, such as injection systems from the same manufacturer, injection systems of the same model, injection systems of the same version and model, injection systems for the same type of injection (e.g., CT-based injection, MRI-based injection, etc.), and / or similar). In some non-limiting embodiments or embodiments, one or more of the multiple injection systems 104 may be separate from one or more other injection systems of the multiple injection systems 104 (e.g., separate injection systems in the same location, separate injection systems at the same imaging site, separate injection systems at different remote locations, separate injection systems at different imaging sites, etc.).

[0052] In some non-limiting embodiments or embodiments, the infusion system 104 is configured to inject, deliver, or administer a contrast fluid containing a contrast agent to a patient, and in some non-limiting embodiments or embodiments, the infusion system 104 is further configured to inject or administer saline or other fluid to the patient before, during, or after administration of the contrast fluid. For example, the infusion system 104 can directly inject one or more prescribed doses of contrast fluid into the patient's bloodstream via a subcutaneous needle and syringe. In some non-limiting embodiments or embodiments, the infusion system 104 is configured to continuously administer saline to the patient via a peripheral IV line (PIV) and catheter, and one or more prescribed doses of contrast fluid may be introduced into the PIV and administered to the patient via the catheter. In some non-limiting embodiments or embodiments, the infusion system 104 is configured to administer a specific amount of saline after injecting a predetermined dose of contrast fluid.

[0053] In some non-limiting embodiments or aspects, the infusion system 104 is configured to administer a single contrast agent. In some non-limiting embodiments or aspects, the infusion system 104 is configured to deliver two or more different contrast agents. In implementations in which the infusion system 104 is configured to deliver multiple contrast agents, the infusion system may be configured to allow the operator to switch between forms depending on the intended procedure. The amount of each contrast agent delivered by the infusion system 104 may vary based on the infusion protocol being used. For example, a specific infusion protocol can be used to achieve desired blood, plasma, and / or tissue levels of contrast agent. A physician or other qualified healthcare professional (and / or the infusion system 104) can determine an appropriate infusion protocol, which is configured to deliver the contrast agent to a particular patient using patient metrics (e.g., age, weight, height, body mass index (BMI), cardiac output, type of procedure to be performed, etc.) according to this protocol. The infusion system 104 may be configured to inject two or more contrast agents individually, sequentially, or simultaneously. Therefore, in some non-limiting embodiments or aspects, the infusion system 104 may include two or more reservoirs, such as vials or syringes, that can hold a radiopharmaceutical before administration. The infusion system 104 may further include an additional medical fluid reservoir that can hold, for example, saline solution, other drugs, or other fluids.

[0054] In some non-limiting embodiments or aspects, the injection system 104 is specified in U.S. Patent Application No. 09 / 267,238, filed on March 12, 1999, issued as U.S. Patent No. 6,317,623; U.S. Patent Application No. 09 / 715,330, filed on November 17, 2000, issued as U.S. Patent No. 6,643,537; and U.S. Patent Application No. 09 / 982, filed on October 18, 2001, issued as U.S. Patent No. 7,094,216. U.S. Patent Application No. 10 / 326,582, filed on December 20, 2002, was published as U.S. Patent No. 7,549,977, U.S. Patent Application No. 10 / 825,866, filed on April 16, 2004, was published as U.S. Patent No. 7,556,619, and U.S. Patent Application No. 12 / 437,011, filed on May 7, 2009, was published as U.S. Patent No. 8,337,456, U.S. Patent No. 8,147,464. U.S. Patent Application No. 12 / 476,513, filed on June 2, 2009, issued as U.S. Patent No. 8,540,698; U.S. Patent Application No. 11 / 004,670, filed on December 3, 2004, issued as U.S. Patent No. 9,463,335; U.S. Patent Application No. 14 / 826,602, filed on August 14, 2015, issued as U.S. Patent No. 9,463,335; and International Application No. PCT / US2016 / 046587, filed on August 11, 2016. This application includes one or more typical injection systems, fluid delivery systems, and / or injectors disclosed in International Patent Application Publication WO2017 / 027724A1, published on 16 February 2017, and International Patent Application Publication WO2017 / 040152A1, filed on 24 August 2016 as International Application PCT / US2016 / 048441 and published on 9 March 2017, the disclosures of each of these patent applications incorporated in their entirety by reference.In some non-limiting embodiments or configurations, the injection system 104 includes the MEDRAD® Stellant CT Injection System with the Certegra® Workstation provided by Bayer and / or the MEDRAD® MRXperion MR Injection System with the Radimetrics® Enterprise Platform also provided by Bayer.

[0055] In some non-limiting embodiments or aspects, the infusion system 104 includes one or more flow sensors for directly measuring the flow rate and / or volume of a fluid flow. For example, referring to Figure 1B, the infusion system 104 may include an injector 120 configured to supply one or more fluids from one or more fluid sources (e.g., fluid 1, fluid 2, fluid N, etc.) to a flow tube 122 in accordance with an infusion protocol (e.g., according to one or more infusion parameters, etc.) to deliver to a medical device 128 (e.g., a catheter, etc.). As an example, the infusion system 104 may include a flow sensor 124 (e.g., an ultrasonic mass flow sensor, such as those manufactured by Transonic Systems, Inc., etc.) configured to measure the flow rate and / or volume of a fluid flow. In such an example, the flow sensor 124 may be configured to directly measure the flow rate and / or volume of the fluid flowing through the flow tube 122 (e.g., the total volume delivered for infusion, etc.). The flow sensor 124 can measure the flow rate and / or volume of fluid flow in the flow tube 122 controlled and / or supplied by an injector 120 (e.g., a motor-powered pump) such as a positive displacement pump, non-positive displacement pump, semi-positive displacement pump, reciprocating pump, piston pump, vane pump, flexible member pump, lobe pump, gear pump, circumferential piston pump, screw pump, centrifugal pump, turbine pump, impeller pump, and / or similar. For example, the flow sensor 124 may be attached or mounted to the outer surface of the flow tube 122 (e.g., via a clip, adhesive, etc.).

[0056] Referring further to Figure 1B, in some non-limiting embodiments or aspects, the flow sensor 124 provides a real-time feedback signal via a feedback control loop between the flow sensor 124 and the injector 120. For example, the real-time feedback signal may include real-time measurements of the flow rate and / or volume of the fluid flow in the flow tube 122. As an example, the injector 120 may be programmed or configured to control the injection protocol based on the real-time feedback signal from the flow sensor 124 (e.g., stopping the injection, adjusting injection control parameters, controlling the delivery of fluid from one or more fluid sources to the flow tube 122). In some non-limiting embodiments or aspects, the injection system 104 (e.g., the flow sensor 124) may be adjusted or calibrated for more accurate measurement of the flow rate and / or volume of the fluid in a particular flow tube 122 (e.g., a particular set of disposable tubes) (e.g., including one or more operating parameters).

[0057] In some non-limiting embodiments or embodiments, the injection system 104 includes an air sensor 126 configured to detect air or gas in the fluid flow. For example, the air sensor 126 may be configured to directly measure the amount of air or gas in the fluid flowing through the flow tube 122. In some non-limiting embodiments or embodiments, the air sensor 126 provides a real-time feedback signal via a feedback control loop between the air sensor 126 and the injector 120. For example, the real-time feedback signal may include a real-time measurement of the amount of gas in the fluid flowing through the flow tube 122. As an example, the injector 120 may be programmed or configured to control the injection protocol based on the real-time feedback signal from the air sensor 126 (e.g., adjusting injection control parameters, controlling fluid delivery to the flow tube 122, etc.).

[0058] Figure 1B shows a single injector 120 (e.g., a single pump) that controls the delivery of fluid from multiple fluid sources 1, 2, ... N, etc. to a single flow tube 122, with a single flow sensor 124 (and / or a single air sensor 126) supplying a single real-time feedback signal to the single injector 120. However, non-limiting embodiments or aspects are not limited thereto, and the infusion system 104 may include each injector 120 (and / or one or more respective control valves, etc.) that controls the delivery of fluid to each respective flow tube 122 for each respective fluid source 1, 2, ... N, etc., with each flow sensor 124 (and / or each air sensor 126) supplying their respective real-time feedback signals to each injector 120. For example, each of the flow tubes 122 can be combined after each flow sensor 124 to deliver the combined fluid flow from each of the fluid sources 1, 2, ... N to a medical device 128.

[0059] In some non-limiting embodiments or aspects, the injection system 104 includes one or more sensors for measuring one or more operating parameters related to the cleanliness of the injection system 104. For example, dirt and / or fluids (e.g., contrast agents) may be undesirably transferred to one or more components or devices of the injection system 104 by the hands of a user or operator and / or one or more droplets or leaks. As an example, referring to Figure 1C, the injector 120 can be housed within the housing 150 and may include one or more ports 152 (e.g., syringe ports) for connecting the proximal end of one or more fluid sources 1, 2, ... N (e.g., syringe 154 containing fluid 1, fluid 2, etc.) and for connecting the plunger 156 to its respective piston element. The syringe ports 152 are generally located at one end of the housing 150, as shown, for example, in Figure 1C. In some non-limiting embodiments or aspects, the syringe 154 may include at least one barcode (BC) containing information regarding the dimensions, volume, and pressure tolerance of the syringe, and / or information regarding the fluid contained within the syringe 154. The at least one barcode (BC) may be read by an optical sensor 158 located on or recessed in the end of the housing 150 or on at least a portion of the inner surface of at least one syringe port 154 of the injector 120.

[0060] In some non-limiting embodiments or aspects, the injection system 104 determines a cleanliness rating of the injection system 104 and / or the optical sensor 158 based on one or more scans of at least one barcode (BC) by the optical sensor 158. For example, the injection system 104 may determine a numerical cleanliness rating based on the number of scans performed before the successful scanning of at least one barcode (BC) (e.g., more scans performed before the successful scanning may indicate lower cleanliness and lower the cleanliness rating, etc.) and / or based on the percentage of the optical sensor 158's field of view that is obscured (e.g., a field of view with a higher percentage of obscuration may indicate a lower cleanliness rating and lower the cleanliness rating by the percentage of obscuration, etc.). In some non-limiting embodiments or aspects, the injection system 104 may determine a wear rating of the injection system 104 and / or the optical sensor 158 in response to maintenance actions indicating that the optical sensor 158 has been cleaned. For example, the injection system 104 can determine that the cause of the cleanliness evaluation is more persistent malfunctions (e.g., scratches on the lens of the optical sensor 158) than less persistent malfunctions (e.g., dirt or fluid on the lens of the optical sensor 158) that can be repaired or removed by the cleaning action.

[0061] Referring to several non-limiting embodiments or aspects, the infusion system 104 may include an image capture device 140, such as a digital camera and / or similar, positioned to have a single field of view including one or more components or devices of the infusion system 104, for capturing images of one or more of one or more components or devices of the infusion system 104. For example, referring to Figure 1B, the infusion system 104 may include an image capture device 140 positioned to have a single field of view (F) including each of the respective fluid sources 1, 2, ... N, an injector 120, a flow tube 122, and / or a medical device 128. As an example, referring to Figure 1C, the injection system 104 may include an image capture device 140 positioned to have a first field of view (F1) including the housing 150, the syringe 154, and / or the medical device 128, and / or a second field of view (F2) including one or more specific components or component connections of the injection system 104 (e.g., port 152, the connection between port 152 and syringe 154, etc.). In such an example, the image capture device 140 may acquire one or more images of the field of view, and the injection system 104 may analyze one or more of the acquired images using image processing techniques such as pattern recognition algorithms and / or similar to identify the proportion of the injection system 104 (or one or more of its components) that is contaminated with undesirable contaminants (e.g., dirt, spilled contrast agent, etc.), and / or identify leaks or cracks in one or more components or component connections of the injection system 104. For example, the injection system 104 may use Insight Explorer imaging processing software provided by Cognex Corporation in Natick, Massachusetts, and the image capture device 140 may be a DataMan 100 camera provided by Cognex Corporation to identify the percentage of the injection system 104 contaminated with undesirable contaminants and / or to identify leaks or cracks in one or more components or component connections of the injection system 104.In such an example, the injection system 104 can compare the features of the current image with those of a training image or a previous image to identify new or increased contaminant coverage and / or leaks or cracks in one or more components or component connections of the injection system 104.

[0062] In some non-limiting embodiments or aspects, the image capture device 140 may be further configured to read at least one barcode (BC). For example, referring again to Figure 1B, the fluid sources 1, 2, ... N may include at least one barcode (BC) containing information about the dimensions, volume, pressure tolerance of the fluid sources and / or information about the fluids contained in the fluid sources 1, 2, ... N. At least one barcode (BC) may be read by the image capture device 140, for example, before, during, and / or after connecting the fluid sources 1, 2, ... N to the injector 120. In such an example, the injection system 104 can determine a numerical cleanliness rating based on the number of scans performed before the successful scanning of at least one barcode (BC) (for example, more scans performed before the successful scanning may indicate a lower cleanliness rating, and / or based on the percentage of the field of view of the obscured image capture device 140 (for example, a field of view with a higher percentage of obscurity may indicate a lower cleanliness rating, and the cleanliness rating may be reduced by the percentage of obscurity).

[0063] In some non-limiting embodiments or aspects, referring to Figure 1C, the injection system 104 includes a force sensor 160 (e.g., a motor current sensor, strain gauge, etc.) in an injector 120 configured to measure the force associated with moving a plunger 156 to deliver fluid from the syringe 154. For example, the accumulation of contrast agent and / or fouling within the components of the injector 120 may increase the force required to move the plunger 156 to deliver fluid from the syringe 154. As an example, the injection system 104 may determine a numerical cleanliness rating based on the force measured by the force sensor 160 required to move the plunger 156 to deliver fluid from the syringe 154 for one or more injections (e.g., a larger measured force may indicate a lower cleanliness rating, or a lower cleanliness rating). In such an example, the injection system 104 may determine a change in the numerical cleanliness rating as a percentage difference of the force measurements by comparing the force measurement of the current or recent injection with the force measurement of a previous injection or a calibration measurement.

[0064] In some non-limiting embodiments or configurations, referring to Figure 1B, the infusion system 104 includes a force sensor 160 (e.g., a motor current sensor, strain gauge, etc.) in the injector 120, configured to measure the force associated with pumping fluid from fluid sources 1, 2, ... N using the injector 120. For example, the accumulation of contrast agent and / or contaminants within the components of the injector 120 increases the force required to pump fluid from fluid sources 1, 2, ... N using the injector 120. As an example, the infusion system 104 can determine a numerical cleanliness rating based on the force measured by the force sensor 160 required to deliver fluid from fluid sources 1, 2, ... N to the medical device 128 using the injector 120 for one or more infusions (e.g., a larger measured force may indicate a lower cleanliness rating, or a lower cleanliness rating may be indicated). In such an example, the injection system 104 can compare the current or recent injection force measurement with a previous injection force measurement or calibration measurement to determine the change in the numerical cleanliness rating as the difference in the percentage of force measurements.

[0065] In some non-limiting embodiments or aspects, the remote system 106 may include one or more devices capable of receiving data and / or information (e.g., operational data, maintenance data, etc.) from the maintenance prediction system 102 and / or the injection system 104 via the network 108 and / or communicating data and / or information (e.g., operational data, maintenance data, etc.) to the maintenance prediction system 102 and / or the injection system 104 via the network 108. For example, the remote system 106 may include computer devices such as a server, a group of servers, and / or other similar devices. In some non-limiting embodiments or aspects, the remote system 106 may be implemented by or instead of the original equipment manufacturer OEM of the injection system 104 (e.g., an OEM of one or more components or devices of the injection system 104), the provider of the injection system 104, the imaging site or hospital containing the injection system 104, an inspection technician assigned to the injection system 104, and / or the like.

[0066] Network 108 may include one or more wired and / or wireless networks. For example, Network 108 could include cellular networks (e.g., Long-Term Evolution (LTE) networks, third-generation (3G) networks, fourth-generation (4G) networks, code division multiple access (CDMA) networks, etc.), public land mobile networks (PLMN), local area networks (LANs), wide area networks (WANs), metropolitan area networks (MANs), telephone networks (e.g., public switched telephone networks (PSTNs)), private networks, ad-hoc networks, intranets, the Internet, fiber optic-based networks, cloud computing networks, short-range wireless communication networks (e.g., Bluetooth® networks, near-field communication (NFC) networks, etc.), and / or similar, and / or combinations of these or other types of networks.

[0067] The number and arrangement of systems, devices, and networks shown in Figures 1A to 1C are provided as examples. There may be additional systems, devices, and / or networks beyond those shown in Figures 1A to 1C, fewer systems, devices, and / or networks than those shown in Figures 1A to 1C, different systems, devices, and / or networks than those shown in Figures 1A to 1C, or systems, devices, and / or networks arranged differently from those shown in Figures 1A to 1C. Furthermore, two or more systems or devices shown in Figures 1A to 1C may be implemented within a single system or a single device, or a single system or a single device shown in Figures 1A to 1C may be implemented as multiple distributed systems or devices. In addition to or instead of this, a set of systems or devices in environment 100 (e.g., one or more systems, one or more devices) may perform one or more functions described as being performed by other sets of systems or devices in environment 100.

[0068] Referring now to Figure 2, which is a diagram of an example of the components of device 200. Device 200 may correspond to one or more devices and / or one or more systems of the maintenance prediction system 102, one or more devices and / or one or more systems of the injection system 104, and / or one or more devices and / or one or more systems of the remote system 106. In some non-limiting embodiments or aspects, the maintenance prediction system 102, the injection system 104, and / or the remote system 106 may include at least one device 200 and / or at least one component of device 200. As shown in Figure 2, device 200 may include a bus 202, a processor 204, memory 206, a storage component 208, an input component 210, an output component 212, and a communication interface 214.

[0069] Bus 202 may include components that enable communication between components of device 200. In some non-limiting embodiments or aspects, the processor 204 may be implemented in hardware, firmware, or a combination of hardware and software. For example, the processor 204 may be a processor (e.g., a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), etc.), a microprocessor, a digital signal processor (DSP), and / or any processing component that can be programmed to perform a function (e.g., a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), etc.). The memory 206 may be random access memory (RAM), read-only memory (ROM), and / or other types of dynamic or static storage devices that store information and / or instructions for use by the processor 204 (e.g., flash memory, magnetic memory, optical memory, etc.).

[0070] The storage component 208 may store information and / or software related to the operation and use of device 200. For example, the storage component 208, along with a corresponding drive, may be a hard disk (e.g., magnetic disk, optical disk, magneto-optical disk, solid-state disk, etc.), a compact disk (CD), a digital multipurpose disk (DVD), a floppy disk, a cartridge, a magnetic tape, and / or other types of computer-readable media.

[0071] The input component 210 may include components that enable the device 200 to receive information, for example, through user input (e.g., a touchscreen display, keyboard, keypad, mouse, button, switch, microphone, etc.). In addition to or instead of this, the input component 210 may include sensors for sensing information (e.g., a global positioning system (GPS) component, accelerometer, gyroscope, actuator, etc.). The output component 212 may include components that provide output information from the device 200 (e.g., a display, speaker, one or more light-emitting diodes (LEDs), etc.).

[0072] The communication interface 214 may include components such as transceivers (e.g., transceivers, separate receivers and transmitters) that enable device 200 to communicate with other devices via, for example, a wired connection, a wireless connection, or a combination of wired and wireless connections. The communication interface 214 may also enable device 200 to receive information from and / or provide information to other devices. Examples of communication interfaces 214 include Ethernet® interfaces, optical interfaces, coaxial interfaces, infrared interfaces, radio frequency (RF) interfaces, Universal Serial Bus (USB) interfaces, Wi-Fi® interfaces, cellular network interfaces, and / or similar.

[0073] Device 200 may perform one or more processes as described herein. Device 200 may perform these processes based on a processor 204 that executes software instructions stored in a computer-readable medium such as memory 206 and / or storage component 208. A computer-readable medium (e.g., a non-temporary computer-readable medium) is defined herein as a non-temporary memory device. A memory device includes a memory space located within a single physical storage device or a memory space spanning multiple physical storage devices.

[0074] Software instructions may be read into memory 206 and / or storage component 208 from other computer-readable media or other devices via the communication interface 214. When the software instructions stored in memory 206 and / or storage component 208 are executed, they may cause the processor 204 to execute one or more processes described herein. In addition to or instead of this, hardwired circuits may be used in place of or in combination with software instructions to execute one or more processes described herein. Therefore, the embodiments or aspects described herein are not limited to any particular combination of hardware circuits and software.

[0075] The number and arrangement of components shown in Figure 2 are provided as an example. In some non-limiting embodiments or configurations, device 200 may include additional components to those shown in Figure 2, fewer components than those shown in Figure 2, components different from those shown in Figure 2, or components arranged differently from those shown in Figure 2. In addition to or instead of this, a set of components of device 200 (e.g., one or more components) may perform one or more functions described as being performed by other sets of components of device 200.

[0076] Referring now to Figure 3, Figure 3 is a flowchart of a non-limiting embodiment or aspect of process 300 for predictive maintenance of an injection system. In some non-limiting embodiments or aspects, one or more steps of process 300 are performed (e.g., entirely, partially, etc.) by the maintenance prediction system 102 (e.g., one or more devices of the maintenance prediction system 102). In some non-limiting embodiments or aspects, one or more steps of process 300 are performed (e.g., entirely, partially, etc.) by other devices or groups of devices separate from or including the maintenance prediction system 102, such as the injection system 104 (e.g., one or more devices of the injection system 104) and / or the remote system 106 (e.g., one or more devices of the remote system 106).

[0077] As shown in Figure 3, in step 302, process 300 includes receiving operational data associated with one or more injection systems. For example, maintenance prediction system 102 receives operational data associated with injection system 104. As an example, the maintenance prediction system 102 can receive operational data related to the infusion system 104 from the infusion system 104, the remote system 106, and / or one or more other devices related to surgery, medical procedures, patients, and / or users or operators associated with the infusion system 104 (e.g., imaging devices or scanners such as power supply systems, computed tomography (CT) systems, magnetic resonance imaging (MRI) systems, and / or similar; patient devices such as patient identification devices containing patient identifiers and / or patient information (e.g., wearable RFID tags and / or computer devices); heart rate monitors, and / or similar; user or operator devices such as user or operator identification devices containing user or operator identifiers and / or user or operator information (e.g., wearable RFID tags and / or computer devices); and / or similar). In such examples, the infusion system 104, the remote system 106, and / or one or more other medical devices may include one or more sensors, one or more input components, one or more output components, and / or similar, that can receive, determine, measure, sense, and / or supply operational data related to functions, medical procedures, patients, and / or users or operators associated with the infusion system 104 (e.g., functions, medical procedures, patients, and / or users or operators associated with one or more operations of the infusion system 104). In some non-limiting embodiments or aspects, the maintenance prediction system 102 receives operational data related to the infusion system 104 continuously, periodically, automatically, and / or similarly, in response to user requests for predictive maintenance analysis, diagnostic operations, and / or benchmark inspections to be performed in connection with the infusion system 104, in response to startup operations and / or other operations performed by the infusion system 104.

[0078] In some non-limiting embodiments or aspects, the operation data includes one or more operation parameters associated with one or more operations of one or more injection systems. For example, the operation data may include one or more operation parameters associated with one or more operations of injection system 104. As an example, the operation of an injection system may include the following operations or functions, namely, software operations or functions (e.g., receiving, installing, modifying, updating, starting, running, and / or monitoring one or more software applications), hardware operations or functions (e.g., receiving, installing, modifying, updating, powering, operating, and / or monitoring one or more hardware components or devices such as microprocessors, memory, storage components, input components, output components, circuit boards, sensors, pumps, valves, and / or similar), and mechanical operations or functions (e.g., flow The operation may include at least one of the following: receiving, supplying, changing, and / or monitoring a fluid flow, and / or delivering fluid to a medical device and / or patient; electrical operation or function (e.g., receiving, supplying, consuming, and / or monitoring power); user interface operation or function (e.g., providing a user interface via output component 212, receiving user input via input component 210); communication operation or function (e.g., receiving, changing, updating, storing, supplying, and / or monitoring operation data and / or maintenance data); or any combination thereof. In such an example, the operation parameters may include parameters that are received, determined, measured, sensed, and / or provided in relation to the function, medical procedure, patient, and / or a user or operator associated with the infusion system 104 (e.g., a user or operator associated with one or more operations of the infusion system 104).

[0079] In some non-limiting embodiments or aspects, the operating parameters are the following parameters related to the injection system (e.g., at least one of the following parameters related to one or more components or devices of the injection system): namely, the flow rate during one or more injections (e.g., maximum, minimum, average, total), the flow rate programmed to be achieved over one or more injections, the amount pumped and / or delivered during one or more injections (e.g., maximum, minimum, average, total), the amount programmed to be delivered during one or more injections, the duration of one or more injections (e.g., maximum, minimum, average, total), the difference between the flow rate during one or more injections and the programmed flow rate of one or more injections (e.g., set by injection parameters of the injection protocol), the difference between the amount pumped and / or delivered during one or more injections and the amount programmed to be pumped and / or delivered during one or more injections (e.g., set by injection parameters of the injection protocol), the number of injections performed, the pressure achieved by one or more injections (e.g., maximum, minimum, average, total), the pressure achieved by one or more injections and one or more The difference between the pressure programmed to be achieved during injection (e.g., set by injection parameters of the injection protocol), the pressure limit or threshold beyond which the injection system is programmed to stop injection delivery, the duration of power-on (maximum, minimum, average, total, etc.), the number of power cycles, energy consumption (e.g., maximum, minimum, average, total, etc.), linear energy supplied or used (e.g., integral of ((pressure)*(flow rate)) / (time), nonlinear energy supplied or used (e.g., integral of f(pressure)*(time)), Voltage (e.g., maximum, minimum, average, total, etc.), resistance, current, noise or signal level, mechanical force, and / or similar (e.g., maximum, minimum, average, total, etc.) generated by the injection system's motor, camera reading count, presence or operability of communication with one or more other systems or devices, number and / or type of error codes received, number, duration, and / or type of user interface keys activated (e.g., pressed, etc.), power line status, temperature and / or humidity within the injection system (e.g., maximum, minimum, average, total, etc.),Temperature and / or humidity of the environment surrounding the injection system (e.g., maximum, minimum, average, total), vibration frequency and / or amplitude (e.g., maximum, minimum, average, total), movement exceeding threshold movement (e.g., measured by an accelerometer, etc.), number of washes, staff evaluation of wear (e.g., numerical evaluation), staff evaluation of cleanliness (e.g., numerical evaluation), inspection records (e.g., number of inspections performed, type of inspection performed, etc.), system evaluation of wear (e.g., numerical evaluation), system evaluation of cleanliness (e.g., numerical evaluation), number of disposable items sold to and / or used by the relevant customer (e.g., syringes, transfer sets, etc.), contrast agent used. This may include at least one of the following: the quantity of the contrast agent used, the type of contrast agent used, the vial size of the contrast agent used, the number of injection systems at the imaging site including the injection system, the user or operator turnover rate associated with the imaging site including the injection system, the identifier of a user or operator associated with the operation or use of one or more injection systems, the identifier of a customer associated with the imaging site including the injection system, the indication of the liquid in the injection system (e.g., detected or measured by one or more liquid sensors), the amount of X-ray radiation, RF exposure, magnetic field exposure, and / or similar in the environment surrounding the injection system (e.g., maximum, minimum, average, total, etc.), one or more injection protocols used for one or more injections, and / or similar.

[0080] In several non-limiting embodiments or aspects, the operational data relating to the injection system 104 is described in U.S. Patent Application No. 10 / 143,562, filed on 10 May 2002 and published as U.S. Patent No. 7,457,804; U.S. Patent Application No. 12 / 254,318, filed on 20 October 2008 and published as U.S. Patent No. 7,996,381; U.S. Patent Application No. 13 / 180,175, filed on 11 July 2011 and published as U.S. Patent No. 8,521,716; and International Patent Application No. 13 / 180,175, filed on 11 August 2016. This application includes one or more typical data types, information types, and / or parameters disclosed in International Patent Application Publication WO2017 / 027724A1, filed as PCT / US2016 / 046587 and published on 16 February 2017, and International Patent Application Publication WO2017 / 040152A1, filed as PCT / US2016 / 048441 on 24 August 2016 and published on 9 March 2017, the disclosures of each of these patent applications incorporated into this application in their entirety by reference.

[0081] As further shown in Figure 3, in step 304, process 300 includes determining one or more predictive scores for one or more injection systems based on operational data. For example, maintenance prediction system 102 determines one or more predictive scores for injection system 104 based on operational data. As an example, maintenance prediction system 102 determines one or more predictive scores for injection system 104 based on operational data, continuously, periodically, automatically, and / or in accordance with startup operations and / or other operations performed by injection system 104, in response to user requests regarding predictive maintenance analysis, diagnostic operations, and / or benchmark inspections to be performed in connection with injection system 104.

[0082] In some non-limiting embodiments or aspects, one or more prediction scores include one or more predictions of one or more malfunctions or misuses for the injection system 104. For example, the prediction scores include a representation of a malfunction or misuse occurring with respect to the injection system 104 (e.g., score, number, ranking, probability, likelihood, etc.). As an example, a malfunction or misuse may include a malfunction or misuse of the injection system 104 (e.g., a malfunction or misuse of one or more operations of the injection system 104, a malfunction or misuse of one or more devices of the injection system 104 and / or one or more components of one or more devices). In such an example, a malfunction of the injection system 104 may include at least one of a software malfunction, hardware malfunction, component or device malfunction, and / or the like, that causes the injection system 104 to operate against one or more predetermined operating thresholds. For example, a malfunction may require inspection, repair, and / or replacement of the software, hardware, component or device, and / or the like that is affected by the malfunction in order for the injection system 104 to operate in an appropriate manner. In such an example, misuse of the injection system 104 may include at least one of the following actions by a user or operator of the injection system 104 that causes the injection system 104 to operate in violation of one or more predetermined operating thresholds: input to the injection system 104, configuration of the injection system 104, operation of the injection system 104, and / or similar actions.

[0083] In some non-limiting embodiments or aspects, the maintenance prediction system 102 determines one or more prediction scores based on one or more machine learning techniques (e.g., pattern recognition techniques, data mining techniques, heuristic techniques, supervised learning techniques, unsupervised learning techniques, etc.). For example, the maintenance prediction system 102 generates one or more models (e.g., estimators, classifiers, predictive models, malfunction or misuse predictive models, maintenance predictive models, etc.) based on one or more machine learning algorithms (e.g., decision tree algorithms, gradient boosted decision tree algorithms, neural network algorithms, convolutional neural network algorithms, random forest algorithms, etc.). In such an example, the maintenance prediction system 102 generates one or more prediction scores using one or more models.

[0084] In some non-limiting embodiments or aspects, one or more predictive models are designed to receive operational data related to the injection system 104 as input and to provide as output predictions (e.g., probabilities, binary output, percentage, yes-no output, score, predictive score, etc.) regarding one or more malfunctions or misuses of the injection system 104. For example, the maintenance prediction system 102 generates one or more predictive models for predicting one or more malfunctions or misuses of one or more injection systems. As an example, the maintenance prediction system 102 can generate one or more predictive models to determine one or more predictive scores, including predictions of whether one or more malfunctions or misuses of one or more injection systems (e.g., one or more components or devices of one or more injection systems) will occur within a predetermined period and / or within a predetermined number of uses of one or more injection systems.

[0085] In some non-limiting embodiments or embodiments, the maintenance prediction system 102 stores one or more prediction models (e.g., one or more models for later use). In some non-limiting embodiments or embodiments, the maintenance prediction system 102 stores one or more prediction models in a data structure (e.g., a database, a linked list, a tree, etc.). In some non-limiting embodiments or embodiments, the data structure is located within or outside the maintenance prediction system 102 (e.g., at a location away from the maintenance prediction system 102).

[0086] In some non-limiting embodiments or aspects, the maintenance prediction system 102 processes operational data (e.g., operational data related to an injection system 104, operational data related to multiple injection systems 104, etc.) to obtain training data for one or more models. For example, the maintenance prediction system 102 processes operational data to change it into a format that can be analyzed (e.g., by the maintenance prediction system 102) and generates one or more models. The modified operational data is referred to as training data. In some implementations, the maintenance prediction system 102 processes operational data to obtain training data based on the receipt of operational data. In addition to or instead of this, the maintenance prediction system 102 processes operational data to obtain training data based on the system 102 receiving instructions from its user that the system 102 should process operational data, such as when the maintenance prediction system 102 receives instructions that the system 102 should create a model.

[0087] In some non-limiting embodiments or aspects, the maintenance prediction system 102 processes operational data by determining one or more variables based on the operational data. In some non-limiting embodiments or aspects, the variables include indicators related to operational failures or misuse of the injection system 104, which may be derived based on the operational data. The variables are analyzed to generate a model. For example, the variables include one or more operational parameters of the injection system 104 and variables related thereto.

[0088] In some non-limiting embodiments or aspects, the maintenance prediction system 102 analyzes training data to generate a model (e.g., one or more prediction models). For example, the maintenance prediction system 102 uses machine learning techniques to analyze training data and generate a model. In some implementations, generating a model (e.g., based on training data obtained from behavioral data, based on training data obtained from existing behavioral data, etc.) is referred to as training the model. Machine learning techniques include supervised and / or unsupervised techniques such as decision trees (e.g., gradient-boosted decision trees), logistic regression, artificial neural networks (e.g., convolutional neural networks), Bayesian statistics, learning automata, hidden Markov modeling, linear classifiers, quadratic classifiers, correlation rule learning, random forests, and / or similar. In some non-limiting embodiments or aspects, the model includes a prediction model specific to a particular injection system 104, a particular set of injection systems 104, a particular set of behavioral parameters, a particular set of behavioral parameters, a particular set of behavioral failures and / or misuses, a particular set of behavioral failures and / or misuses, and / or similar. In addition to or instead of this, the predictive model is specific to a particular user or operator (e.g., a specific user or operator of the injection system 104, a specific imaging site including the injection system 104, a specific customer operating the injection system 104, etc.).

[0089] In addition to or instead of this, when analyzing the training data, the maintenance prediction system 102 identifies one or more variables (e.g., one or more independent variables) as predictor variables used to make predictions (e.g., when analyzing the training data). In some implementations, the values ​​of the predictor variables are inputs to the model. For example, the maintenance prediction system 102 identifies a subset of variables (e.g., a suitable subset) as predictor variables used to accurately predict one or more malfunctions or misuses for the injection system. In some implementations, the predictor variables include one or more of the aforementioned variables (e.g., one or more operating parameters) that have a significant influence on the probability that a malfunction or misuse will occur with respect to the injection system within a given period or within a given number of uses of the injection system.

[0090] In some non-limiting embodiments or aspects, the maintenance prediction system 102 validates the model. For example, the maintenance prediction system 102 validates the model after it has generated it. In some implementations, the maintenance prediction system 102 validates the model based on a portion of the training data that is to be used for validation. For example, the maintenance prediction system 102 divides the training data into a first portion and a second portion, in which case the first portion is used to generate the model as described above. In this example, the second portion of the training data (e.g., validation data) is used to validate the model. In some non-limiting embodiments or aspects, the first portion of the training data is different from the second portion of the training data.

[0091] In some non-limiting embodiments or aspects, the maintenance prediction system 102 provides validation data, including operational data, which includes multiple operational parameters of multiple injection systems, as input to the model, and validates the model by determining, based on the output of the prediction model, whether the prediction model accurately or inaccurately predicted one or more operational failures or misuses of the multiple injection systems. In some implementations, the maintenance prediction system 102 validates the model based on a validation threshold (e.g., a threshold for validation data). For example, the maintenance prediction system 102 is configured to validate the model when the model accurately predicts operational failures or misuses of injection systems (e.g., operational failures or misuses within a predetermined period or within a predetermined number of uses of the injection system) (e.g., when the prediction model accurately predicts 50% of the validation data, or when the prediction model accurately predicts 70% of the validation data). In some non-limiting embodiments or aspects, if the maintenance prediction system 102 does not validate the model (e.g., when the percentage of validation data does not meet the validation threshold), the maintenance prediction system 102 generates an additional prediction model.

[0092] In some non-limiting embodiments or aspects, if one or more models have been validated, the maintenance prediction system 102 further trains one or more models and / or creates new models based on the receipt of new training data. In some non-limiting embodiments or aspects, the new training data includes operational data related to multiple injection systems 104 that are different from the previous multiple injection systems 104 that have already been used to train one or more models.

[0093] In some non-limiting embodiments or aspects, predictions from multiple machine learning algorithms may be combined to construct an ensemble of algorithms or predictive models to enhance predictive power. In such examples, the maintenance prediction system 102 selects machine learning algorithms or models from multiple machine learning algorithms or models based on their ability to predict the training data. For example, the maintenance prediction system 102 may compare the accuracy of various models when predicting the training data. In such examples, the maintenance prediction system 102 may construct an ensemble model by averaging the predictions of the selected models, comparing the plot with the sum of squared residuals of the individual models, and selecting an ensemble model or individual models based on the comparison.

[0094] In some non-limiting embodiments or aspects, the maintenance prediction system 102 determines one or more prediction scores based on one or more measurements of flow rate and volume delivered by the injection system 104. For example, the maintenance prediction system 102 receives operational data including flow rate measurements of one or more injections using the injection system 104 (e.g., pump type-independent measurements including non-positive displacement pumps) and total volume measurements delivered by one or more injections using the injection system 104 (e.g., measurements from the flow sensor 124 of the injection system 104 used in a feedback control loop between the flow sensor 124 and the pump 120), and can apply one or more models and / or equations or formulas (e.g., a cube root lifetime equation) to the operational data to determine a prediction score for predicting the remaining service time and / or number of uses of the injection system 104 until malfunction or misuse. In such an example, the maintenance prediction system 102 can apply a cube root lifetime equation in which the average load can be replaced by the maximum achieved pressure, and can also use the injection volume and flow rate to establish the load duration and determine the prediction score.

[0095] In some non-limiting embodiments or aspects, the maintenance prediction system 102 uses a pattern prediction model to determine one or more prediction scores. For example, the maintenance prediction system 102 may receive operational data including operational parameters associated with objective factors of injection system wear (e.g., one or more temperatures over one or more periods in and / or the environment surrounding the injection system 104, one or more vibrations over one or more periods in the injection system 104, etc.) and / or operational parameters associated with subjective factors of injection system wear (e.g., evaluations related to staff handling of the injection system 104, etc.), input this operational data into a pattern prediction model, and receive prediction scores as output for predicting the remaining usage time and / or remaining number of uses of the injection system 104 until malfunction or misuse. As an example, the maintenance prediction system 102 may alert hospital management when a non-random pattern is recognized in the inspection history of the injection system 104. In such an example, the pattern recognition system can be linked to hospital staff records to identify the user or operator (e.g., hospital staff, etc.) on duty when a problem is reported. In some non-limiting embodiments or configurations, the maintenance prediction system 102 may automatically recalibrate and / or adjust the duty cycle or other operation of the injection system 104 based on a prediction score in an attempt to mitigate predicted operational failures or misuse.

[0096] In some non-limiting embodiments or aspects, the maintenance prediction system 102 determines one or more prediction scores for the injection system 104 based on one or more comparisons of operational data related to the injection system 104 with operational data related to a plurality of other injection systems. For example, the maintenance prediction system can collect operational data from a plurality of injection systems 104 (e.g., from injection systems in the same location, from injection systems in the same imaging site, from injection systems in different remote locations, from injection systems in different imaging sites, etc.) and compare the operational data of one or more of the plurality of injection systems 104 with the operational data of one or more other injection systems among the plurality of injection systems 104. As an example, the maintenance prediction system 102 can provide as a prediction score a comparison (e.g., numerical difference) between operational data (e.g., inspection records, number of times the injection system 104 has been inspected, etc.) of one or more injection systems (e.g., a global database of injection systems in imaging sites operated by one or more other customers, etc.) and operational data (e.g., numerical difference). In such an example, the maintenance prediction system 102 can determine whether one or more of a customer's injection systems are experiencing a substantially different number of malfunctions or misuses (e.g., satisfying a threshold difference, etc.) compared to the number of malfunctions or misuses experienced by one or more other injection systems (e.g., with respect to other customers and / or other imaging sites of the same customer). For example, the maintenance prediction system 102 can use inspection benchmarks to show how an injection system 104 associated with a customer or hospital compares to injection systems associated with other customers or hospitals (e.g., how the operation and / or use of injection system 104 compares to other injection systems). In such an example, the maintenance prediction system 102 can determine cost savings associated with performing one or more maintenance actions for and / or using injection system 104.For example, the maintenance prediction system 102 can determine cost savings as the cost of the amount of contrast agent saved by reducing the waste of contrast agent due to performing one or more maintenance actions compared to one or more other injection systems.

[0097] In some non-limiting embodiments or aspects, the maintenance prediction system 102 determines one or more prediction scores based on the energy consumption of one or more injection systems. For example, the maintenance prediction system 102 may, as input, apply the energy consumption of injection system 104 to one or more prediction models and / or one or more lookup tables, formulas, and / or thresholds, and as output, receive prediction scores for predicting the remaining service life and / or number of uses of injection system 104 until malfunction or misuse. For example, the maintenance prediction system 102 may automatically recalibrate and / or adjust the duty cycle or other operation of injection system 104 based on the prediction scores in an attempt to mitigate a predicted malfunction or misuse. As an example, the maintenance prediction system 102 may automatically schedule inspection technicians (e.g., dispatching inspection technicians to injection system 104) in response to the energy consumption of injection system 104 that meets a threshold indicating that injection system 104 is reaching the end of its expected service life and / or an imminent failure of injection system 104 is expected.

[0098] In some non-limiting embodiments or aspects, the maintenance prediction system 102 determines one or more prediction scores based on customer-specific usage of one or more infusion systems. For example, the maintenance prediction system 102 may receive usage-based operating parameters (e.g., the number of one or more disposable items used in connection with the infusion system 104 (e.g., syringes, transfer sets, etc.), the amount, type, vial size, and / or similar of contrast agents used in connection with the infusion system 104), local regulations and / or practices related to the infusion system 104 indicating thresholds for one or more usage-based operating parameters, an indication of lack of contrast agent warming, the number of infusion systems related to the customer, the user or operator turnover rate of the infusion system 104, an assessment of the competence of users or operators related to the infusion system 104, and / or similar. For example, the maintenance prediction system 102 may, as input, apply one or more usage-based operating parameters to one or more prediction models and / or one or more lookup tables, formulas, and / or thresholds, and as output, receive prediction scores for predicting the remaining usage time and / or remaining number of uses of the infusion system 104 until malfunction or misuse. In some non-limiting embodiments or aspects, the maintenance prediction system 102 can determine a tailored preventive inspection plan for the customer based on the predicted reliability of the injection system 104.

[0099] In some non-limiting embodiments or aspects, the maintenance prediction system determines one or more prediction scores based on a contrast agent contamination scoring system. For example, the maintenance prediction system 102 may receive one or more evaluations related to the cleanliness of the injection system 104.

[0100] In some non-limiting embodiments or aspects, the injection system 104 may provide prompts (e.g., via a user interface with an output component 212, etc.) that require the user or operator of the injection system 104 to input an evaluation of the cleanliness of the injection system 104 (e.g., on a numerical scale, etc.) (e.g., via a user interface associated with an input component 210). In such examples, the injection system 104 may provide prompts at periodic intervals before, during, and / or after each operation and / or injection process by the injection system 104, in response to user requests for an evaluation of cleanliness and / or similar. In some non-limiting embodiments or aspects, the maintenance prediction system 102 may, as input, apply one or more cleanliness evaluations to one or more prediction models and / or one or more lookup tables, formulas, and / or thresholds, and as output, receive a prediction score for predicting the remaining usage time and / or remaining number of uses of the injection system 104 until malfunction or misuse. For example, the maintenance prediction system 102 can automatically recalibrate and / or adjust the duty cycle or other operation of the injection system 104 based on the prediction score in an attempt to mitigate predicted malfunctions or misuse. As an example, the maintenance prediction system 102 can automatically schedule inspection technicians (e.g., dispatching inspection technicians to the injection system 104) in accordance with the overall cleanliness evaluation of the injection system 104 that meets a threshold. In some non-limiting embodiments or aspects, the maintenance prediction system 102 can compare users or operators with respect to the cleanliness evaluation of their systems by associating the prediction scores of multiple injection systems 104, determined based on the cleanliness evaluation, with identifiers of the users or operators operating the injection systems.

[0101] In some non-limiting embodiments or aspects, the injection system 104 can determine an evaluation of its cleanliness (e.g., on a numerical scale). For example, the injection system 104 can determine an evaluation of its cleanliness based on one or more scans of at least one barcode (BC) by the optical sensor 158 and / or the image capture device 140, one or more images of the field of view of the image capture device 140, one or more force measurements measured by the force sensor 160, or any combination thereof. In such an example, the injection system 104 can capture scans, images, and / or force measurements at periodic intervals before, during, and / or after each operation and / or injection process by the injection system 104, in response to user requests and / or similar requests for evaluating cleanliness. In some non-limiting embodiments or aspects, the maintenance prediction system 102 may, as input, apply one or more cleanliness ratings to one or more prediction models and / or one or more lookup tables, formulas, and / or thresholds, and as output, receive a prediction score for predicting the remaining usage time and / or number of uses of the injection system 104 until malfunction or misuse. For example, the maintenance prediction system 102 may automatically recalibrate and / or adjust the duty cycle or other operation of the injection system 104 based on the prediction score in an attempt to mitigate a predicted malfunction or misuse. As an example, the maintenance prediction system 102 may automatically schedule an inspection technician (e.g., dispatch an inspection technician to the injection system 104) depending on the total cleanliness rating of the injection system 104 that meets a threshold. In some non-limiting embodiments or aspects, the maintenance prediction system 102 may compare the prediction scores of multiple injection systems 104 determined based on cleanliness ratings with the identifiers of the users or operators operating the injection systems, thereby comparing the users or operators with respect to the cleanliness ratings of those systems.

[0102] As further shown in Figure 3, in step 306, process 300 includes providing maintenance data related to one or more malfunctions or misuses. For example, the maintenance prediction system 102 provides maintenance data related to one or more malfunctions or misuses. As an example, the maintenance prediction system 102 provides maintenance data related to one or more malfunctions or misuse to the user or operator of the injection system 104, to the injection system 104, to the remote system 106 (for example, to a computer system implemented by or on behalf of the original equipment manufacturer OEM of the injection system 104 (e.g., an OEM of one or more components or devices of the injection system 104), to a computer system implemented by or on behalf of the provider of the injection system 104 (e.g., the MEDRAD® Stellant CT Injection System with Certegra® Workstation is provided by Bayer, etc.), to an imaging site, customer, or hospital, or to a computer system implemented on behalf of them), and / or similar entities.

[0103] In some non-limiting embodiments or aspects, the maintenance prediction system 102 provides maintenance data to a user or operator, an injection system, and / or a remote computer system or entity based on a user or operator identifier, an injection system identifier, and / or an identifier of a remote computer system or entity included in the maintenance data and / or operational data used to determine one or more prediction scores on which the maintenance data is based. In some non-limiting embodiments or aspects, the type and / or amount of maintenance data provided is based on the recipient of the maintenance data.

[0104] In some non-limiting embodiments or aspects, maintenance data includes operational data (e.g., one or more operational parameters relating to one or more operations of the injection system 104) and / or data relating to one or more maintenance actions (e.g., prompts prompting a user or operator to perform one or more maintenance actions, commands to cause the injection system 104 to perform one or more maintenance actions, indications that one or more maintenance actions are scheduled to be performed for and / or using the injection system 104, indications that one or more maintenance actions have been performed for and / or using the injection system 104, a list of other injection systems among a plurality of injection systems located at the imaging site including the injection system 104, and one or more maintenance contracts associated with the injection system 104).

[0105] In some non-limiting embodiments or aspects, maintenance data is based on one or more predictive scores. For example, the maintenance prediction system 102 may determine maintenance data based on one or more predictive scores. As an example, the maintenance prediction system 102 may query a lookup table or database that associates one or more maintenance actions with one or more expected malfunctions or misuses of the injection system 104 (e.g., one or more expected malfunctions or misuses of one or more components or devices of the injection system 104) based on one or more predictive scores. In such an example, the maintenance prediction system 102 may retrieve and / or provide maintenance data related to one or more maintenance actions relating to one or more malfunctions or misuses of the injection system 104 that have one or more predictive scores that satisfy one or more threshold scores.

[0106] In some non-limiting embodiments or aspects, maintenance data includes commands to cause the infusion system 104 to automatically perform one or more maintenance actions. For example, maintenance actions include any of the following actions performed automatically using the infusion system 104 (e.g., one or more components or devices of the infusion system 104): providing a prompt (e.g., via the user interface of the output component 212) to prompt the user to perform one or more maintenance actions for the infusion system 104; scheduling an inspection technician to repair, inspect, and / or replace the infusion system 104 (e.g., dispatching an inspection technician to the infusion system 104); automatically ordering one or more disposable items (e.g., syringes, transfer sets, etc.) and / or one or more contrast agents; providing commands (e.g., via the user interface of the output component 212) to the user or operator to avoid specific malfunctions and / or misuse of the infusion system 104 using the infusion system 104 in a particular manner; and providing recommendations (e.g., via the user interface of the output component 212) to improve inspections based on a comparison of the infusion system 104 with one or more other infusion systems. To do so, provide (e.g., via the user interface of the output component 212) the amount used, remaining amount, pressure limit, and / or similar related to the injection system 104, provide (e.g., via the user interface of the output component 212) an inspection plan based on the operating parameters based on the use of the injection system 104, provide (e.g., via the user interface of the output component 212) customized preventive maintenance inspections (e.g., cleaning and calibration of power supplies, motors, etc.) (e.g., via the user interface of the output component 212) recommend training to the user or operator (e.g., via the user interface of the output component 212) restart the software, update the software, transmit operating data and / or warnings to the remote system 106, measure the deterioration, wear, or cleanliness of components, provide remote input to the remote computer system to change and / or update the software and / or one or more operating parameters, disable or restrict one or more operating parameters or functions (e.g.,This includes, for example, disabling the injection by operating parameters that define a flow rate that satisfies a threshold flow rate and / or a pressure that satisfies a threshold pressure, disabling power supply, stopping the injection, restarting the power, prompting the customer to send an inspection request directly from the injection system 104, and / or at least one of the same.

[0107] In some non-limiting embodiments or aspects, the maintenance prediction system 102 determines one or more prediction scores for the injection system 104 and / or provides maintenance data associated with one or more prediction scores continuously, periodically, and automatically in response to startup operations and / or other operations performed by the injection system 104.

[0108] While the present invention has been described in detail for illustrative purposes based on what is considered to be the most practical and preferred embodiment or aspect, it should be understood that such details are for that purpose only, and the invention is not limited to the disclosed embodiment or aspect. Rather, it is intended to cover modifications and equivalent configurations that fall within the technical spirit and scope of the appended claims. For example, it should be understood that the invention anticipates that, to the extent possible, one or more features of any embodiment or aspect can be combined with one or more features of any other embodiment or aspect. [Explanation of Symbols]

[0109] 100 Environment 102 Maintenance Prediction System 104 Injection System 106 Remote Systems 108 Network 120 injectors, pumps 122 Flow Tube 124 Flow Sensor 126 Air Sensor 128 Medical devices 140 Image Capture Devices 150 Housing 152 Syringe port 154 Syringes, syringe ports 156 Plunger 158 Optical Sensors 160 force sensor 200 devices Bus 202 204 Processors 206 memory 208 Memory Components 210 Input Components 212 Output Components 214 Communication Interface

Claims

1. It is a predictive maintenance system, One or more injection systems, including an injector configured to supply one or more fluids from one or more fluid sources, Receiving operational data associated with one or more injection systems, wherein the operational data includes one or more operational parameters related to one or more operations of the one or more injection systems. Determining one or more predictive scores for one or more injection systems based on the operational data, wherein the one or more predictive scores include one or more predictions of one or more operational failures or misuses for one or more injection systems. To provide maintenance data related to one or more malfunctions or misuses, wherein the maintenance data is determined based on one or more predictive scores, A computer system comprising one or more processors programmed or configured to perform the following: Equipped with, The one or more of the aforementioned operating parameters include a mechanical force generated by the motor, and the predictive maintenance system, A predictive maintenance system further comprising one or more force sensors configured to measure the mechanical force generated by the motor to supply the one or more fluids from the one or more fluid sources, wherein the one or more predictive scores include one or more predictions of one or more malfunctions or misuses relating to the one or more injection systems relating to wear, contrast agent and / or fouling accumulation, and the effect of wear, contrast agent and / or fouling accumulation on each of the one or more injection systems is determined by comparing the force of the motor in the current or recent injection operation with the force of the motor in one of the previous injection operations and calibration operations of the motor.

2. The predictive maintenance system according to claim 1, wherein the one or more operations include at least one of the following operations of the one or more injection systems: hardware operation, mechanical operation, electrical operation of one or more hardware components, software operation of one or more software applications, user interface operation, communication operation, or any combination thereof.

3. The predictive maintenance system according to claim 2, wherein the one or more operations include the hardware operations of the one or more hardware components, and the one or more hardware components include at least one of the following hardware components of the one or more injection systems: a microprocessor, memory, storage components, input components, output components, circuit boards, sensors, pumps, valves, or any combination thereof.

4. The predictive maintenance system according to claim 2, wherein the one or more operations include the mechanical operations, and the mechanical operations include supplying the one or more fluids from the one or more fluid sources.

5. The one or more operating parameters exceeding the following operating parameters related to the operation of the one or more injection systems: vibration frequency and / or amplitude, pressure achieved for one or more injections, number of injections performed, resistance, current, voltage, noise or signal level, acceleration of components exceeding a threshold, power line condition, energy consumption, duration at power-on, linear energy supplied or used, nonlinear energy supplied or used, flow rate during one or more injections, difference between the flow rate during one or more injections and the programmed flow rate of one or more injections, amount pumped and / or delivered during one or more injections, difference between the amount pumped and / or delivered during one or more injections and the amount programmed to be pumped and / or delivered during one or more injections, duration of one or more injections, if exceeded, the one or more injection systems stop delivering one or more injections. A predictive maintenance system according to claim 1, comprising at least one of the following: a pressure limit or threshold programmed to; temperature and / or humidity in the one or more injection systems; number of power cycles; number of user interface keys operated; presence of communication with one or more other systems or devices; number and / or type of error codes received; number of scans performed by the image capture device before the image capture device successfully scans the barcode of the fluid source; type of contrast agent used; temperature in the one or more injection systems over one or more periods; temperature and / or humidity of the environment surrounding the one or more injection systems; indication of liquid in the one or more injection systems detected or measured by one or more liquid sensors; identifier of a user or operator; staff rating of wear; staff rating of cleanliness; or any combination thereof.

6. The one or more operating parameters include the vibration frequency and / or amplitude related to the one or more operations of the one or more injection systems, and the predictive maintenance system is A predictive maintenance system according to claim 5, comprising one or more vibration monitoring sensors configured to measure the vibration frequency and / or amplitude relating to the operation of one or more of the one or more injection systems, wherein the one or more predictive scores include one or more predictions of one or more malfunctions or misuses relating to the one or more injection systems relating to wear, contrast agent and / or fouling accumulation, and the effect of wear, contrast agent and / or fouling accumulation on each of the one or more injection systems is determined by applying a pattern prediction model to the vibration frequency and / or amplitude over one or more periods.

7. The predictive maintenance system according to claim 5, wherein the one or more operating parameters include the flow rate during the one or more injections and the amount pumped and / or delivered during the one or more injections, the one or more predictive scores include one or more predictions of one or more malfunctions or misuses relating to the one or more injection systems related to wear, contrast agent and / or fouling accumulation, the effect of wear, contrast agent and / or fouling accumulation on each of the one or more injection systems is determined by applying at least one of the cubic root lifetime equation and one or more other suitable equations to the flow rate during the one or more injections and the amount pumped and / or delivered during the one or more injections, the mean load being replaced by the maximum achieved pressure in the cubic root lifetime equation, and the duration of the load being established using the flow rate during the one or more injections and the amount pumped and / or delivered during the one or more injections to determine the one or more predictive scores.

8. The one or more operating parameters include the number of scans performed by one or more image capture devices before at least one barcode scan is successful, and the predictive maintenance system, The predictive maintenance system according to claim 5, further comprising one or more image capture devices configured to read at least one barcode.

9. The predictive maintenance system according to claim 8, wherein one or more image capture devices are located in or placed on the housing of the injector, or on at least a portion of the inner surface of at least one syringe port of the injector.

10. The system further comprises one or more image capture devices positioned to have a field of view including the one or more components of the one or more injection systems for capturing one or more images of one or more components of the one or more injection systems, and the one or more processors A predictive maintenance system according to claim 1, further programmed or configured to analyze the one or more images using one or more image processing techniques to identify at least one of (i) the proportion of the one or more components covered in undesirable contaminants, and (ii) leaks or cracks in the one or more components, wherein the one or more predictive scores include one or more predictions of one or more malfunctions or misuses of the one or more injection systems relating to the new or increased contaminant coverage and / or leaks or cracks of the one or more components, determined by comparing the current image with a previous image to identify new or increased contaminant coverage and / or leaks or cracks of the one or more components.

11. The predictive maintenance system according to claim 1, wherein the maintenance data includes a prompt that prompts the user to initiate at least one maintenance action related to the one or more injection systems.

12. The predictive maintenance system according to claim 11, wherein the at least one maintenance activity includes at least one of the following activities: scheduling an inspection for the one or more injection systems; operating the one or more injection systems in a particular manner indicated by the maintenance data; requesting an inspection directly from the one or more injection systems; or any combination thereof.

13. The predictive maintenance system according to claim 1, wherein the maintenance data includes an instruction to one or more injection systems that causes one or more injection systems to automatically initiate at least one maintenance action.

14. The predictive maintenance system according to claim 13, wherein the at least one maintenance action includes prompting a user to initiate at least one maintenance action relating to the one or more infusion systems; transmitting operational data and / or warnings to a remote system; providing remote input to a remote computer system to modify and / or update software and / or one or more operational parameters; scheduling an inspection technician; performing a specific action; providing recommendations for improving inspections based on a comparison of the one or more infusion systems with one or more other infusion systems; restarting the software of the one or more infusion systems; updating the software of the one or more infusion systems; providing customized preventive maintenance inspections; providing an inspection plan based on operational parameters based on usage; disabling or limiting one or more actions or functions; restarting the power supply; disabling power; recommending training to a user; measuring the deterioration, wear, or cleanliness of components; providing instructions to avoid specific malfunctions and / or misuse using the one or more infusion systems in a particular manner; ordering one or more disposable items and / or one or more contrast agents; or any combination thereof.

15. At least one processor, The predictive maintenance system according to claim 1, further programmed or configured to automatically disable one or more injection systems from performing an injection operation based on the maintenance data.

16. The predictive maintenance system according to claim 1, further comprising one or more sensors configured to measure one or more operating parameters relating to the operation of one or more of the one or more injection systems, wherein the one or more sensors include at least one of the following sensors: a flow sensor, a temperature sensor, an accelerometer, a vibration monitoring sensor, a strain gauge, a motor current sensor, an image sensor, an air sensor, a force sensor, or any combination thereof.

17. The predictive maintenance system according to claim 1, wherein the one or more operational failures or misuses relating to the one or more injection systems include at least one of the following: failure of an electrical component, failure of a software component, failure of a mechanical component, receiving user input from a user of the one or more injection systems that causes the one or more injection systems to operate in violation of one or more predetermined operating thresholds, or any combination thereof.

18. A computer program for predictive maintenance, wherein the computer program includes program instructions, and when executed by at least one processor, the program instructions are provided to the at least one processor. It receives operational data associated with one or more injection systems, and the operational data includes one or more operational parameters related to one or more operations of the one or more injection systems. The program instruction causes the at least one processor to determine one or more prediction scores for the one or more injection systems based on the operation data, wherein the one or more prediction scores include one or more predictions for one or more operational failures or misuses for the one or more injection systems. The program instruction causes the at least one processor to provide maintenance data related to the one or more malfunctions or misuses, and the maintenance data is determined based on the one or more predictive scores. The one or more operating parameters include a mechanical force generated by the motor, and the program instruction, when executed by the at least one processor, further, A computer program that controls one or more force sensors to measure the mechanical force generated by the motor to supply the one or more fluids from the one or more fluid sources, wherein the one or more prediction scores include one or more predictions of one or more malfunctions or misuses relating to the injection system related to wear, contrast agent and / or fouling accumulation, and the effect of wear, contrast agent and / or fouling accumulation on the injection system is determined by comparing the force of the motor in the current or recent injection operation with the force of the motor in one of the previous injection operations and calibration operations of the motor.

19. An injection system, An injector configured to supply one or more fluids from one or more fluid sources in one or more operations, One or more sensors configured to measure one or more operating parameters related to one or more operations of the injector, Receiving operational data related to the injector, wherein the operational data includes one or more operational parameters related to one or more operations of the injector. Determining one or more predictive scores for the injector based on the aforementioned operational data, wherein the one or more predictive scores include one or more predictions of one or more operational failures or misuses related to the injector. To provide maintenance data related to one or more malfunctions or misuses, wherein the maintenance data is determined based on one or more predictive scores, A computer system comprising one or more processors programmed or configured to perform the following: Equipped with, The one or more operating parameters include a mechanical force generated by a motor, and the injection system is An injection system further comprising one or more force sensors configured to measure the mechanical force generated by the motor to supply the one or more fluids from the one or more fluid sources, wherein the one or more prediction scores include one or more predictions of one or more malfunctions or misuses relating to the injection system relating to wear, contrast agent and / or fouling accumulation, and the effect of wear, contrast agent and / or fouling accumulation on the injection system is determined by comparing the force of the motor in the current or recent injection operation with the force of the motor relating to one of the motor's previous injection operations and calibration operations.

20. The injection system according to claim 19, wherein the one or more operations include at least one of the following operations: hardware operations, mechanical operations, electrical operations of one or more hardware components, software operations of one or more software applications, user interface operations, communication operations, or any combination thereof.

21. The injection system according to claim 20, wherein the one or more operations include the hardware operations of the one or more hardware components, and the one or more hardware components include at least one of the following hardware components: a microprocessor, memory, storage components, input components, output components, circuit boards, sensors, pumps, valves, or any combination thereof.

22. The injection system according to claim 20, wherein the one or more operations include the mechanical operation, and the mechanical operation includes supplying the one or more fluids from the one or more fluid sources.

23. The one or more operating parameters exceeding the following operating parameters related to the operation of the one or more injectors: vibration frequency and / or amplitude, pressure achieved for one or more injections, number of injections performed, resistance, current, voltage, noise or signal level, acceleration of components exceeding a threshold, power line condition, energy consumption, duration when power is turned on, linear energy supplied or used, nonlinear energy supplied or used, flow rate during one or more injections, difference between the flow rate during one or more injections and the programmed flow rate of one or more injections, amount pumped and / or delivered during one or more injections, difference between the amount pumped and / or delivered during one or more injections and the amount programmed to be pumped and / or delivered during one or more injections, duration of one or more injections, if the injection system exceeds the duration of one or more injections, the injection system will not deliver one or more injections. The injection system according to claim 19, comprising at least one of the following: a pressure limit or threshold programmed to stop, temperature and / or humidity within the injection system, number of power cycles, number of user interface keys operated, presence of communication with one or more other systems or devices, number and / or type of error codes received, number of scans performed by the image capture device before the image capture device successfully scans the barcode of the fluid source, type of contrast agent used, temperature within the injection system over one or more periods, temperature and / or humidity of the environment surrounding the injection system, indication of liquid within the injection system detected or measured by one or more liquid sensors, user or operator identifier, staff rating of wear, staff rating of cleanliness, or any combination thereof.

24. The one or more operating parameters include the vibration frequency and / or amplitude related to the one or more operations of the injector, and the injection system The injection system according to claim 23, further comprising one or more vibration monitoring sensors configured to measure the vibration frequency and / or amplitude relating to one or more operations of the injector, wherein the one or more prediction scores include one or more predictions of one or more malfunctions or misuses relating to the injection system relating to wear, contrast agent and / or fouling accumulation, and the effect of wear, contrast agent and / or fouling accumulation on the injection system is determined by applying a pattern prediction model to the vibration frequency and / or amplitude over one or more periods.

25. The injection system according to claim 23, wherein the one or more operating parameters include the flow rate during the one or more injections and the amount pumped and / or delivered during the one or more injections, the one or more prediction scores include one or more predictions of one or more malfunctions or misuses relating to the injection system related to wear, contrast agent and / or fouling accumulation, the effect of wear, contrast agent and / or fouling accumulation on the injection system is determined by applying at least one of the cubic root lifetime equation and one or more other suitable equations to the flow rate during the one or more injections and the amount pumped and / or delivered during the one or more injections, the average load being replaced by the maximum achieved pressure in the cubic root lifetime equation, and the duration of the load being established using the flow rate during the one or more injections and the amount pumped and / or delivered during the one or more injections to determine the one or more prediction scores.

26. The one or more operating parameters include the number of scans performed by the one or more image capture devices before the successful scanning of at least one barcode, and the injection system The injection system according to claim 23, further comprising one or more image capture devices configured to read at least one barcode.

27. The injection system according to claim 26, wherein one or more image capture devices are located in or placed on the housing of the injector, or on at least a portion of the inner surface of at least one syringe port of the injector.

28. The system further comprises one or more image capture devices positioned to have a field of view including the one or more components of the injection system for capturing one or more images of one or more components of the injection system, and the one or more processors The injection system according to claim 19, further programmed or configured to analyze the one or more images using one or more image processing techniques to identify at least one of (i) the proportion of the one or more components covered in undesirable contaminants, and (ii) leaks or cracks in the one or more components, wherein the one or more prediction scores include one or more predictions of one or more malfunctions or misuses of the injection system relating to the new or increased contaminant coverage and / or leaks or cracks of the one or more components, determined by comparing the current image with a previous image to identify new or increased contaminant coverage and / or leaks or cracks of the one or more components.

29. The injection system according to claim 19, wherein the maintenance data includes a prompt prompting the user to initiate at least one maintenance action related to the injection system.

30. The injection system according to claim 29, wherein the at least one maintenance action includes at least one of the following maintenance actions: namely, scheduling an inspection for the injection system; operating the injection system in a particular manner indicated by the maintenance data; requesting an inspection directly from the injection system; or any combination thereof.

31. The injection system according to claim 19, wherein the maintenance data includes an instruction to the injection system causing the injection system to automatically initiate at least one maintenance action.

32. The infusion system according to claim 31, wherein the at least one maintenance action includes prompting a user to initiate at least one maintenance action related to the infusion system; transmitting operational data and / or warnings to a remote system; providing remote input to a remote computer system to modify and / or update software and / or one or more operational parameters; scheduling an inspection technician; performing a specific action; providing recommendations for improving inspections based on a comparison of the infusion system with one or more other infusion systems; restarting the software of the infusion system; updating the software of the infusion system; providing customized preventive maintenance inspections; providing an inspection plan based on operational parameters based on usage; disabling or limiting one or more actions or functions; restarting the power supply; disabling power; recommending training to a user; measuring the deterioration, wear, or cleanliness of components; providing instructions to avoid specific malfunctions and / or misuse by using the infusion system in a particular manner; ordering one or more disposable items and / or one or more contrast agents; or any combination thereof.

33. At least one processor, The injection system according to claim 19, further programmed or configured to automatically disable the injection system from performing an injection operation based on the maintenance data.

34. The injection system according to claim 19, further comprising one or more sensors configured to measure one or more operating parameters relating to one or more operations of the injector, wherein the one or more sensors include at least one of the following sensors: a flow sensor, a temperature sensor, an accelerometer, a vibration monitoring sensor, a strain gauge, a motor current sensor, an image sensor, an air sensor, a force sensor, or any combination thereof.

35. The injection system according to claim 19, wherein the one or more malfunctions or misuses relating to the injection system include at least one of the following: a malfunction of an electrical component, a malfunction of a software component, a malfunction of a mechanical component, the injection system receiving user input from a user of the injection system that causes the injection system to operate in violation of one or more predetermined operating thresholds, or any combination thereof.

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