Use of CATH LAB images for predicting and controlling contrast agent use

Abstract

An example system includes a memory; and processing circuitry communicatively coupled to the memory, the processing circuitry configured to: determine a maximum contrast agent dose for a cardiac catheterization laboratory procedure for a current patient; determining a cumulative amount of contrast agent used concurrently with the cardiac catheterization laboratory procedure; and outputting, for display, a graphical representation of the comparison between the cumulative amount of contrast agent used and the maximum dose of contrast agent during the cardiac catheterization laboratory procedure.

Description

[0001] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 365,939, filed on June 6, 2022, the entire contents of which are incorporated herein by reference. Technical Field

[0002] The present disclosure relates to the use of images captured during medical procedures. Background Art

[0003] During a medical procedure, a clinician may use an imaging system to be able to visualize the internal anatomy of a patient. Such an imaging system may display anatomical structures, medical devices, etc., and may be used to diagnose a patient condition or help guide a clinician to move a device, such as a medical device, to a desired location within the patient's body. An imaging system may use sensors to capture video images that may be displayed during a medical procedure. Imaging systems include angiography systems, ultrasound imaging systems, computed tomography (CT) scanning systems, magnetic resonance imaging (MRI) systems, isocentric C-arm fluoroscopy systems, positron emission tomography (PET) systems, intravascular ultrasound (IVUS), optical coherence tomography (OCT), and other imaging systems. Summary of the invention

[0004] In general, the present disclosure relates to various techniques and medical systems for using images captured during a medical procedure to perform a procedure and / or device evaluation. For example, the system may track the movement of a medical device to evaluate operator technique as a study variable, such as which operator technique provides the best results. As used herein, a medical device includes any device that can be used to treat a patient. The system may also connect other equipment commonly used in a cardiac catheterization laboratory (Cath Lab) and integrate their corresponding data, such as integrating ablation data from a renal denervation (RDN) generator into an imaging system (such as fluoroscopy) to enable a more detailed evaluation of the results. Additionally or alternatively, the system may track the movement of a device to evaluate the cross-capability of the device with a specific type of lesion. By tracking the position of a medical device during a therapeutic medical procedure, the system may determine, for example, which operator technique provides the best results and / or which medical device provides the best results for a specific type of lesion. The system may include a computer vision model that can be used to identify, classify and / or score specific lesions. The system may also include a machine learning model that can be used to determine potential treatments that are most likely to achieve a successful outcome and present such potential treatment options to the clinician before, during, or after a therapeutic medical procedure.

[0005] The present disclosure also relates to various technologies and medical systems for using images captured during diagnostic medical procedures for treatment planning purposes. For example, during a diagnostic session (e.g., diagnostic angiography), there are three possible results. First, the clinician may determine that intervention is not needed. Second, the clinician may determine that emergency intervention is needed, and the clinician can handle intervention during the same Cath Lab session (e.g., without the patient leaving and then returning again). Third, treatment may be needed, but either the clinician feels uncomfortable when performing the treatment, or the hospital where the Cath Lab is located does not have the necessary equipment to carry out the treatment. In the case of the third possible result, there may be imaging data (e.g., angiography data) from diagnostic medical procedures. The example system can use such imaging data to plan or help clinicians plan treatment. Such a system may include a computer vision model and a machine learning model. The computer vision model can be used to identify, classify and / or score specific lesions. The machine learning model can be used to determine the potential treatment that is most likely to obtain a successful result, and such potential treatment strategies can be presented to clinicians to plan treatment for therapeutic medical procedures. The system can be configured to run simulations of potential treatment strategies to assist a clinician in selecting one or more treatment strategies to use during a therapeutic medical procedure.

[0006] The present disclosure also relates to various technologies and medical systems for streaming or sharing representations of imaging data from one or more image sensors during a medical procedure (e.g., a cardiac catheterization medical procedure) with a remote clinician. For example, when a specific clinician is performing a medical procedure in a cardiac catheterization laboratory (Cath Lab), a medical system can establish (e.g., via a secure network) a communication session with a device associated with a remote clinician (i.e., a second clinician not located in the Cath Lab). A computing system can stream or share imaging data to a remote clinician via a communication session. In this way, a medical system can enable a remote clinician to provide assistance (e.g., observation and / or consultation) to a specific clinician (i.e., a clinician who actually performs the procedure) when the specific clinician performs the procedure. Enabling such remote assistance may bring one or more advantages. As an example, remote assistance can improve the comfort and / or confidence of a specific clinician in terms of specific diagnosis, treatment strategy, technology, equipment or tool selection. Thus, consultations from remote clinicians via a communication session may result in more cases being moved from the third possible outcome described above (where treatment must be delayed) to the second possible outcome described above (where the operating clinician handles the intervention during the same session). Enabling remote assistance can provide the aforementioned benefits without the burden of having to obtain on-site assistance from another clinician. For example, a particular clinician may be located at a rural medical facility and may be the only clinician on site with Cath Lab experience. The technology of the present disclosure enables such clinicians to obtain live in-procedure assistance without the need for another clinician to travel to the rural facility. Although primarily described with respect to medical procedures including percutaneous coronary intervention (PCI) procedures, the medical system according to the present disclosure is also applicable to other medical procedures within the Cath Lab. For example, the medical system according to the present disclosure can be used to perform one or more other medical procedures performed in a Cath lab, the one or more other medical procedures including diagnostic cardiac catheterization, atrial septal atherectomy, cardiac ablation, cardiac resynchronization therapy, CardioMEMS TMHF system implantation, coronary stent implantation, coronary ultrasound, electrophysiology studies, implantable cardioverter defibrillator (ICD) placement, implantable loop recorder, intravascular ultrasound (IVUS), percutaneous transluminal angioplasty (PTCA), peripheral angioplasty, permanent pacemaker placement, rotablator, TAVR, three-dimensional mapping, valvuloplasty, etc. In some examples, the PCI procedure is a medical procedure performed on patients with lesions (e.g., bifurcation lesions) within their vasculature. As described herein, examples of the present disclosure relate to a medical system for treating a patient condition where the patient condition is a lesion. However, other patient conditions that can be treated within a Cath lab are considered, such as structural heart disease (e.g., cardiomyopathy, congenital heart disease, heart valve disease, etc.).

[0007] In some examples, a medical system according to the present disclosure may prevent streaming or sharing of a patient's personal health information (PHI) with a clinician who does not have permission to observe PHI. For example, the system may be configured to determine the permission status of a remote clinician, who may be within the same hospital or network or outside a hospital or network associated with a clinician in operation. In some examples, in response to determining that the permission status of a remote clinician is "external" or indicating that the remote clinician is not affiliated with some other designation of a Cath Lab that is performing an intervention, the medical system may be configured to edit personal health information from a representation of imaging data. In this way, personal health information (PHI) can be protected, and the benefits of clinician consultation can be achieved when both clinicians are observing imaging data (e.g., without obtaining further patient consent for PHI disclosure). The systems and techniques disclosed herein may allow suggestions or instructions from remote clinicians to observe imaging data in real time through a communication session. Therefore, since a patient may only need to undergo a single intervention, the risk can be reduced because fewer interventions can be performed and the patient can receive necessary treatment immediately.

[0008] The present disclosure also relates to medical systems and techniques for generating and / or sharing curated video highlights or images from medical procedures. A medical system according to the present disclosure may generate a compressed version of imaging data (e.g., fluoroscopic imaging) sensed by one or more image sensors during a medical procedure. The compressed version of the imaging data may include images corresponding to specific events during a medical procedure (such as a cardiac catheterization medical procedure). In some examples, a medical system according to the present disclosure may be configured to receive a user input to start or end a video excerpt, which may correspond to a key part of the medical procedure. Additionally or alternatively, a medical system according to the present disclosure may be configured to execute a computer vision model to identify a patient condition or an event within a procedure in the received imaging data. In some examples, in response to identifying a patient condition or an event within a procedure, the medical system may be configured to present to a clinician an option of providing a user input to start a video excerpt of the received imaging data. In some examples, the medical system may be configured to edit personal health information from a compressed version of the imaging data. In some examples, the medical system may be further configured to share or stream a compressed version of the imaging data with a remote clinician. Thus, systems and techniques according to the present disclosure may allow for easy sharing of before / after images of identified events or medical procedures via a secure platform. Additionally, in some examples, remote clinicians may view a condensed version of the procedure to quickly review ongoing medical procedures.

[0009] The present disclosure also relates to medical systems and techniques for receiving imaging data including one or more identification elements from one or more image sensors and generating a de-identified version of the imaging data that does not include the one or more identification elements. In some examples, at least one of the one or more identification elements may be personal health information (PHI), which may be protected health information as defined by HIPAA or similar regulations. The medical system may be configured to edit, remove, blur or make illegible text information (such as patient name, birthday or other personal health information). In some examples, the imaging data from one or more image sensors may include PHI, and the medical system may be configured to scan the imaging data, identify the text overlay, and edit, remove, blur or make illegible the text overlay. In some examples, the medical system may be further configured to upload the de-identified version of the imaging data to a server. In some examples, the medical system may be further configured to present the clinician with the option of posting the de-identified version of the imaging data on a social network or otherwise sharing the de-identified version of the imaging data. In some examples, the medical system may be configured to prevent or block the imaging data from being posted or published to a social network before the imaging data is properly de-identified. In some examples, the social network may be a physician-only social network (e.g., Murmur). Relative to hospitals that lack secure sharing methods or rely on third-party anonymization software, medical systems and techniques according to the present disclosure may allow for the posting and discussion of case videos, video highlights, before / after images, etc. in a secure manner. Medical systems and techniques according to the present disclosure may facilitate clinician discussion and education and / or enhance the reputation of operating clinicians who are able to smartly and securely share imaging data obtained from medical procedures they have performed (e.g., cardiac catheterization medical procedures).

[0010] The present disclosure also relates to medical systems and techniques for clinician education. In some examples, medical systems and techniques according to the present disclosure may allow clinicians to see how their cases or treatment strategies differ from the strategies of another clinician (e.g., an expert) in similar cases. In some examples, a medical system according to the present disclosure may be configured to output an overlay or side-by-side representation of imaging data from a first medical procedure and a second medical procedure stored in a memory for display. The medical system may execute a computer vision model and / or a machine learning model to identify the second medical procedure for output for display with the first medical procedure based on the similarity of one or more patient conditions of the first medical procedure to the second medical procedure using propensity matching. In some examples, the patient condition may be a lesion, and the medical system may use propensity matching by comparing one or more lesion characteristics from a lesion associated with the first medical procedure, and identify and select a separate medical procedure including a similar lesion (e.g., the most similar lesion) from a plurality of medical procedures stored in the memory based on the one or more lesion characteristics to output for display as a second medical procedure. In some examples, the medical system may be further configured to allow the clinician to sort and / or filter the medical procedures stored in the memory in one or more ways to allow the clinician to filter the desired results. Therefore, because the medical system of the present disclosure can use computer vision and / or machine learning to find and output similar medical procedures stored in the memory for display so as to be compared with the immediate medical procedures, the clinician can receive more relevant and targeted feedback than would otherwise be observable. In this way, the medical system according to the present disclosure can provide the desired learning and educational benefits, which can enhance the skills of the clinician and better prepare them to perform upcoming medical procedures.

[0011] The present disclosure also relates to a learning path that a clinician (e.g., an interventional cardiologist) completes in exchange for credits. In some examples, credits can be integrated with a certification body to provide a clever procedure for reporting and receiving credits from a certification body. In some examples, a medical system can be configured to capture user information from a user interacting with the medical system to identify the user. The medical system can be configured to store data representing an excerpt of imaging data from a separate medical procedure in a plurality of cardiac catheterization medical procedures in a memory. An excerpt from a separate medical procedure can be output for display, and after outputting an excerpt from a medical procedure for display, the medical system can count the user's viewing of the excerpt of the medical procedure as credit. In some examples, the medical system can be further configured to reward the user with at least a portion of continuing medical education (CME) credits. In some examples, the medical system can be configured to present to the user an option to filter through any one of a plurality of acceptable medical techniques (e.g., acceptable percutaneous coronary intervention techniques). Traditionally, certain types of clinicians may lack a structured curriculum to advance at a certain stage in their careers. A medical system according to the present disclosure may provide a mechanism for setting up online courses. Such a mechanism may provide further specialization for clinicians, as clinicians may view and receive credits for viewing multiple medical procedures. For example, a medical system according to the present disclosure may help an interventional cardiologist further specialize in complex PCI (e.g., bifurcation disease) by viewing multiple medical procedures related to one or more of the six currently accepted techniques for treating lesions.

[0012] The present disclosure is also intended to monitor and provide guidance on the use of contrast agents during Cath Lab procedures. The amount of contrast agent used is a balance between using enough contrast agent so that the image contains the necessary details and not using too much contrast agent to avoid causing undesirable side effects. In some examples, the system can track how much contrast agent has been used and provide guidance to the clinician. For example, the system can inform the clinician how the actual amount of contrast agent used compares to the expected / predicted amount of contrast agent.

[0013] Aspects of the present disclosure are applicable to at least Cath Lab procedures. Example Cath lab procedures include, but are not necessarily limited to, coronary procedures, renal denervation (RDN) procedures, structural heart and aortic (SH and A) procedures (e.g., transcatheter aortic valve replacement (TAVR), transcatheter mitral valve replacement (TMVR), etc.), device implantation procedures (e.g., cardiac monitors, pacemakers, defibrillators, etc.), etc.

[0014] In one example, a medical system includes: a memory configured to store at least one computer vision model; and a processing circuit communicatively coupled to the memory, the processing circuit configured to: receive imaging data of at least a portion of a patient's vascular system generated during a cardiac catheterization procedure; and execute the at least one computer vision model based on the received imaging data to determine characteristics of a lesion of the vascular system.

[0015] In another example, a method includes receiving, by a processing circuit, imaging data of at least a portion of a patient's vascular system generated during a cardiac catheterization procedure; and executing, by the processing circuit, at least one computer vision model based on the received imaging data to determine characteristics of a lesion in the vascular system.

[0016] In another example, a non-transitory computer-readable medium stores instructions that, when executed, cause a processing circuit to: receive imaging data of at least a portion of a patient's vascular system generated during a cardiac catheterization procedure; and execute at least one computer vision model based on the received imaging data to determine characteristics of a lesion in the vascular system.

[0017] In another example, a medical system includes: a memory configured to store at least one computer vision model and at least one machine learning model; and a processing circuit communicatively coupled to the memory, the processing circuit configured to: receive diagnostic imaging data of at least a portion of a patient's vascular system generated during a cardiac catheterization diagnostic procedure; execute at least one computer vision model based on the received diagnostic imaging data to determine characteristics of a lesion in the vascular system; and execute the at least one machine learning model based on the determined characteristics of the lesion to determine at least one treatment strategy, the at least one treatment strategy comprising at least one treatment technique and at least one medical device.

[0018] In another example, a method includes: receiving, by a processing circuit, diagnostic imaging data of at least a portion of a patient's vascular system generated during a cardiac catheterization diagnostic procedure; executing, by the processing circuit, at least one computer vision model to determine characteristics of a lesion in the vascular system based on the received diagnostic imaging data; and executing, by the processing circuit, the at least one machine learning model to determine at least one treatment strategy based on the determined characteristics of the lesion, the at least one treatment strategy comprising at least one treatment technique and at least one medical device.

[0019] In another example, a non-transitory computer-readable storage medium stores instructions that, when executed, cause a processing circuit to: receive diagnostic imaging data of at least a portion of a patient's vascular system generated during a cardiac catheterization diagnostic procedure; execute at least one computer vision model based on the received diagnostic imaging data to determine characteristics of a lesion in the vascular system; and execute at least one machine learning model based on the determined characteristics of the lesion to determine at least one treatment strategy, the at least one treatment strategy comprising at least one treatment technique and at least one medical device.

[0020] In another example, a medical system includes: a memory; one or more image sensors; and a processing circuit communicatively coupled to the memory, the processing circuit configured to: receive a representation of user input from a first clinician performing a cardiac catheterization laboratory procedure requesting a consultation from a second clinician located remotely from the first clinician; in response to receiving the representation of user input requesting the consultation, establish a communication session between a first computing device associated with the first clinician and a second computing device associated with the second clinician; and stream a representation of data of the cardiac catheterization laboratory procedure captured by the one or more image sensors via the communication session.

[0021] In another example, a non-transitory computer-readable storage medium stores instructions that, when executed, cause processing circuitry to: receive a representation of user input from a first clinician who is performing a cardiac catheterization laboratory procedure requesting a consultation from a second clinician located remotely from the first clinician; in response to receiving the representation of user input requesting the consultation, establish a communication session between a computing device associated with the first clinician and a computing device associated with the second clinician; and stream a representation of data of the cardiac catheterization laboratory procedure captured by the one or more image sensors via the communication session.

[0022] In another example, a method includes: receiving, by processing circuitry, a representation of user input from a first clinician who is performing a cardiac catheterization laboratory procedure requesting a consultation from a second clinician located remotely from the first clinician; in response to receiving the representation of user input requesting the consultation, establishing a communication session between a computing device associated with the first clinician and a computing device associated with the second clinician; and streaming, via the communication session, a representation of data of the cardiac catheterization laboratory procedure captured by one or more image sensors.

[0023] In another example, a medical system includes: a memory; and a processing circuit communicatively coupled to the memory, the processing circuit being configured to: receive imaging data sensed by one or more image sensors during a cardiac catheterization medical procedure; and generate a compressed version of the imaging data based on the imaging data, the compressed version of the imaging data including images corresponding to specific events of the cardiac catheterization medical procedure.

[0024] In another example, a method includes receiving, by a processing circuit, imaging data sensed by one or more image sensors during a cardiac catheterization medical procedure and generating, based on the imaging data, a compressed version of the imaging data, the compressed version of the imaging data including images corresponding to specific events of the cardiac catheterization medical procedure.

[0025] In another example, a medical system includes: a memory; and a processing circuit communicatively coupled to the memory, the processing circuit being configured to: receive imaging data, the imaging data being sensed by one or more image sensors during a cardiac catheterization medical procedure, the received imaging data including one or more identification elements; and generate a de-identified version of the imaging data based on the imaging data, the de-identified version of the imaging data not including at least one of the one or more identification elements.

[0026] In another example, a method includes: receiving, by a processing circuit, imaging data sensed by one or more image sensors during a cardiac catheterization medical procedure, the received imaging data including one or more identification elements; and generating, by the processing circuit, a de-identified version of the imaging data based on the imaging data, the de-identified version of the imaging data excluding at least one of the one or more identification elements.

[0027] In another example, a medical system includes: a memory; and a processing circuit communicatively coupled to the memory, the processing circuit being configured to: receive imaging data from one or more image sensors during a first cardiac catheterization medical procedure; execute at least one computer vision model to identify a second cardiac catheterization medical procedure among a plurality of cardiac catheterization medical procedures stored in the memory; and output a representation of the imaging data from the first cardiac catheterization medical procedure and a representation of the imaging data from the second cardiac catheterization medical procedure for display.

[0028] In another example, a method includes: receiving, by a processing circuit, imaging data from one or more image sensors during a first cardiac catheterization medical procedure; executing, by the processing circuit, at least one computer vision model to identify a second cardiac catheterization medical procedure among a plurality of cardiac catheterization medical procedures stored in a memory; and outputting, by the processing circuit, a representation of the imaging data from the first cardiac catheterization medical procedure and a representation of the imaging data from the second cardiac catheterization medical procedure for display via a display.

[0029] In another example, a medical system includes: a memory; and a processing circuit that is communicatively coupled to the memory, the processing circuit being configured to: capture user information from a user interacting with the medical system to identify the user; store data representing imaging data from multiple cardiac catheterization medical procedures in the memory; output data stored in the memory representing a separate cardiac catheterization medical procedure from the multiple cardiac catheterization medical procedures for display; and after outputting the data representing the separate cardiac catheterization medical procedure for display, credit the user for viewing an excerpt of the cardiac catheterization medical procedure.

[0030] In another example, a method includes: capturing, by processing circuitry, user information from a user interacting with the medical system to identify the user; storing, by the processing circuitry, data representing imaging data from a plurality of cardiac catheterization medical procedures in the memory; outputting, by the processing circuitry, data representing a separate cardiac catheterization medical procedure from the plurality of cardiac catheterization medical procedures stored in the memory for display; and, after outputting the data representing the separate cardiac catheterization medical procedure for display, crediting the user for viewing the separate cardiac catheterization medical procedure.

[0031] In another example, a medical system includes: a memory; and a processing circuit communicatively coupled to the memory, the processing circuit being configured to: determine a cumulative amount of contrast media used during a cardiac catheterization laboratory procedure; and output for display a graphical representation of the cumulative amount of contrast media used during the cardiac catheterization laboratory procedure.

[0032] In another example, a method includes determining a cumulative amount of contrast media used during a cardiac catheterization laboratory procedure; and outputting for display a graphical representation of the cumulative amount of contrast media used during the cardiac catheterization laboratory procedure.

[0033] In another example, a computer readable storage medium stores instructions that cause one or more processors to: determine a cumulative amount of contrast media used during a cardiac catheterization laboratory procedure; and output for display a graphical representation of the cumulative amount of contrast media used during the cardiac catheterization laboratory procedure.

[0034] These and other aspects of the present disclosure will be apparent from the following detailed description.In no event, however, should the above summary be construed as limiting the claimed subject matter, which is defined solely by the appended claims.

[0035] The present disclosure is intended to provide an overview of the subject matter described in the present disclosure. It is not intended to provide an exclusive or exhaustive explanation of the devices and methods described in detail in the following figures and descriptions. Further details of one or more examples are set forth in the following figures and descriptions. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 is a schematic perspective view of one example of a system for guiding a medical device through a patient area according to one or more aspects of the present disclosure.

[0037] Figure 2 is a block diagram of one example of a computing device according to one or more aspects of the present disclosure.

[0038] Figure 3 is a block diagram of an example energy generating device according to one or more aspects of the present disclosure.

[0039] Figure 4 is a conceptual diagram showing a representation of ablated tissue overlaid on imaging data in accordance with one or more aspects of the present disclosure.

[0040] Figure 5 is a flow chart illustrating an example technique for determining characteristics of a lesion in accordance with one or more aspects of the present disclosure.

[0041] Figure 6 is a schematic perspective view of one example of a system for determining a treatment strategy according to one or more aspects of the present disclosure.

[0042] Figure 7 is a schematic diagram of an example of a computing device according to one or more aspects of the present disclosure.

[0043] Figure 8 is a flow chart illustrating an example technique for determining a treatment strategy in accordance with one or more aspects of the present disclosure.

[0044] Fig. 9is a schematic perspective view of one example of a system for establishing a communication system between an operating clinician and a remote clinician and streaming imaging data representing a medical procedure in accordance with one or more aspects of the present disclosure.

[0045] Fig.10 is a flow diagram illustrating an example technique for streaming representations of data to a remote clinician in accordance with one or more aspects of the present disclosure.

[0046] Fig.11 is a time diagram illustrating an example compressed version of imaging data sensed by one or more image sensors during a cardiac catheterization medical procedure in accordance with one or more aspects of the present disclosure.

[0047] Fig.12 is a flow chart illustrating an example technique for generating a compressed version of imaging data sensed by one or more image sensors in accordance with one or more aspects of the present disclosure.

[0048] Fig.13A are example conceptual screenshots illustrating example representations of imaging data including identification elements in accordance with one or more aspects of the present disclosure.

[0049] Fig. 13B Is shown not including Fig.13A An example screenshot of an example representation of imaging data of at least one of the identified elements.

[0050] Fig.14 is a flow chart illustrating an example technique for generating a de-identified version of imaging data sensed by one or more image sensors during a medical procedure in accordance with one or more aspects of the present disclosure.

[0051] Fig.15 is a screenshot illustrating an example user interface for using an example medical system of the present disclosure for educational purposes in accordance with one or more aspects of the present disclosure.

[0052] Fig.16 is a flow chart illustrating an example technique for using an example medical system of the present disclosure for educational purposes in accordance with one or more aspects of the present disclosure.

[0053] Fig.17 is a flow chart illustrating an example technique for using an example medical system of the present disclosure for educational purposes in accordance with one or more aspects of the present disclosure.

[0054] Fig.18 is a schematic perspective view of an example medical system configured to provide contrast usage data according to one or more aspects of the present disclosure.

[0055] Fig.19 is a conceptual diagram illustrating an example graphical user interface (GUI) including contrast media usage data according to one or more aspects of the present disclosure.

[0056] Fig. 20 is a flow chart illustrating an example technique for providing contrast media usage data according to one or more aspects of the present disclosure.

[0057] Fig.21 is a conceptual diagram illustrating an example machine learning model according to one or more aspects of the present disclosure

[0058] Fig. 22 is a conceptual diagram illustrating an example training process for a machine learning model according to one or more aspects of the present disclosure. DETAILED DESCRIPTION

[0059] The imaging system can be used to assist clinicians in medical procedures (such as diagnostic medical procedures, therapeutic medical procedures such as percutaneous coronary intervention (PCI) procedures and RDN procedures, structural heart procedures, etc., or any combination thereof). For example, the imaging system can be used to determine the presence of lesions in the patient's vasculature, which may limit or block blood flow in the patient's vasculature. The imaging system can also be used when performing ablation procedures, angioplasty procedures, or other therapeutic medical procedures intended to treat lesions in the patient's vasculature (including the heart). Although this article is mainly described with respect to the patient's vasculature, the imaging system described herein can be used for other medical purposes and is not limited to cardiovascular purposes. The imaging system can generate images and / or video data via sensors. The video data can be displayed and / or recorded during the medical procedure for later use. The video data may include representations of multiple parts of the patient's vasculature or heart, including one or more lesions that may limit blood flow through the patient's vasculature or the part of the heart, the geometry and position of such lesions in the blood vessels or the heart, and / or any medical device that may be within the field of view of one or more sensors of the imaging system. In some examples, contrast fluid may be injected into the patient's vasculature and the imaging data may include fluoroscopic imaging.

[0060] As referred to herein, a medical procedure may be a diagnostic medical procedure or a therapeutic medical procedure. A diagnostic medical procedure is a medical procedure that uses imaging or other techniques to diagnose a disease. A therapeutic medical procedure is a medical procedure in which treatment is delivered and / or intervention is performed, such as PCI. A single Cath Lab session may include 1) only diagnostic medical procedures, for example, where no lesions are identified as requiring treatment or where treatment is too difficult for a given clinician or the hospital where the Cath Lab is located does not have the necessary equipment to treat the lesions; 2) only therapeutic medical procedures, for example, where the lesions were previously diagnosed; or 3) a diagnostic medical procedure followed by a therapeutic medical procedure. An example of a medical procedure that can be performed in a Cath Lab is a cardiac catheterization procedure (which can be a diagnostic medical procedure or a therapeutic medical procedure).

[0061] In some examples, representations of data from sensors collected during a medical procedure may be shared or streamed with a clinician at a remote location (i.e., outside the Cath Lab where the medical procedure is being performed). A remote clinician may ask questions or make suggestions during a medical procedure, and the medical system may allow such real-time input from a remote clinician, thereby potentially improving medical outcomes. Additionally or alternatively, imaging data from one or more sensors may be stored in a memory or uploaded to a network for training or educational purposes. A medical system according to the present disclosure may include a processing circuit configured to use propensity matching using computer vision and / or machine learning to identify and select a medical procedure from a plurality of medical procedures stored in a memory to output for display on a user interface. Therefore, a clinician may see how a medical procedure for treating a similar patient condition (e.g., a lesion with a similar size, position, geometry, etc.) was treated in a previous medical procedure.

[0062] Figure 11 is a schematic perspective view of an example of a system for guiding a medical device through a patient area according to one or more aspects of the present disclosure. System 1000 includes a guide workstation 1052, a display device 1010, a table 1020, a medical device 1030, an imager 1040, and a computing device 1050. System 1000 may be an example of a system for use in a catheterization laboratory (Cath lab). The guide workstation 1052 may include, for example, an off-the-shelf device, such as a laptop computer, a desktop computer, a tablet computer, a smart phone, or other similar devices. In some examples, a controller of such a device may generate controller data. Such a device may include an atherectomy device, an energy generating device, or other devices that can generate data. In some examples, the guide workstation may be a dedicated device. The guide workstation 1052 may be configured to control an electrosurgical generator, a peristaltic pump, a power supply, or any other accessories and peripheral devices associated with the system 1000 or forming a part of the system. In some examples, the guide workstation 1052 may include an electrosurgical generator, such as an energy generating device 1054. For example, the energy generating device 1054 may include an RDN generator configured to generate radiofrequency energy for an ablation catheter (e.g., medical device 1030) to deliver to ablate tissue in a renal artery to treat hypertension. Although shown as part of the guide workstation 1052, in some examples, the energy generating device 1054 may be a stand-alone device. The computing device 1050 may include, for example, an off-the-shelf device such as a laptop computer, a desktop computer, a tablet computer, a smart phone, or other similar devices, or may include a dedicated device. In some examples, the guide workstation 1052 may perform various control functions with respect to the imager 1040 and may interact extensively with the computing device 1050. The guide workstation 1052 may be communicatively coupled to the computing device 1050 so that the guide workstation 1052 can control the operation of the imager 1040 and receive the output of the imager 1040. In some examples, the computing device 1050 may control various operations of the imager 1040.

[0063] The display device 1010 may be configured to output instructions, images, and messages related to at least one of the performance, position, orientation, or trajectory of the medical device 1030. In addition, the display device 1010 may be configured to output information about the medical device 1030, such as the model, type, size, etc. The table 1020 may be, for example, a surgical table or other table suitable for use during a medical procedure, which may optionally include an electromagnetic (EM) field generator 1021. The EM field generator 1021 may optionally be included and used to generate an EM field during a medical procedure, and when included, may form part of an EM tracking system for tracking the position of one or more medical devices within a patient's body. The EM field generator 1021 may include various components, such as a specially designed mat to be placed under or integrated into an operating table or bed.

[0064] Medical devices can also be visualized through the use of imaging (e.g., ultrasound imaging). Figure 1 In an example of an imaging device 1040, such as an intravascular ultrasound (IVUS) device, an ultrasound wand, or other imaging device, may be used to image a patient's body during a medical procedure to visualize the location of a medical device, such as a surgical instrument, a device delivery or placement device, and an implant, within the patient's body. The imager 1040 may include one or more sensors 1070. For example, the imager 1040 may include an interventional ultrasound probe having an ultrasound transducer array. In some examples, the imager 1040 may include an ultrasound transducer array including a plurality of transducer elements or other types of imaging sensors. These transducer elements may be configured to sense ultrasound energy reflected from the patient's anatomical structure and / or the medical device 1030. The imager 1040 may optionally have an EM tracking sensor, such as a clip-on sensor or a patch sensor, embedded or attached to the interventional ultrasound probe. Although primarily described as an IVUS device, the imager 1040 may be any type of imaging device including one or more sensors, such as a CT device, an MRI device, a fluoroscopic device, a PET device, an angiography device, and the like.

[0065] Imager 1040 may image a region of interest in the patient's body. The particular region of interest may depend on the anatomy, the diagnostic medical procedure, and / or the intended therapy. For example, when performing PCI, a portion of the vasculature may be the region of interest, or when performing a cardiac medical procedure, a portion of the heart may be the region of interest.

[0066] As further described herein, the imager 1040 can be positioned relative to the medical device 130 so that the medical device is at an angle to the ultrasound image plane, thereby enabling the clinician to visualize the spatial relationship of the medical device 130 to the ultrasound image plane and to the imaged object. In addition, if provided, the EM tracking system can also track the position of the imager 1040. In one or more examples, the imager 1040 can be placed in the patient's body. The EM tracking system can then track the position of such imager 1040 and medical device 1030 in the patient's body. In some examples, the functions of the computing device 150 can be performed by the guidance workstation 1052, and the computing device 1050 may not be present.

[0067] The position of the medical device 1030 within the patient's body can be tracked during a therapeutic medical procedure. An exemplary technique for tracking the position of the medical device 1030 includes using an imager 1040 to track the position of the medical device 1030. Another exemplary method of tracking the position of the medical device 1030 includes using an EM tracking system that tracks the position of the medical device 1030 by tracking a sensor attached to or incorporated into the medical device 1030. Before starting the medical procedure, the clinician can verify the accuracy of the tracking system using any suitable one or more techniques. Any suitable medical device 1030 can be used with the system 1000. Examples of medical devices or apparatus include stents, catheters, angioplasty devices, ablation devices, etc.

[0068] The computing device 1050 may be communicatively coupled to the imager 1040, the workstation 1052, the display device 1010, and / or the server 1060, for example, by wired, optical, or wireless communications. The server 1060 may be a hospital server, a cloud-based server, or the like, which may or may not be located in a hospital's catheterization laboratory (Cath Lab). The server 1060 may be configured to store patient video data, electronic health or medical records, and the like. In some examples, the computing device 1050 may be an example of a workstation 1052.

[0069] The computing device 1050 may be configured to receive imaging data (e.g., generated by the sensor 1070) from the imager 1040. The imaging data may include image and / or video data. The system 1000 may track the movement of a medical device 1030, such as a guide catheter for cannulating a target vessel in a patient. Such tracked movement may be saved by the computing device 1050, the guidance workstation 1052, and / or the server 1060, and may be used to evaluate operator technique (e.g., successful medical device use) for training purposes, as a variable for study, as an input to a machine learning model for training a machine learning model, etc. For example, the system 1000 may track the movement of an electrohydraulic intravascular lithotripsy device through a calcified lesion. The computing device 1050, the guidance workstation 1052, and / or the server 1060 may use the tracked movement to evaluate the crossing capability (ability to pass through a lesion) of a particular medical device in a particular type of lesion. For example, computing device 1050, guidance station 1052, and / or server 1060 may execute a computer vision model to determine the type of lesion being treated and determine whether a medical device successfully passed through a particular lesion based on the tracked motion. In some examples, computing device 1050, guidance station 1052, and / or server 1060 may establish a database of lesion types and medical devices so that computing device 1050, guidance station 1052, and / or server 1060 may determine the likelihood that a particular medical device may be successful in passing through or treating a particular type of lesion. In some examples, computing device 1050, guidance station 1052, and / or server 1060 may train a machine learning model for the motion of the medical device, the type of lesion, and the type of medical device being used.

[0070] In some examples, during a therapeutic medical procedure, computing device 1050, guidance workstation 1052, and / or server 1060 executes a machine learning model to propose one or more treatment strategies, which may include one or more treatment techniques and one or more medical devices. For example, computing device 1050, guidance workstation 1052, and / or server 1060 executing a machine learning model may output for display a treatment strategy that is most likely to be successful for a particular type of lesion.

[0071] In some examples, computing device 1050 , guidance workstation 1052 , and / or server 1060 may save the motion information, lesion type, and medical device type for future review by a clinician to facilitate the clinician's evaluation of a specific treatment strategy for a specific lesion type.

[0072] Such techniques may be useful because there are several different lesion types, such as bifurcation lesions, calcified lesions, chronic total occlusions (CTOs), in-stent restenosis (ISRs), left main disease, etc. For example, the Medina classification system includes seven different subtypes of bifurcation lesions. In addition, there are multiple treatment techniques for different types of lesions. For example, there are at least six techniques for treating bifurcation lesions, and these techniques may include the use of different medical devices and / or the use of medical devices in different sequences. Therefore, the number of different permutations of treatment strategies for a given lesion may be quite large.

[0073] The computing device 1050, the guidance workstation 1052, and / or the server 1060 may use the imaging data to execute a computer vision model to determine the characteristics of the lesion. For example, the computing device 1050, the guidance workstation 1052, and / or the server 1060 may determine the type of lesion, the subtype of the lesion, or otherwise classify the lesion (e.g., provide a score or other identifier), etc. based on the determined characteristics of the lesion. The characteristics of the lesion may include, for example, the type of lesion (e.g., bifurcation lesion), the diameter of the lesion, the degree of stenosis, the degree of calcification, the vessel take-off angle, etc. For example, the computing device 1050, the guidance workstation 1052, and / or the server 1060 may use the classification of the lesions to execute a machine learning model to determine one or more treatment strategies that are most likely to succeed. The classification of lesions may cause lesions with greater similarity to be classified as being the same or close to each other, which may be referred to as "propensity matching" in some examples. In this manner, if a lesion has particular characteristics, computing device 1050, guidance workstation 1052, and / or server 1060 may determine one or more treatment strategies that are most likely to successfully treat a lesion having those (or nearly those) characteristics, and output the one or more treatment strategies for display to a clinician. By providing one or more treatment strategies with the highest likelihood of success to a clinician, for example, during a therapeutic medical procedure, the techniques of the present disclosure may enable specific treatment or prevention of a disease or medical condition. These techniques may improve treatment outcomes for patients, reduce the need for repeat therapeutic medical procedures, expedite therapeutic medical procedures, reduce patient exposure to radiocontrast agents, and conserve medical resources.

[0074] In some examples, the classification of lesions can be used as a basis to support clinical research, case planning, and / or case analysis (e.g., post-treatment medical procedures) for clinicians who want to understand why certain techniques are effective in some cases and not in other cases. The classification of lesions can also be used to highlight key factors that distinguish treatment strategies for two different lesion classifications to aid such clinicians in their analysis. In some examples, the classification of lesions can be used in clinical trials. For example, many people with lesions of the same (or very similar) characteristics can be treated with different treatment strategies, and the results can be compared.

[0075] In some examples, computing device 1050, guidance workstation 1052, and / or server 1060 may execute a computer vision model to determine the medical device being used. Alternatively or additionally, a clinician may enter the type of medical device being used into computing device 1050, guidance workstation 1052, and / or server 1060 via a user interface. Computing device 1050, guidance workstation 1052, and / or server 1060 may execute a computer vision model to determine whether the treatment of the lesion was successful, for example, via imaging data from a therapeutic medical procedure after the lesion has been treated. In some examples, imaging data after the therapeutic medical procedure may be collected, and computing device 1050, guidance workstation 1052, and / or server 1060 executing a computer vision model may determine the success of the treatment of the lesion. For example, the success of the treatment of the lesion may include the degree of openness of the area after treatment.

[0076] In some examples, computing device 1050 , guidance workstation 1052 , and / or server 1060 may execute machine learning models to evaluate medical device performance and provide guidance on new medical device specifications to help manufacturers improve the design of such medical devices to achieve higher levels of success in treating lesions.

[0077] The energy generating device 1054 (which may be an RDN generator) may be configured to generate energy for an ablation catheter (e.g., medical device 1030) that may ablate a lesion within the patient's vasculature or heart. The energy generating device 1054 may also generate information such as the amount of energy used during ablation, the length of time of ablation, error codes, etc. In some examples, the energy generating device 1054 may be communicatively coupled to the computing device 1050 and / or the guidance workstation 1052, and the computing device 1050 and / or the guidance workstation 1052 may integrate the generator data with the imaging data. For example, the system 1000 may introduce a timestamp into the generator data and the imaging data. The timestamp may be used to align the generator data with the imaging data. For example, the computing device 1050 and / or the guidance workstation 1052 may use the received generator data to determine the location and size of the ablated tissue. In some examples, the computing device 1050 and / or the guidance workstation may use a machine learning model to determine the location and size of the ablated tissue. In some examples, the machine learning model may reside on server 1060 instead of computing device 1050 or guidance workstation 1052, and computing device 1050 and / or guidance workstation 1052 may communicate with server 1060 to determine the location and size of ablated tissue using the machine learning model. Computing device 1050 and / or guidance workstation 1052 may output the imaging data together with the representation of the ablated tissue for display. The representation of the ablated tissue may be overlaid onto the imaging data. In some examples, computing device 1050 and / or guidance workstation 1052 may generate the representation of the ablated tissue using different colors, different shading, different pattern fills, etc., so that the ablated tissue can be easily identified by a clinician on a display such as display device 1010. In this way, the clinician can evaluate the likely success of the ablation cycle and determine whether further ablation of a particular lesion is desired or whether the clinician can proceed from the lesion.

[0078] If the generator data includes an error code, the computing device 1050 and / or the guidance workstation 1052 may output to show that an error occurred and the tissue was not ablated as expected. In this case, the computing device 1050, the guidance workstation 1052, and / or the server 1060 may execute a computer vision model to determine which (if any) tissue was actually ablated. The computing device 1050 and / or the guidance workstation 1052 may then output the imaging data and a representation of the tissue that was actually ablated for display. In some examples, when the generator data includes an error code, the computing device 1050 and / or the guidance workstation 1052 may control the energy generating device 1054 to shut down the energy generating device 1054 for patient safety reasons until the clinician or another person can check the error code. The computing device 1050 and / or the guidance workstation 1052 may also output a path through the patient's vasculature for display to guide the clinician as the clinician turns the ablation catheter for the next round of ablation. Such a path may be represented using a different color, a different shade, or a different cross-hatching so that the clinician can easily see the path.

[0079] In some examples, computing device 1050, guidance workstation 1052, and / or server 1060 may store imaging data with representations of ablation for future viewing and / or training of a machine learning model. For example, the stored imaging data with representations of ablation may be used to enable a more detailed study of patient treatment outcomes and / or for technology and / or medical device evaluation.

[0080] In some examples, the system 1000 may include an automatic contrast agent delivery device 1090. In such examples, the system 1000 may monitor the time at which the patient receives contrast agent and / or the amount of contrast agent provided to the patient by the automatic contrast agent delivery device. The computing device 1050, the guidance workstation 1052, and / or the automatic contrast agent delivery device may control the automatic contrast agent delivery device 1090 to stop delivering contrast agent when at least one of the time at which the patient receives contrast agent or the amount of contrast agent provided to the patient meets a threshold.

[0081] Figure 2 is a block diagram of an example of a computing device according to one or more aspects of the present disclosure. The computing device 2000 may be Figure 1 Examples of computing devices 1050, boot workstations 1052, and / or servers 1060 may include workstations, desktop computers, laptop computers, servers, smart phones, tablet computers, dedicated computing devices, or any other computing devices capable of performing the techniques of the present disclosure.

[0082] In some examples, computing device 2000 may be configured to perform processing, control, and other functions associated with guidance workstation 1052, imager 1040, and an optional EM tracking system. Figure 2 As shown in , computing device 2000 represents multiple instances of computing devices, each of which may be associated with one or more of guide workstation 1052, imager 1040, or EM tracking system. Computing device 2000 may include, for example, memory 2002, processing circuit 2004, display 2006, network interface 2008, input device 2010, or output device 2012, each of which may represent any of multiple instances of such devices within a computing system for ease of description.

[0083] Although the processing circuit 2004 appears in Figure 2 2000, but in some examples, features attributed to processing circuit 2004 may be performed by processing circuits of any of computing device 1050, guidance workstation 1052, imager 1040, server 1060, or an EM tracking system, or a combination thereof. In some examples, one or more processors associated with processing circuit 2004 in computing device 2000 may be distributed and shared across any combination of computing device 1050, guidance workstation 1052, imager 1040, server 1060, and an EM tracking system. Additionally, in some examples, processing operations or other operations performed by processing circuit 2004 may be performed by one or more processors residing remotely (such as one or more cloud servers or processors), each of which may be considered part of computing device 2000. Computing device 2000 may be used to perform any of the techniques described in this disclosure and may, alone or in combination with other components (such as computing device 1050, guidance workstation 1052, imager 1040, server 1060, EM tracking system, or components of a system including any or all of such devices), form all or part of a device or system configured to perform such techniques.

[0084] The memory 2002 of the computing device 2000 includes any non-transitory computer-readable storage medium for storing data or software that can be executed by the processing circuit 2004 and control the operation of the computing device 1050, the boot workstation 1052, the imager 1040, the server 1060, or the EM tracking system as applicable. In one or more examples, the memory 2002 may include one or more solid-state storage devices, such as flash memory chips. In one or more examples, the memory 2002 may include one or more mass storage devices connected to the processing circuit 2004 through a mass storage controller (not shown) and a communication bus (not shown).

[0085] Although the description of computer-readable media herein refers to solid-state storage devices, it should be understood by those skilled in the art that computer-readable storage media can be any available media that can be accessed by the processing circuit 2004. That is, computer-readable storage media include non-transitory, volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information such as computer-readable instructions, data structures, program modules or other data. For example, computer-readable storage media include RAM, ROM, EPROM, EEPROM, flash memory or other solid-state memory technology, CD-ROM, DVD, Blu-ray or other optical storage devices, cassettes, magnetic tapes, magnetic disk storage devices or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by the computing device 2000. In one or more examples, the computer-readable storage media can be stored in a cloud or remote storage device and accessed through at least one of a wired or wireless connection using any suitable one or more technologies.

[0086] Memory 2002 may store machine learning model 2022 and / or computer vision model 2024. In some examples, machine learning model 2022 and computer vision model 2024 may be the same. In other examples, machine learning model 2022 and computer vision model 2024 may be different.

[0087] The memory 2002 may store imaging data 2014 and controller data 2020. The imaging data 2014 may be acquired by the imager 1040 ( Figure 1 ) captured during a medical procedure for a patient. Processing circuit 2004 may receive imaging data 2014 from imager 1040 and store imaging data 2014 in memory 2002. Controller data 2020 may be generated by energy generating device 1054, and processing circuit 2004 may receive controller data 2020 from energy generating device 1054. Processing circuit 2004 may determine lesion length and / or location based on controller data 2020 and generate a representation of ablated tissue. Processing circuit 2004 may register controller data 2020 and imaging data 2014 using a timestamp (which may be placed in the data by, for example, energy generating device 1054, imager 1040, computing device 1050, or guidance workstation 1052). Processing circuit 2004 may output imaging data 2014 for display, for example, to display 2006 or display device 1010, along with a representation of ablated tissue overlaid on imaging data 2014. For example, processing circuitry 2004 may execute computer vision model 2024 to identify anatomical structures, such as vessels, bifurcations, orifices, etc., and may use such identified anatomical structures to align imaging data and representations of ablated tissue by associating the imaging data and representations of ablated tissue in an xy plane.

[0088] Memory 2002 may also store motion data 2028, medical device data 2026, and lesion classification 2030. Motion data 2028 may include tracked motion of medical device 1030 through a patient during a therapeutic medical procedure. Motion data 2028 may be generated by EM field generator 1021 by processing circuit 2004 executing computer vision model 2024 to track motion of medical device 1030 during a therapeutic medical procedure using imaging data from imager 1040, by imager 1040, or by another technique. Motion data 2028 may indicate the therapeutic technique being used, the performance of the clinician using the therapeutic technique, the success of the medical device 1030 used with the technique (e.g., the cross-capability of the medical device for a particular lesion type or classification). Medical device data 2026 may be generated by processing circuit 2004 executing computer vision model 2024. The computer vision model may be trained to recognize a medical device based on imaging data 2014. Alternatively or additionally, medical device data may be input into the computing device 2000 by a clinician via the input device 2010 or the user interface 2018. The medical device data 2026 may include the brand (make) and model of each medical device used during the therapeutic medical procedure. The medical device data 2026 may also include other identifying information or information that may be unique to the medical device, such as a bar code, year of manufacture, number of uses, etc.

[0089] The memory 2002 may also store a treatment strategy 2032. The processing circuit 2004 executing the machine learning model 2022 may, for example, determine a treatment strategy during a therapeutic medical procedure for presentation to a clinician. The treatment strategy 2032 may include one or more treatment techniques and one or more medical devices for use in a therapeutic medical procedure. Generally speaking, the treatment strategy 2032 may include one or more of the use of diagnostic catheters, plain old balloon angioplasty (POBA), mechanical atherectomy, intravascular lithotripsy (IVL), drug-coated balloon angioplasty, stent delivery (including bare metal stents, drug-eluting stents (DES), bioresorbable stents, etc.), post-stent implantation optimization, wire-based blood flow reserve fraction (FFR) or other blood flow reserve metrics, image-based FFR or other blood flow reserve metrics, OCT, IVUS, etc. In some examples, the treatment strategy 2032 may also include the location of one or more medical devices during the therapeutic medical procedure and / or the order in which one or more medical devices are used. The treatment strategy 2032 may include a treatment strategy that is more likely to succeed based on past therapeutic medical procedures.

[0090] For example, machine learning model 2022 may be trained using data collected from past therapeutic medical procedures, such as imaging data, tracked motion of medical instruments, generator data, lesion classification, etc. Thus, machine learning model 2022 may be trained on actual treatments and actual outcomes from past therapeutic medical procedures, and treatment strategies may be included in treatment strategies 2032 based on treatment strategies that are more likely to result in successful outcomes.

[0091] For example, a k-means clustering model with multiple clusters can be used: one cluster for each specific treatment technique using one or more specific medical devices. Each identified lesion can be associated with a vector that includes, for example, the following variables: type of coronary artery problem, severity of coronary artery problem, complexity of coronary artery problem, location of coronary artery problem, classification of lesions, anatomical structure of the coronary artery problem area, other anatomical structures, patient's comorbidities, cholesterol level, blood pressure, blood oxygen, age, physical exercise level, etc. The position of the vector in a given cluster can indicate a specific treatment using one or more specific medical devices. For example, if the vector falls within a cluster of antegrade anatomical reentry (ADR) using a specific medical device, the machine learning model 2022 can include ADR as a treatment technique in the treatment strategy 2032, and can include the specific medical device in the treatment strategy 2032.

[0092] Alternatively, the k-means clustering algorithm may have multiple clusters, one for each classification of lesions. Each treatment strategy may be associated with a vector that includes, for example, the following variables: type of coronary problem, severity of coronary problem, complexity of coronary problem, location of coronary problem, anatomical structure of the coronary problem area, other anatomical structures, patient's comorbidities, cholesterol level, blood pressure, blood oxygen, age, physical exercise level, etc.

[0093] Other potential machine learning or artificial intelligence techniques that can be used include Naive Bayes, k-nearest neighbors, random forests, support vector machines, neural networks, linear regression, logistic regression, etc.

[0094] The lesion classification 2030 may be determined by the processing circuit 2004 executing a computer vision model. For example, the computer vision model 2024 may be trained to recognize characteristics of lesions and classify lesions based on the characteristics of the lesions, with lesions having the same characteristics being classified as the same. For example, the computer vision model 2024 may include a convolutional neural network (CNN) that may extract characteristics or features of the lesions to form a vector based on the extracted characteristics. Such a vector may be used to classify the lesion based on other lesions on which the computer vision model 2024 was trained. Although the use of a CNN is described, other computer vision models may also be used.

[0095] Processing circuit 2004 may execute user interface 2018 to cause display 2006 (and / or Figure 1 The memory 2002 may also store a machine learning model 2022, a computer vision module 2024, and a user interface 2018.

[0096] The processing circuit 2004 may be implemented by one or more processors, which may include any number of fixed-function circuits, programmable circuits, or combinations thereof. In various examples, the control of any function by the processing circuit 2004 may be implemented directly or in combination with any suitable electronic circuit system suitable for the specified function. A fixed-function circuit refers to a circuit that provides a specific functionality and is preset on an executable operation. A programmable circuit refers to a circuit that can be programmed to perform various tasks and provide flexible functionality in an executable operation. For example, a programmable circuit may execute software or firmware that causes the programmable circuit to operate in a manner defined by the instructions of the software or firmware. A fixed-function circuit may execute software instructions (e.g., receive parameters or output parameters), but the type of operation performed by the fixed-function circuit is generally immutable. In some examples, one or more of the units may be different circuit blocks (fixed-function or programmable), and in some examples, the one or more units may be integrated circuits.

[0097] Instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), general-purpose microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), graphics processing units (GPUs), or other equivalent integrated or discrete logic circuit systems. Therefore, the term "processing circuit 2004" as used herein may refer to one or more processors having any of the aforementioned processors or processing structures or any other structure suitable for implementing the techniques described herein. In addition, in some aspects, the functionality described herein may be provided within a dedicated hardware or software module configured for encoding and decoding, or incorporated into a combined codec. In addition, these techniques may be fully implemented in one or more circuits or logic elements.

[0098] Display 2006 can be touch-sensitive or voice-activated, so that display 2006 can serve as both an input device and an output device. Alternatively, a keyboard (not shown), a mouse (not shown) or other data input device (e.g., input device 2010) may be employed.

[0099] The network interface 2008 may be adapted to connect to a network, such as a local area network (LAN), a wide area network (WAN), a wireless mobile network, a Bluetooth network, or the Internet, including a wired network or a wireless network. In some examples, the network interface 2008 may include one or more application programming interfaces (APIs) for facilitating communication with other devices. For example, the computing device 2000 may receive imaging data 2014 from the imager 1040 via the network interface 2008 during a therapeutic medical procedure. The computing device 2000 may also receive controller data 2020 from the energy generating device 1054 via the network interface 2008. In some examples, the computing device 2000 may receive motion data 2028 from, for example, the EM field generator 1021 via the network interface 2008. The computing device 2000 may receive updates to its software (e.g., the application 2016) via the network interface 2008. The computing device 2000 may also display a notification on the display 2006 that a software update is available.

[0100] Input device 2010 may be any device that enables a user to interact with computing device 2000, such as, for example, a mouse, keyboard, foot pedal, touch screen, augmented reality input device that receives input such as gestures or body movements, or a voice interface.

[0101] The output device 2012 may include any connection port or bus, such as, for example, a parallel port, a serial port, a universal serial bus (USB), or any other similar connection port known to those skilled in the art.

[0102] The application 2016 may be one or more software programs stored in the memory 2002 and executed by the processing circuit 2004 of the computing device 2000. The processing circuit 2004 may execute a user interface 218 that may be displayed on the display 2006 and / or the display device 1010 ( Figure 1 ) displays imaging data 2014, ablated tissue representations, lesion classifications 2030, and / or treatment strategies 2032. Imaging data 2014 and / or ablated tissue representations may be stored for future use, such as training and / or performance evaluation of clinicians performing therapeutic medical procedures. In some examples, processing circuitry 2004 may communicate with server 1060 ( Figure 1 ) communications to upload imaging data during or after a therapeutic medical procedure 2014.

[0103] In some examples, processing circuit 2004 can provide real-time clinical guidance to clinicians. For example, processing circuit 2004 can use or execute computer vision model 2024 to determine the characteristics of the lesion and / or determine the location of the lesion, and execute machine learning model 2022 to provide a recommended treatment strategy to the clinician.

[0104] Figure 3 is a block diagram of an example energy generating device according to one or more aspects of the present disclosure. Figure 3 The energy generating device 3000 may be the energy generating device 1054 ( Figure 1 ). Figure 2 As shown in , the energy generating device 3000 may include a positive terminal (+) 3012 , a negative terminal (−) 3014 , an energy generator 3002 , a processing circuit 3004 , a user interface 3006 , a storage device 3008 , and a network interface 3020 .

[0105] The positive terminal 3012 may be coupled to the energy generator 3002 and may be configured to be attached to one or more conductors of an ablation catheter (not shown) to conduct electricity between the energy generator 3002 and the one or more conductors. The negative terminal 3014 may be coupled to the energy generator 3002 (or alternatively grounded) and may be configured to be attached to one or more conductors of the ablation catheter to conduct electricity between the one or more conductors and the energy generator 3002. The energy generator 3002 may be configured to provide radio frequency electrical pulses to one or more conductors of the ablation catheter to perform an electroporation procedure or other ablation procedure on a lesion (such as a vascular lesion, a cardiac lesion, or other tissue in a patient's body, such as renal tissue, airway tissue, and organs or tissue within the cardiac space or pericardial space). Although in Figure 3 In the example of , a single energy generator is shown, but the energy generating device 3000 is not limited thereto. For example, the energy generating device 3000 may include multiple energy generators, each of which is capable of generating ablation signals in parallel. In some examples, the energy generating device 3000 may include different types of energy generators, such as a radio frequency energy generator (such as an RDN generator), a pulsed field energy generator, and / or a cryogenic energy generator.

[0106] The processing circuit 3004 may include one or more processors, such as any one or more of the following: a microprocessor, a controller, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a discrete logic circuit system, or any other processing circuit configured to provide the functionality attributed to the processing circuit 3004, which may be embodied herein as firmware, hardware, software, or any combination thereof. The processing circuit 3004 controls the energy generator 3002 to generate signals according to various settings 3010 (which settings may be stored in the storage device 3008).

[0107] The storage device 3008 may be configured to store controller data 3016 within the energy generating device 3000 during operation. The controller data 3016 may be an example of the controller data 2020, and may include the amount of energy delivered during ablation, the amount of time to deliver energy, error codes, etc. The storage device 3008 may include a computer-readable storage medium or a computer-readable storage device. In some examples, the storage device 3008 includes one or more of a short-term memory or a long-term memory. The storage device 3008 may include, for example, a random access memory (RAM), a dynamic random access memory (DRAM), a static random access memory (SRAM), a magnetic disk, an optical disk, a flash memory, or various forms of electrically programmable memory (EPROM) or electrically erasable programmable memory (EEPROM). In some examples, the storage device 3008 is used to store data indicating instructions executed, for example, by the processing circuit 3004.

[0108] The user interface 3006 may include buttons or a keypad, lights, a speaker / microphone for voice commands, and / or a display (such as a liquid crystal (LCD), a light emitting diode (LED), or an organic light emitting diode (OLED). The user interface 3006 may be configured to receive input from a clinician, such as selecting settings from settings 3010 for use during an ablation therapy session. In some examples, the display may be configured to display information about the ongoing ablation therapy session, such as patient parameters or other information that may be useful to the clinician.

[0109] Figure 4 is a conceptual diagram showing a representation of ablated tissue overlaid on imaging data in accordance with one or more aspects of the present disclosure. Figure 4 may be displayed during a therapeutic medical procedure by the user interface 2018 ( Figure 2 ) on the display 2006 and / or the display device 1010 ( Figure 1 ) is an example of information displayed on the processing circuit 2004 ( Figure 2 ) may generate and output a representation of the ablation catheter 4002 (which in some examples may be included in the imaging data 2014 ( Figure 2 ) and ablated tissue 4000 for display, which processing circuitry 2004 may overlay on imaging data 4010 (which may be an example of imaging data 2014). Imaging data 4010 may include tissue 4012 surrounding ablation catheter 4002. In some examples, processing circuitry 2004 may represent ablated tissue 4000 in a different manner than imaging data 4010 (e.g., in a different color, different shading, different pattern fill, etc.) so that ablated tissue 4000 may be viewed by a clinician on a display such as display 2006 ( Figure 2 ) or display device 1010 ( Figure 1 ) on the display.

[0110] Figure 5 is a flow chart illustrating an example technique for determining characteristics of a lesion according to one or more aspects of the present disclosure. Figure 2 The computing device 2000 is described, but Figure 5 The technology can be provided by system 1000 ( Figure 1 ) or any combination of devices of system 1000 capable of performing such techniques. Processing circuit 2004 may receive imaging data (5000) of at least a portion of a patient's vasculature generated during a cardiac catheterization procedure. For example, processing circuit 2004 may receive imaging data (5000) of at least a portion of a patient's vasculature from imager 1040 ( Figure 1 ) receives imaging data 2014 of at least a portion of a patient's vasculature generated during a diagnostic medical procedure or a therapeutic medical procedure. Processing circuitry 2004 may execute at least one computer vision model to determine characteristics of a lesion in the vasculature based on the received imaging data (5002). For example, processing circuitry 2004 may execute computer vision model 2024 to determine characteristics of a lesion appearing in imaging data 2014. In some examples, processing circuitry 2004 may execute at least one computer vision model to determine, for example, a degree of success in ablating a lesion during a therapeutic medical procedure. For example, processing circuitry 2004 may execute computer vision model 2024 to examine tissue surrounding an ablation and determine a degree of success of the ablation.

[0111] In some examples, the computer vision model is trained for multiple lesions in past imaging data for multiple patients. In some examples, processing circuit 2004 may execute at least one computer vision learning model to determine a medical device type for a medical device used during a cardiac catheterization procedure. For example, processing circuit 2004 may execute computer vision model 2024 to determine medical device 1030 ( Figure 1 ) or the brand, model, and / or other information of any other medical device used during the cardiac catheterization procedure. In some examples, at least one computer vision model is trained on post-ablation information in past imaging data from multiple patients, and processing circuit 2004 can execute computer vision model 2024 to determine the degree of success of lesion ablation.

[0112] In some examples, processing circuit 2004 may track the motion of a medical device during a cardiac catheterization procedure and output for display a representation of the motion of the medical device during the cardiac catheterization procedure based on the tracked motion and imaging data. For example, processing circuit 2004 may receive motion data 2028 (e.g., from EM field generator 1021 ( Figure 1)) or by executing a computer vision model 2024 on the imaging data 2014 to generate motion data 2028 to track the motion of the medical device 1030. The processing circuit 2004 may output a representation of the motion of the medical device based on the motion data 2028 and the imaging data 2014 (e.g., see Figure 4 In some examples, the processing circuit 2004 may determine whether at least a portion of the cardiac catheterization procedure was successful based on the tracked motion.

[0113] In some examples, the processing circuit 2004 may execute the machine learning model 2022 to guide the clinician during the cardiac catheterization procedure. For example, the processing circuit 2004 may output to the display 2006 a representation of the path to be followed by the clinician, one or more techniques to be adopted, one or more medical devices to be used, the order of medical devices to be used, etc. In some examples, the processing circuit 2004 outputs guidance to the clinician based on the characteristics of the lesion during the cardiac catheterization procedure. In some examples, the processing circuit 2004 outputs guidance to the clinician based on the identity of the clinician during the cardiac catheterization procedure. In some examples, the processing circuit 2004 predicts the ability of the medical device to pass through the lesion based at least in part on the characteristics of the lesion. For example, the processing circuit 2004 may execute the machine learning model 2022 to predict the ability of a medical device (e.g., medical device 1030) to pass through the lesion. In some examples, the processing circuit 2004 determines whether the medical device passes through the lesion. In some examples, at least one machine learning model is trained on data collected from past therapeutic medical procedures, the data comprising at least one of past imaging data, past tracked motion of a medical device, past controller data, or past lesion classifications.

[0114] In some examples, processing circuit 2004 may receive a signal from a device (e.g., energy generating device 1054 ( Figure 1 )) receives controller data 2020. Processing circuit 2004 may process controller data 2020 to generate a representation of ablated tissue. Processing circuit 2004 may output imaging data 2014 and the representation of ablated tissue for display.

[0115] In some examples, processing circuitry 2004 applies one or more timestamps to imaging data 2014 and one or more timestamps to at least one of controller data 2020 or the representation of ablated tissue prior to outputting the imaging data 2014 and the representation of ablated tissue for display. As part of outputting the imaging data 2014 and the representation of ablated tissue for display, processing circuitry 2004 may register imaging data 2014 using the one or more timestamps applied to imaging data 2014 and the one or more timestamps applied to at least one of controller data 2020 or the representation of ablated tissue. Processing circuitry 2004 may overlay the representation of ablated tissue on the imaging data.

[0116] In some examples, the processing circuit 2004 may control the energy generating device 1054 to stop delivering energy based on the controller data 2020. In some examples, the processing circuit 2004 may control the automatic contrast agent delivery device 1090 to adjust the contrast agent delivery rate of the contrast agent to the patient based on at least one of the quality of the imaging data or in response to the patient's tolerance for contrast agents.

[0117] Figure 6 6000 is a schematic perspective view of an example of a system for determining a treatment strategy according to one or more aspects of the present disclosure. System 6000 includes a display device 6010, a table 6020, an imager 6040, and a computing device 6050. System 6000 may be an example of a system for use in a Cath lab. In some examples, system 6000 may include other devices, such as Figure 1 For simplicity, Figure 6 Such devices are not shown in the figure. In some examples, the system 6000 may also include a server 6060 and / or a computing device 6052 that may be located in the Cath lab or elsewhere. The system 6000 may be used to diagnose a patient's cardiovascular problem during a diagnostic session. During such a diagnostic session (e.g., a diagnostic angiogram), there are three possible results. The first possible result is that the clinician may determine that intervention is not required. The second possible result is that the clinician may determine that emergency intervention is required, and the clinician may handle the intervention during the same session (e.g., without the patient leaving and then returning again). The third possible result is that treatment is required, but either the clinician feels uncomfortable when performing the treatment, or the hospital where the Cath lab is located does not have the necessary equipment to perform the treatment. In the case of the third possible result, there may be imaging data (e.g., angiography data) from a diagnostic medical procedure, which can be used to plan treatment for the patient. For example, the system can use such imaging data to plan or help the clinician plan treatment.

[0118] Such a system may include a computer vision model and a machine learning model. The computer vision model may be used to identify, classify, and / or score specific lesions. The machine learning model may be used to determine potential treatments that are most likely to achieve a successful outcome, and such potential treatment strategies may be presented to a clinician to plan treatment for a therapeutic medical procedure. The system may be configured to run simulations on potential treatment strategies to help the clinician select one or more treatment strategies to use during a therapeutic medical procedure.

[0119] The computing device 6050 may include, for example, an off-the-shelf device such as a laptop computer, desktop computer, tablet computer, smart phone, or other similar device, or may include a dedicated device. The computing device 6050 may perform various control functions with respect to the imager 6040. In some examples, the computing device 6050 may include a boot workstation such as Figure 1 The computing device 6050 may control the operation of the imager 6040 and receive the output of the imager 6040.

[0120] Display device 6010 may be configured to output instructions, images, and messages related to the performance of a diagnostic medical procedure.Table 6020 may be, for example, a surgical table or other table suitable for use in a medical procedure such as a diagnostic medical procedure.

[0121] exist Figure 6 In the example of , an imager 6040 such as an angiographic imager or other imaging device can be used to image a patient's body during a diagnostic medical procedure to visualize the characteristics and location of lesions inside the patient's body. Although primarily described as an angiographic imager, the imager 6040 can be any type of imaging device, such as an IVUS device, a CT device, an MRI device, a fluoroscopy device, a PET device, an ultrasound device, etc.

[0122] The imager 6040 may image a region of interest in the patient's body. The specific region of interest may depend on the anatomy, diagnostic medical procedure, and / or intended therapy. For example, when performing a diagnostic medical procedure for a cardiovascular problem, a portion of the vasculature and / or the heart may be the region of interest.

[0123] The computing device 6050 may be communicatively coupled to the imager 6040, the display device 6010, the computing device 6052, and / or the server 6060, for example, by wired, optical, or wireless communications. The server 6060 may be a hospital server that may or may not be located in a hospital's catheterization lab, a cloud-based server, etc. The server 6060 may be configured to store patient imaging data, electronic health or medical records, etc.

[0124] The computing device 6052 may include, for example, an off-the-shelf device such as a laptop computer, desktop computer, tablet computer, smart phone, or other similar device, or may include a dedicated device. The computing device 6052 may be a hospital computing device (e.g., owned by the hospital) or may be a clinician's personal computing device.

[0125] Any one or any combination of computing device 6050, computing device 6052, and / or server 6060 may include at least one computer vision model and / or at least one machine learning model. For example, computing device 6050, computing device 6052, and / or server 6060 may receive diagnostic imaging data. Computing device 6050, computing device 6052, and / or server 6060 may execute at least one computer vision model to determine characteristics of lesions in the received diagnostic imaging data. Computing device 6050, computing device 6052, and / or server 6060 may execute at least one machine learning model to determine at least one treatment strategy based on the determined lesion characteristics, the at least one treatment strategy including at least one treatment technique and at least one medical device. In some examples, at least one treatment strategy may also include information related to the placement of at least one medical device during a therapeutic medical procedure and / or the order in which at least one medical device is used during a therapeutic medical procedure. For example, computing device 6050, computing device 6052, and / or server 6060 may execute at least one machine learning model to determine one or more treatment strategies that have a higher probability of success than other treatment strategies. For example, at least one machine learning model may predict the probability of success of different treatment strategies based on previous therapeutic medical procedures. For example, at least one machine learning algorithm may analyze treatment strategies from previous therapeutic medical procedures for lesions with the same or similar characteristics. For example, a machine learning model may determine that without pre-dilation, electrohydraulic intravascular lithotripsy has a 40% chance of passing through the lesion, while a low-profile non-compliant (NC) balloon has a 75% chance of passing through the lesion. Computing device 6050, computing device 6052, and / or server 6060 may output at least one treatment technique, for example on display device 6010 or a display of computing device 6050 or computing device 6052, for display to a clinician.

[0126] In some examples, computing device 6050, computing device 6052, and / or server 6060 can be configured to run one or more simulations of treating a lesion using any of at least one treatment strategy in response to input from a clinician. In some examples, computing device 6050, computing device 6052, and / or server 6060 can overlay a simulation or a result of a simulation on actual imaging data in a display or a still image from the imaging data showing the lesion.

[0127] In some examples, computing device 6050, computing device 6052 and / or server 6060 may be configured to receive user input of at least one selected treatment technique and at least one selected medical device, and computing device 6050, computing device 6052 and / or server 6060 may modify at least one treatment strategy accordingly. For example, after seeing at least one treatment strategy and possibly after running one or more simulations, a clinician may determine that a particular treatment strategy is the treatment strategy that the clinician intends to use and may delete any other suggested treatment strategies for the lesion. Additionally or alternatively, if one or more treatment techniques and / or one or more medical devices within the treatment strategy are changed, the clinician may determine that a particular treatment strategy may be a treatment strategy that the clinician can use. In this case, the clinician may change any treatment technique and / or medical device within the treatment strategy via a user interface. Additionally or alternatively, clinicians may prepare their own treatment strategy, which they may add to at least one treatment strategy or which they may replace at least one treatment strategy with. In some examples, computing device 6050, computing device 6052, and / or server 6060 can run further simulations using modified treatment strategies for treating lesions in response to clinician input. These techniques can be repeated iteratively until the clinician is satisfied with at least one resulting treatment strategy. In some examples, these techniques can be used to train researchers or can be used by clinicians to plan therapeutic medical procedures.

[0128] Such techniques may be useful because there are several different lesion types, such as bifurcation lesions, calcified lesions, chronic total occlusions (CTOs), in-stent restenosis (ISRs), left main disease, etc. For example, the Medina classification system includes seven different subtypes of bifurcation lesions. In addition, there are multiple treatment techniques for different types of lesions. For example, there are at least six techniques for treating bifurcation lesions, and these techniques may include the use of different medical devices and / or the use of medical devices in different sequences. Therefore, the number of different permutations of treatment strategies for a given lesion may be quite large.

[0129] By providing clinicians with one or more treatment strategies with the highest probability of success, such as for planning therapeutic medical procedures, the techniques of the present disclosure can enable specific treatment or prevention of a disease or medical condition. These techniques can improve patient outcomes, reduce the need for repeat therapeutic medical procedures, expedite therapeutic medical procedures, reduce patient exposure to radiocontrast agents, and / or conserve medical resources.

[0130] Figure 7 is a schematic diagram of an example of a computing device according to one or more aspects of the present disclosure. The computing device 7000 may be Figure 6Examples of computing device 6050, computing device 6052, and / or server 6060 may include a workstation, a desktop computer, a laptop computer, a server, a smart phone, a tablet computer, a dedicated computing device, or any other computing device capable of performing the techniques of the present disclosure.

[0131] Computing device 7000 may include, for example, memory 7002, processing circuitry 7004, a display 7006, a network interface 7008, an input device 7010, and / or an output device 7012, each of which, for ease of description, may represent any one of multiple instances of such devices within a computing system.

[0132] Although the processing circuit 7004 appears Figure 7 7000 in the computing device 7000, but in some examples, the features attributed to the processing circuit 7004 may be provided by the system 6000 ( Figure 6 ) or any combination thereof. In some examples, one or more processors associated with processing circuit 7004 in computing device 7000 may be distributed and shared across any combination of computing device 6050, computing device 6052, and server 6060. Additionally, in some examples, processing operations or other operations performed by processing circuit 7004 may be performed by one or more processors residing remotely (such as one or more cloud servers or processors), each of which may be considered part of computing device 7000. Computing device 7000 may be used to perform any of the techniques described in this disclosure, and may form all or part of a device or system configured to perform such techniques, alone or in combination with other components (such as components of computing device 6050, computing device 6052, server 6060, or a system including any or all of such devices).

[0133] The memory 7002 of the computing device 7000 includes any non-transitory computer-readable storage medium for storing data or software that can be executed by the processing circuit 7004 and control the operation of the computing device 7000. In one or more examples, the memory 7002 may include one or more solid-state storage devices, such as flash memory chips. In one or more examples, the memory 7002 may include one or more mass storage devices connected to the processing circuit 7004 through a mass storage controller (not shown) and a communication bus (not shown).

[0134] Although the description of computer-readable media herein refers to solid-state storage devices, it should be understood by those skilled in the art that computer-readable storage media can be any available media that can be accessed by the processing circuit 7004. That is, computer-readable storage media include non-transitory, volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information such as computer-readable instructions, data structures, program modules or other data. For example, computer-readable storage media include RAM, ROM, EPROM, EEPROM, flash memory or other solid-state memory technology, CD-ROM, DVD, Blu-ray or other optical storage devices, cassettes, magnetic tapes, magnetic disk storage devices or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by the computing device 7000. In one or more examples, the computer-readable storage medium can be stored in a cloud or remote storage device and accessed through at least one of a wired or wireless connection using any suitable one or more technologies.

[0135] Memory 7002 may store machine learning model 7022 and / or computer vision model 7024. In some examples, machine learning model 7022 and computer vision model 7024 may be the same. In other examples, machine learning model 7022 and computer vision model 7024 may be different.

[0136] The memory 7002 may store imaging data 7014. The imaging data 7014 may be acquired by the imager 6040 ( Figure 6 ) is captured during a diagnostic medical procedure on a patient. Processing circuit 7004 may receive imaging data 7014 from imager 6040 and store imaging data 7014 in memory 7002.

[0137] The memory 7002 may also store lesion classifications 7030, such as classifications of lesions that appear in the imaging data 7014. The processing circuit 6004 executing the computer vision model 7024 may determine the classifications of lesions that may be stored in the lesion classifications. For example, the processing circuit 6004 may classify lesions based on characteristics of the lesions, as described above with respect to Figure 1 to Figure 2As discussed. The memory 7002 may also store a treatment strategy 7032. The processing circuit 7004 executing the machine learning model 7022 may determine a treatment strategy for presentation to a clinician, for example, to help plan a therapeutic medical procedure or for training purposes. The treatment strategy 7032 may include one or more treatment techniques and one or more medical devices for a therapeutic medical procedure. Generally speaking, the treatment strategy 7032 includes one or more of the use of diagnostic catheters, plain old balloon angioplasty (POBA), mechanical atherectomy, intravascular lithotripsy (IVL), drug-coated balloon angioplasty, stent delivery (including bare metal stents, drug-eluting stents (DES), bioresorbable stents, etc.), post-stent implantation optimization, wire-based FFR or other blood flow reserve metrics, image-based FFR or other blood flow reserve metrics, OCT, IVUS, etc. In some examples, the treatment strategy 7032 may also include the location of one or more medical devices during the therapeutic medical procedure and / or the order of use of one or more medical devices. The treatment strategy 7032 may include a treatment strategy that is more likely to succeed based on past therapeutic medical procedures.

[0138] For example, machine learning model 7022 may be trained using data collected from past therapeutic medical procedures, such as imaging data, tracked motion of medical instruments, generator data, lesion classification, etc. Thus, machine learning model 7022 may be trained on actual treatments and actual outcomes from past therapeutic medical procedures, and treatment strategies may be included in treatment strategies 7032 based on treatment strategies that are more likely to result in successful outcomes.

[0139] For example, a k-means clustering model with multiple clusters can be used: one cluster for each specific treatment technique using one or more specific medical devices. Each identified lesion can be associated with a vector that includes, for example, the following variables: type of coronary artery problem, severity of coronary artery problem, complexity of coronary artery problem, location of coronary artery problem, classification of lesions, anatomical structure of the coronary artery problem area, other anatomical structures, patient's comorbidities, cholesterol level, blood pressure, blood oxygen, age, physical exercise level, etc. The position of the vector in a given cluster can indicate a specific treatment using one or more specific medical devices. For example, if the vector falls within a cluster of antegrade anatomical reentry (ADR) using a specific medical device, the machine learning model 7022 can include ADR as a treatment technique in the treatment strategy 7032, and can include the specific medical device in the treatment strategy 7032.

[0140] Alternatively, the k-means clustering algorithm may have multiple clusters, one for each classification of lesions. Each treatment strategy may be associated with a vector that includes, for example, the following variables: type of coronary problem, severity of coronary problem, complexity of coronary problem, location of coronary problem, anatomical structure of the coronary problem area, other anatomical structures, patient's comorbidities, cholesterol level, blood pressure, blood oxygen, age, physical exercise level, etc.

[0141] Other potential machine learning or artificial intelligence techniques that can be used include Naive Bayes, k-nearest neighbors, random forests, support vector machines, neural networks, linear regression, logistic regression, etc.

[0142] The lesion classification 7030 may be determined by the processing circuit 7004 executing a computer vision model. For example, the computer vision model 7024 may be trained to recognize characteristics of lesions and classify lesions based on the characteristics of the lesions, with lesions having the same (or nearly the same) characteristics being classified as the same. For example, the computer vision model 7024 may include a convolutional neural network (CNN) that may extract characteristics or features of the lesions to form a vector based on the extracted characteristics. Such a vector may be used to classify the lesion based on other lesions on which the computer vision model 7024 was trained. Although the use of a CNN is described, other computer vision models may also be used.

[0143] The processing circuit 7004 may execute the user interface 7018 to cause the display 7006 (and / or Figure 6 The display device 6010 of the machine learning model 7022 may present a user interface 7018 to a clinician or clinician-in-training who is preparing a therapeutic medical procedure. In some examples, the user interface 7018 may display, for example, on the display 7006, a treatment strategy 7032 that the processing circuitry executing the machine learning model 7022 may determine has a better chance of a successful outcome than other treatment strategies for a particular classification of the patient's lesions.

[0144] In some examples, the clinician may desire to run a simulation for at least one of the treatment strategies 7032. In this case, the clinician may provide user input via the user interface 7018 or input device 7010 indicating that the processing circuitry 7004 should run such a simulation for the selected one of the treatment strategies 7032. The processing circuitry 7004 may then load the simulation application 7016 from memory and execute the simulation application 7016 for the selected treatment strategy.

[0145] In some examples, the clinician may desire to delete certain treatment strategies from the treatment strategies 7032, change certain aspects of a given treatment strategy (e.g., one or more treatment techniques, medical devices, locations of medical devices, and / or the order in which medical devices are used), or create a treatment strategy that is not in the treatment strategies 7032. In this case, the clinician can provide user input via the user interface 7018 or input device 7010 indicating that the clinician wants to modify the treatment strategy 7032, and the processing circuit 7004 can assist the clinician in modifying the treatment strategy 7032. The memory 7002 can also store the machine learning model 7022, the computer vision module 7024, and the user interface 7018.

[0146] The processing circuit 7004 may be implemented by one or more processors, which may include any number of fixed-function circuits, programmable circuits, or combinations thereof. In various examples, the control of any function by the processing circuit 7004 may be implemented directly or in combination with any suitable electronic circuit system suitable for the specified function. A fixed-function circuit refers to a circuit that provides a specific functionality and is preset on an executable operation. A programmable circuit refers to a circuit that can be programmed to perform various tasks and provide flexible functionality in an executable operation. For example, a programmable circuit may execute software or firmware that enables the programmable circuit to operate in a manner defined by the instructions of the software or firmware. A fixed-function circuit may execute software instructions (e.g., receive parameters or output parameters), but the type of operation performed by the fixed-function circuit is generally immutable. In some examples, one or more of the units may be different circuit blocks (fixed-function or programmable), and in some examples, the one or more units may be integrated circuits.

[0147] Instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), general-purpose microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), graphics processing units (GPUs), or other equivalent integrated or discrete logic circuit systems. Therefore, the term "processing circuit 7004" as used herein may refer to one or more processors having any of the aforementioned processors or processing structures or any other structure suitable for implementing the techniques described herein. In addition, in some aspects, the functionality described herein may be provided within a dedicated hardware or software module configured for encoding and decoding, or incorporated into a combined codec. In addition, these techniques may be fully implemented in one or more circuits or logic elements.

[0148] Display 7006 can be touch-sensitive or voice-activated, so that display 7006 can serve as both an input device and an output device. Alternatively, a keyboard (not shown), a mouse (not shown) or other data input device (e.g., input device 7010) may be employed.

[0149] The network interface 7008 may be adapted to connect to a network, such as a local area network (LAN), a wide area network (WAN), a wireless mobile network, a Bluetooth network, or the Internet, including a wired network or a wireless network. In some examples, the network interface 7008 may include one or more application programming interfaces (APIs) for facilitating communication with other devices. For example, the computing device 7000 may receive imaging data 7014 from the imager 6040 via the network interface 7008 during or after a diagnostic medical procedure. The computing device 7000 may receive updates to its software (e.g., application 7016) via the network interface 7008. The computing device 7000 may also display a notification on the display 2006 that a software update is available.

[0150] The input device 7010 may be any device that enables a user to interact with the computing device 7000, such as, for example, a mouse, a keyboard, a foot pedal, a touch screen, an augmented reality input device that receives input such as gestures or body movements, or a voice interface.

[0151] The output device 7012 may include any connection port or bus, such as, for example, a parallel port, a serial port, a universal serial bus (USB), or any other similar connection port known to those skilled in the art.

[0152] Application 7016 may be one or more software programs stored in memory 7002 and executed by processing circuit 7004 of computing device 7000. Processing circuit 7004 may execute user interface 7018, which may be displayed on display 7006 and / or display device 6010 ( Figure 6 ) displays treatment strategy 7032, simulation, lesion classification 7030 and / or imaging data 7014.

[0153] Figure 8 is a flow chart illustrating an example technique for determining a treatment strategy according to one or more aspects of the present disclosure. Figure 7 The computing device 7000 is described, but Figure 7 The technology can be used by System 6000 ( Figure 6 ) or any combination of devices of system 6000 capable of performing such techniques. Processing circuit 7004 may receive diagnostic imaging data (8000) of at least a portion of a patient's vasculature generated during a cardiac diagnostic medical procedure. For example, processing circuit 7004 may receive imaging data 7014 from imager 6040, computing device 6050, server 6060, computing device 6052, or from a memory (e.g., a thumb drive).

[0154] Processing circuit 7004 may execute at least one computer vision model based on the received diagnostic imaging data to determine a characteristic of a lesion in a vasculature (8002). For example, processing circuit 7004 may execute computer vision model 7024 to determine a characteristic of a lesion in imaging data 7014. Processing circuit 7004 may execute at least one machine learning model based on the determined characteristic of the lesion to determine at least one treatment strategy, the at least one treatment strategy comprising at least one treatment technique and at least one medical device (8004). For example, processing circuit 7004 may execute machine learning model 7022 based on lesion classification 7030 to determine treatment strategy 7032.

[0155] In some examples, the processing circuit 7004 is further configured to output a treatment strategy (which may be a single treatment strategy) 7032 for display. In some examples, the treatment strategy 7032 further includes an indication of a predicted degree of success of the use of at least one treatment technique and at least one medical device that is likely to be successful. In some examples, the processing circuit 7004 is further configured to perform a first simulation of a first medical procedure using at least one treatment strategy in response to user input. For example, the processing circuit 7004 may load a simulation application 7016 and execute the simulation application 7016 to simulate the use of a treatment strategy selected by a clinician to simulate a lesion. In some examples, the simulation is based at least in part on the received diagnostic imaging data (e.g., imaging data 7014).

[0156] In some examples, the processing circuit 7004 may receive user input of the selected at least one treatment technique. The processing circuit 7004 may be configured to receive user input to modify the selected at least one treatment strategy and modify the selected at least one treatment strategy (e.g., treatment strategy 7032) based on the user input to generate at least one modified treatment strategy.

[0157] In some examples, processing circuit 7004 may perform a second simulation of a second medical procedure using at least one modified treatment strategy. In some examples, the at least one modified treatment strategy includes at least one of the selected at least one treatment technique and / or at least one medical device. In some examples, the at least one treatment strategy does not include at least one of the selected at least one treatment technique or at least one of the selected medical devices.

[0158] In some examples, machine learning model 7022 is trained on imaging data from previous therapeutic medical procedures, multiple lesion types, multiple medical devices, multiple clinicians, and multiple therapeutic medical procedures.

[0159] Fig. 9 is a schematic perspective view of an example medical system 9000. The medical system 9000 may be Figure 1 Medical Systems 1000 and / or Figure 6 Example of a medical system 6000. Fig. 9 The Medical System 9000 is similar to Figure 6 The medical system 6000 of the present invention is different from the medical system 6000 described below, wherein like reference numerals indicate like elements. The medical system 9000 may provide a system for establishing a communication session between an operating clinician and a remote clinician and streaming imaging data representing a medical procedure to the remote clinician for consultation.

[0160] System 9000 includes a display device 9010, a table 9020, an imager 9040, a first computing device 9050, a server 9060, a network 9056, and a second computing device 9058. System 9000 may be an example of a system for use in a Cath lab. In some examples, system 9000 may include other devices, such as Figure 1 The additional devices depicted in the figure are not shown in the figure for the sake of simplicity. Fig. 9 . System 9000 can be used to diagnose cardiovascular problems in patients during diagnostic sessions. As discussed above, during such diagnostic sessions (e.g., diagnostic angiography), there are three possible outcomes. The first possible outcome is that the clinician may determine that intervention is not required. The second possible outcome is that the clinician may determine that urgent intervention is required, and the clinician can handle the intervention during the same session (e.g., without the patient leaving and then returning again). The third possible outcome is that treatment is required, but either the clinician feels uncomfortable when performing the treatment, or the hospital where the Cath lab is located does not have the necessary equipment to perform the treatment. In the case of the third possible outcome, there may be imaging data (e.g., angiography data) from diagnostic medical procedures, which can be used to plan treatment for the patient. System 9000 can move the case from the third possible outcome to the second possible outcome by allowing a consultation from a remote clinician, which can make the clinician in operation more comfortable when performing the procedure, and therefore be able to provide the necessary medical treatment to the patient during the same session.

[0161] The first computing device 9050 may be associated with a first clinician who may be located in a Cath Lab during a medical procedure. The first computing device 9050 may be computing device 6050 or 6052 ( Figure 6), and may include, for example, off-the-shelf devices such as laptop computers, desktop computers, tablet computers, smart phones, or other similar devices. The first computing device 9050 includes a memory 9002 and a processing circuit 9004. The second computing device 9058 may be associated with a second clinician located remotely. For example, the second computing device may be associated with a second clinician located in another part of the hospital, a specialist call center, at home, on vacation, etc. Similar to the first computing device 9050, the second computing device 9058 may include, for example, off-the-shelf devices such as laptop computers, desktop computers, tablet computers, smart phones, or other similar devices.

[0162] Although processing circuit 9004 appears Fig. 9 9050, but in some examples, features attributed to processing circuit 9004 may be performed by processing circuits of any of the first computing device 9050, the second computing device 9058, the imager 9040, the server 9060, the network 9056, other elements of the system 9000, or a combination thereof. In some examples, one or more processors associated with processing circuit 9004 in the first computing device 9050 may be distributed and shared across any combination of the first computing device 9050, the second computing device 9058, the imager 9040, the server 9060, the network 9056, and the display device 9010. Additionally, in some examples, processing operations or other operations performed by processing circuit 9004 may be performed by one or more processors residing remotely (such as one or more cloud servers or processors), each of which may be considered to be part of computing device 9050.

[0163] System 9000 includes network 9056, which is a suitable network, such as a local area network (LAN), a wide area network (WAN), a wireless mobile network, a Bluetooth network, or the Internet, including a wired network or a wireless network. In some examples, network 9056 can be a secure network, such as a hospital network, which can restrict user access.

[0164] The processing circuit 9004 is communicatively coupled to a first computing device 9050 operated by a clinician or assistant in a Cath Lab and a second computing device 9058 associated with a remotely located second clinician. In some examples, the first clinician may be performing a medical procedure such as a cardiac catheterization laboratory procedure, and may encounter an uncomfortable patient condition (e.g., one or more lesions with a particular complexity) that they are treating. In this case, the first clinician may input a request through a user interface at a display device 9010, and the computing device 9050 may forward a representation of a user input requesting a consultation from a second clinician to a second computing device 9058 associated with the second clinician, the second computing device being located elsewhere as discussed above. In response to receiving a representation of a user input requesting a consultation, a communication session may be established between the first computing device 9050 and the second computing device 9058. During the communication session, the system 9000 may be configured to stream a representation of data captured by the imager 9040 of the medical procedure. As discussed above, the representation of data captured by the imager 9040 may be a photo or video data.

[0165] As discussed above, the imager 9040 may be an angiographic imager or other imaging device and may be used to image the patient's body during a procedure to visualize the characteristics and location of lesions inside the patient's body. The imager 9040 may be any type of imaging device, such as a CT device, an MRI device, a fluoroscopic device, a PET device, an ultrasound device, and the like. As part of the normal operation of one or more of these imaging devices, the one or more imaging devices may capture personal health information (PHI) from the patient that may not be approved to be shared with certain clinicians who may serve as second, remote clinicians. For example, a first clinician may request a consultation with a former attending physician, alumni, professor, and the like who is not affiliated with the hospital and therefore is not approved to observe PHI associated with the patient. In some examples, the processing circuit 9004 may be configured to: determine the permission status of the second clinician; and, prior to streaming and in response to determining that the permission status of the second clinician is below a threshold permission status, edit the personal health information from a representation of the data captured by one or more image sensors, as will be described below with respect to Fig.13A and Fig. 13B described in further detail.

[0166] In some examples, processing circuit 9004 can help the first clinician determine when and whether to request a consultation from the second clinician. For example, processing circuit 9004 can be configured to execute, for example, computer vision model 2024 ( Figure 2 ) and / or machine learning models 2022( Figure 2) to identify a patient condition or an intra-procedural event in a representation of data captured by the imager 9040. In response to identifying a patient condition or an intra-procedural event (e.g., a malfunctioning or damaged tool or instrument, a method of approaching a lesion, etc.), the processing circuit 9004 may present a user interface at the display device 9010 that includes an option to request a consultation from the second computing device 9058 from the first clinician.

[0167] Although the second computing device 9058 is described above as a single computing device associated with a single remotely located second clinician, in some examples, the processing circuit 9004 can be configured to receive an indication of a request by the first clinician to message or call more than one second computing device associated with more than one second clinician. In this manner, the processing circuit 9004 can be configured to allow the clinician to contact multiple potential visiting clinicians during a medical procedure and receive a consultation even if one or more of the potential second clinicians are unavailable.

[0168] In some examples, the processing circuit 9004 may be configured to receive a note about the representation of the data from the first clinician through the first computing device 9050 or from one or both of the second clinicians through the second computing device 9058, and send the note to the other of the first clinician or the second clinician during the communication session. Such a note may include a note or mark indicating, for example, a suggested path. Additionally or alternatively, the processing circuit 9004 may be configured to capture data from one or more audio sensors (not shown) during the medical procedure and stream the data captured from the one or more audio sensors during the communication session. The audio sensor may be an off-the-shelf component of a laptop, tablet computer, mobile phone, etc., or may be a part of a Cath Lab.

[0169] In some examples, the processing circuitry 9004 may be configured to cause a display associated with the second computing device 9058 to display a list of medical devices available to the first clinician. The processing circuitry configured to share the list of instruments and tools available to the operating clinician may assist the second clinician in delivering the best possible advice or recommendation under the circumstances, given that the operating clinician may be in a non-state-of-the-art environment and may not have access to some or all of the best instruments and equipment when performing a medical procedure.

[0170] In some examples, processing circuit 9004 may be configured to link data from imager 9040 captured during a medical procedure to data from another medical test or procedure. Other medical tests or procedures may occur before or during a medical procedure that is streamed via a communication setting. For example, a patient may be undergoing percutaneous coronary intervention, the streamed medical procedure may be a cardiac catheterization procedure, and the data from another medical test or procedure may be data from a previous calcification test. In some examples, the calcification test may be a coronary artery calcium score, which may be generated by computed tomography (CT). In some examples, a CT scan may occur before a PCI procedure. In some examples, other medical tests or procedures may include data generated via intravascular ultrasound, which may be generated during a medical procedure in some examples. In some examples, other medical tests may include data generated during an echocardiogram. In an example where the medical procedure is a structural heart medical procedure, echocardiogram data may be particularly important.

[0171] Fig.10 is a flow chart illustrating an example technique for streaming a representation of data to a remote clinician according to one or more aspects of the present disclosure. Fig. 9 The system 9000 is described, but Fig. 9 The techniques may be used by other systems, such as system 1000 ( Figure 1 ), System 6000( Figure 6 ) or any combination of devices of system 9000 capable of performing such techniques.

[0172] Processing circuit 9004 may receive a representation of a user input requesting a consultation from a second clinician located remotely from the first clinician from a first clinician who is performing a medical procedure and is associated with a first computing device 9050 (10002). In some examples, processing circuit 9004 may execute a computer vision model to identify a patient condition or an intra-procedure event in a representation of data captured by imager 9040. Further, in some examples, processing circuit 9004 may cause display device 9010 to display a user interface to the first clinician that includes an option to request a consultation from a second computing device 9058 associated with the second clinician. In some examples, processing circuit 9004 may receive a representation of a user input requesting a consultation from the second clinician, and processing circuit 9004 may cause first computing device 9050 to call or message second computing device 9058. In some examples, the patient condition may be a lesion, and the medical procedure may be a percutaneous coronary intervention.

[0173] The processing circuit 9004 may establish a communication session between the first computing device 9050 and the second computing device 9058 in response to receiving an indication of a user input requesting a consultation ( 10004 ).

[0174] In some examples, Fig.10 The technology may include processing circuit 9004 determining a clearance status of the second clinician. Processing circuit 90004 may edit the personal health information from the representation of the data captured by imager 9040 before streaming and in response to determining that the clearance status of the second clinician is below a threshold clearance status.

[0175] The processing circuit 9004 may stream a representation of the data of the medical procedure captured by the imager 9040 via the communication session (10006). In some examples, the data captured by the imager 9040 may include video data, fluoroscopic imaging, or both. In some examples, the processing circuit 9004 may receive annotations about the representation of the data from one or both of the first clinician or the second clinician through the corresponding associated computing devices of the first clinician or the second clinician. In some examples, the processing circuit 9004 may send annotations to the corresponding computing device of the other of the first clinician or the second clinician during the communication session. In some examples, the stream during the communication session may further include data captured from one or more audio sensors by the processing circuit 9004 during the medical procedure. In some examples, the processing circuit 9004 may cause a display associated with the second computing device 9058 to display a list of medical equipment available to the first clinician. In some examples, the processing circuit 9004 may link data from the imager 9040 during the medical procedure to data from another medical test or procedure. In some examples, the other medical test or procedure is a calcification test or data generated during an intravascular ultrasound medical procedure.

[0176] Fig.11 is a time diagram illustrating an example compressed version of imaging data sensed by one or more image sensors during a cardiac catheterization medical procedure in accordance with one or more aspects of the present disclosure. Fig.11 The time dimension is shown along axis A, which starts at time 0 and extends to time t. Fig. 9 and Fig.11 , the medical system 9000 may be configured to receive imaging data from the imager 9040. The imaging data may be sensed by the imager 9004 during a medical procedure, such as a cardiac catheterization procedure. The imaging data may be sensed as a substantially continuous stream of discrete images beginning at time 0, which may correspond to the time when the medical procedure begins and / or the imager 9040 is turned on. The continuous stream of discrete images begins at time 0 and extends continuously to time t.

[0177] Processing circuit 9004 may be configured to generate a compressed version of the imaging data, the compressed version of the imaging data including images corresponding to specific events of the medical procedure. Similarly, processing circuit 9004 may create a shortened version of the medical procedure, including only the key parts of the medical procedure. For example, the compressed version may include an excerpt of video 11000A such as extending from time t2 to time t3. Additionally or alternatively, the compressed version may include an excerpt of video 11000B such as extending from time t4 to time t5. The compressed version may further include an excerpt of video 11000C such as extending from time t6 to time t7. In some examples, the compressed version may include a continuous segment such as video 11000A. In some examples, videos 11000A, 11000B, and 11000C (collectively referred to as "video 11000") may be added together by processing circuit 9004 in any combination to include any portion of the duration of the medical procedure that is less than the total duration of the medical procedure. In some examples, as shown, video 11000 may individually include any portion of a medical procedure that is less than the entire length of the medical procedure.

[0178] Furthermore, additionally or alternatively, the compressed version of the imaging data may include one or more images 11002A, 11002B (collectively, "images 11002"). In some examples, 11002A may correspond to a "before" picture and 11002B may correspond to an "after" picture. In this manner, a clinician may be enabled to curate a shortened video and / or still images of before and after along with key case annotations, and to neatly and efficiently share the curated portion of a medical procedure with a second clinician (e.g., a referring interventional cardiologist) who may be too busy to attend the consultation during the entire medical procedure.

[0179] In some examples, processing circuit 9004 may be configured to receive user input to begin excerpting video 11000A of the received imaging data from the cardiac catheterization imaging data at time t2. In some examples, processing circuit 9004 may be configured to receive user input to end video 11000A at time t3. In some examples, processing circuit 9004 may be configured to receive audio commands to receive user input to begin and / or end video excerpting.

[0180] In some examples, processing circuit 9004 can be configured to receive user input to start video excerpting, and further configured to output a user interface to present the clinician with an option to start video excerpting of the received imaging data for display via display device 9010. In some examples, processing circuit 9004 can be configured to, alone or in conjunction with machine learning model 2022 ( Figure 2 )Combined execution of computer vision models 2024( Figure 2) to identify a patient condition (e.g., a lesion) or an intra-procedural event (e.g., such as a critical time limit for crossing a lesion with a medical device). In some examples, the processing circuit 9004 can be configured to store a compressed version in the memory 9002. In some examples, the processing circuit 9004 can be configured to upload a compressed version of the received imaging data to the server 9060.

[0181] In some examples, the processing circuitry may be configured to receive, from a first computing device 9050 associated with a first clinician who is performing a medical procedure, an indication of a user input requesting a consultation from a second clinician located remotely from the first clinician. The processing circuitry 9004 may, in response to receiving the indication of the user input requesting the consultation, establish a communication session between the first computing device 9050 associated with the first clinician and the second computing device 9058 associated with the second clinician. The processing circuitry may stream a compressed version of the imaging data from the imager 9040 via the communication session.

[0182] In some examples, as described above with respect to Fig. 9 In the example described, in which a medical procedure is continuously streamed in real time, the processing circuit 9004 may be configured to determine a second clinician's clearance status and, prior to streaming and in response to determining that the second clinician's clearance status is below a threshold clearance status, edit the personal health information from a compressed version of the data captured by the imager 9040.

[0183] The compressed version of the imaging data may be used as a "highlights reel" of the medical procedure that may be quickly shared and viewed by the referring physician. Thus, video 11000A may include a video representation of a first particular event, such as traversal of a first lesion, and processing circuitry 9004 may be configured to generate a second compressed version of the imaging data as video 11000B based on the imaging data from imager 9040, which second compressed version of the imaging data may include images corresponding to a second particular event of the cardiac catheterization medical procedure, such as traversal of a second lesion. Processing circuitry 9004 may be configured to receive case annotations associated with the medical procedure, and may be configured to associate the received case annotations with the compressed version of the imaging data.

[0184] Fig.12 is a flow chart illustrating an example technique for generating a compressed version of imaging data sensed by one or more image sensors according to one or more aspects of the present disclosure. Fig. 9 System 9000 and Fig.11 The timing diagram is described, but Fig.12 The techniques may be used by other systems, such as system 1000 ( Figure 1 ), System 6000( Figure 6) or any combination of devices of system 9000 capable of performing such techniques.

[0185] Processing circuitry 9004 may receive imaging data sensed by imager 9040 during a medical procedure (12002). Processing circuitry 9004 may generate a compressed version of the imaging data from imager 9040 based on the sensed imaging data, the compressed version of the imaging data including images corresponding to specific events of the cardiac catheterization medical procedure (12004). In some examples, processing circuitry 9004 may generate a video excerpt 11000 ( Fig.11 ) to generate a compressed version of the imaging data. In some examples, the processing circuit 9004 can generate a video excerpt by receiving a user input to start and / or end a video excerpt 11000 of the imaging data received from the cardiac catheterization imaging data. In some examples, the processing circuit 9004 can receive an audio command from a clinician to start and / or end the video excerpt 11000.

[0186] The option to start a condensed version or an excerpt of a condensed version may be presented to the clinician via a user interface at the display device 9010, output for display by the processing circuit 9004. To output a user interface including an option to start a condensed version or an excerpt of a condensed version of the video excerpt 11000A for display, the processing circuit 9004 may execute the computer vision model 2024 ( Figure 2 ) and / or machine learning models 2022( Figure 2 ) to identify a patient condition or an intra-procedural event in the received imaging data. Thus, in some examples, these advanced features of system 9000 can help clinicians identify situations in which a consultation from a second clinician may be valuable. In response to an indication of a user input requesting a consultation from a second clinician located remotely from the first clinician, processing circuitry 9004 can establish a communication session between a computing device associated with the first clinician and a computing device associated with the second clinician, and stream a compressed version of the imaging data. Prior to establishing the communication session, processing circuitry 9004 can determine that the second clinician does not have the authority required to view the patient's personal health information, and can edit the PHI from the video 11000 and images 11002 that constitute the compressed version of the imaging data.

[0187] Processing circuit 9004 can capture Fig.11The processing circuit 9004 may upload, mark and / or store the videos 11000A, 11000B and / or 11000C together or independently in the memory 9002 or the server 9060. In some examples, the processing circuit 9004 may associate case annotations associated with the performed medical procedure and further associate the received case annotations with the compressed version of the imaging data.

[0188] Fig.13A and Fig. 13B is shown in the case of a second computing device 9058 ( Fig. 9 ) is an example conceptual screenshot of an example representation of imaging data 13700A, 13700B at a user interface associated with a second computing device associated with a second remote clinician. Fig.13A An example screenshot including identification elements 13702, 13704 is shown. Identification element 13704 includes personal health information associated with a patient undergoing a medical procedure, which personal health information can be shared only with clinicians and other employees who meet a threshold permission status. For example, a clinician may need to be an employee or affiliate of the hospital where the medical procedure is performed to obtain a threshold permission status, which means that the system 9000 cannot be used with certain clinicians who are remote second clinicians because the imaging data to be shared via the communication session may be collected together with the patient's personal health information or information that is overlaid or otherwise attached to the imaging data that the first clinician does not wish to share. In addition, a de-identified version of the imaging data can be shared with other interested parties, such as on a social network, as a teaching tool or credit tool.

[0189] Fig. 13B Shows Fig.13A , which includes a representation of imaging data 13700B with identification elements 13702, 13704 edited by edit boxes 13706. In this manner, processing circuit 9004 ( Fig. 9 ) may be configured to be from imager 9040 ( Fig. 9 ) receives imaging data (which may include one or more identification elements (13702, 13704)) and generates a de-identified version of the imaging data that does not include the identification elements 13702, 13704 ( Fig. 13B ). The processing circuit 9004 is configured to not include the identification elements 13702 and 13704 ( Fig. 9) allows imaging data from Imager 90004 to be securely shared with interested parties.

[0190] Although the elements 13702, 13704 identified with respect to Fig. 13B is described as being blocked by edit box 13706, but processing circuit 9004 ( Fig. 9 ) may be configured to otherwise not include the identification elements 13702, 13704 in the de-identified version. Processing circuit 9004 ( Fig. 9 ) can be configured to edit, remove, blur or render illegible the identification element 13704 including the PHI to generate a de-identified version of the imaging data. For example, since the imager 9040 ( Fig. 9 ) can apply a text overlay containing an identification element to the sensed imaging data, so that the processing circuit 9004 ( Fig. 9 ) may be configured to scan from imager 9040 ( Fig. 9 ) to find text overlays, identify text overlays, and edit, remove, blur, or make illegible the text overlays.

[0191] In some examples, processing circuit 9004 ( Fig. 9 ) can be configured to send a de-identified version of the imaging data ( Fig. 13B ) is uploaded to the server 9060. In some examples, the processing circuit 9004 ( Fig. 9 ) can be configured to send a de-identified version of the imaging data ( Fig. 13B ) posted on a social network (such as a physician-only social network).

[0192] Fig.14 is a flow chart illustrating an example technique for generating a de-identified version of imaging data sensed by one or more image sensors during a medical procedure in accordance with one or more aspects of the present disclosure. Fig. 9 System 9000 and Fig.13A and Fig. 13B The imaging data version is described, but Fig.14 The techniques may be used by other systems, such as system 1000 ( Figure 1 ), System 6000( Figure 6 ) or any combination of devices of system 9000 capable of performing such techniques.

[0193] Processing circuit 9004 may receive imaging data from imager 9040 during a medical procedure, the received imaging data including one or more identification elements 13702, 13704 (14002). Processing circuit 9004 may generate a de-identified version of the imaging data based on the imaging data from imager 9040 (14003). Fig. 13B), the de-identified version of the imaging data does not include the identification elements 13702, 13704. The identification element 13704 may include personal health information, and the processing circuit 9004 may generate the de-identified version by editing, removing, blurring or making it illegible to the personal health information ( Fig. 13B ) to generate a de-identified version of the imaging data ( Fig. 13B ). In some examples, processing circuit 9004 may scan imaging data from one or more image sensors for text overlays, thereby identifying the text overlays; and edit, remove, blur, or otherwise render the text overlays illegible.

[0194] In some examples, processing circuit 9004 may process a de-identified version of the imaging data ( Fig. 13B ) to a server. In some examples, processing circuit 9004 may post a de-identified version of the imaging data on a social network, such as a physician-only social network. In some examples, the imaging data from imager 9040 may include video data, fluoroscopic data, or both.

[0195] Fig.15 is a screenshot showing an example user interface 15000 for using an example medical system of the present disclosure for educational purposes in accordance with one or more aspects of the present disclosure. The user interface 15000 may be present in Fig. 9 A user interface at a display of one or more devices of the system 9000. Processing circuit 9004 ( Fig. 9 ) may be configured to output the user interface 15000 to any suitable display, such as the display device 9010 ( Fig. 9 ), and the first computing device 9050 ( Fig. 9 ) associated with the computing device, a display associated with the second computing device 9058, or another display device.

[0196] Processing circuit 9004 ( Fig. 9 ) can be configured to output a main button 15806, an observation window 15820, a training menu 15810, a filter menu 15812, and an operation menu 15808 for display at the user interface 15000.

[0197] Viewing window 15820 may display images from imager 9040 ( Fig. 9 ) or a representation of imaging data of another imager, in Fig.15 In some examples, the processing circuit 9004 ( Fig. 9 ) can be configured to automatically display the data stored in the memory 9002 ( Fig. 9) as the first medical procedure 15802. In some examples, the processing circuit 9004 ( Fig. 9 ) may be configured to receive and select a storage device stored in memory 9002 ( Fig. 9 ) to output the first medical procedure for user input displayed as the first medical procedure. Processing circuit 9004 ( Fig. 9 ) can be configured to output multiple drop-down menus in the filter menu 15812 for display to help the clinician find and select the first medical procedure. Additionally or alternatively, the processing circuit 9004 ( Fig. 9 ) outputs a search bar for display and includes a search bar that the clinician can use to select from memory 9002 ( Fig. 9 ) within the search function to identify and select the first medical procedure 15802.

[0198] The viewing window 15820 may also display a representation of the imaging data from the second medical procedure 15804, illustrated as "Expert Treatment of Similar Lesions". The representation of the imaging data from the second medical procedure 15804 may be stored in the memory 9002 ( Fig. 9 ). Processing circuit 9004 ( Fig. 9 ) can be configured to execute computer vision model 2024 ( Figure 2 ) and / or machine learning models 2024( Figure 2 ) to identify and / or select second medical procedure 15804 from a plurality of medical procedures to output for display with first medical procedure 15802. Processing circuit 9004 ( Fig. 9 ) can be a beneficial learning tool for clinicians to compare lesions, techniques, treatment strategies, etc. between a first medical procedure 15802 and a second medical procedure 15804. In some examples, clinicians can select a medical procedure that they have recently performed as a first medical procedure 15802 and compare their strategy with that of another clinician in a similar case, where identification of similar cases is enabled by computer vision model 2024 and / or machine learning model 2022.

[0199] As described above, the processing circuit 9004 ( Fig. 9 ) can execute computer vision model 2022 using propensity matching to identify and select a medical procedure for display as second medical procedure 15804. In some examples, propensity matching may include using processing circuit 9004 ( Fig. 9 ) to analyze the imager 9040 ( Fig. 9) sensed imaging data to determine the type of lesion, the subtype of the lesion, or otherwise classify the lesion (e.g., provide a score or other identifier), etc. based on the determined characteristics of the lesion. The characteristics of the lesion may include, for example, the type of lesion (e.g., bifurcation lesion), the diameter of the lesion, the degree of stenosis, the degree of calcification, the angle of vascular departure, etc. The classification of the lesions may allow lesions with greater similarity to be classified as being the same or close to each other. The processing circuitry may be configured to display a medical procedure that includes a lesion that is in the same category or a similar category as a lesion present in first medical procedure 15802 as second medical procedure 15804.

[0200] In some examples, processing circuit 9004 ( Fig. 9 ) can be configured to use computer vision model 2024( Figure 2 ) and / or machine learning models 2022( Figure 2 ) by comparing the imaging data from the first medical procedure 15802 with the imaging data stored in the memory 9002 ( Fig. 9 ) to perform propensity matching, and selecting from among the plurality of medical procedures in memory 9002 ( ) based on similarity of one or more patient conditions of medical procedure 15802 to a second medical procedure. Fig. 9 ) to select the most similar medical procedure to output for display with first medical procedure 15802. In some examples, first medical procedure 15802 may be displayed with first medical procedure 15802 stored in memory 9002 ( Fig. 9 ) and may select to output the most similar medical procedure in one or more ways for display as the second medical procedure 15804. Alternatively, a threshold level of similarity (e.g., patient conditions including lesions of the same classification) may be set, and the processing circuit 9004 ( Fig. 9 ) may output any of the medical protocols that satisfy the threshold for output for display as second medical protocol 15804. In some examples, processing circuitry 9004 may be configured to compare medical protocols stored in memory 9002 that include lesions within the patient's vasculature, and may compare lesions based on lesion characteristics including at least one of lesion length, shape, geometry, location, extent, vessel departure angle, or calcification percentage.

[0201] Processing circuit 9004 ( Fig. 9 ) can also output a filter menu 15812 for display at the user interface 1600, which can enable the clinician to filter the stored in the memory 9002 ( Fig. 9) in the first medical procedure 15802 and / or the second medical procedure 15804. Filters may be applied to narrow down the candidate medical procedures for one or both of the first medical procedure 15802 and / or the second medical procedure 15804. As shown, the filter menu 15812 may present the clinician with the option of filtering based on one or more of patient characteristics, patient conditions, medical tools or equipment used during the cardiac catheterization medical procedure, clinicians in the operation, treatments used or treatment categories. Additionally or alternatively, the filter menu 15812 may present the clinician with the option of filtering based on the type of hospital, the sequence of tools or treatments used, or the length of the procedure. In some examples, patient characteristics may include patient age, height, weight, gender, disease, or diagnosis. In some examples, the medical device, tool, or equipment used may include catheter tip size or geometry.

[0202] In some examples, the imaging data forming the representation of first medical procedure 15802 may be video data. In some examples, the imaging data forming the representation of second medical procedure 15804 may be video data. Fig.15 are shown adjacent to each other, but in some examples, processing circuit 9004 ( Fig. 9 ) can be configured such that the display overlays video data from the second medical procedure 15804 over the video data of the first medical procedure 15802, or vice versa, such that the videos overlap each other within the viewing window 15820. Viewing the medical procedures in this manner may be advantageous for the clinician to more closely analyze the location of the lesion, the speed of the instrument through the vascular system, or other parameters. Although described herein as two different medical procedures for ease of description, in some examples, processing circuitry 9004 ( Fig. 9 ) may be configured to output more than two medical procedures for display within viewing window 15820, such as three medical procedures, or four medical procedures, or more.

[0203] Fig.16 is a flow chart illustrating an example technique for using an example medical system of the present disclosure for educational purposes. Fig. 9 System 9000 and Fig.15 The user interface 15000 is described, but Fig.16 The techniques may be used by other systems, such as system 1000 ( Figure 1 ), System 6000( Figure 6 ) or any combination of devices of system 9000 capable of performing such techniques.

[0204] Processing circuit 9004 ( Fig. 9 ) can be found in Medical Regulation 15802( Fig.15 ) during the period from the imager 9040 ( Fig. 9) receives imaging data (16002). Processing circuit 9004 ( Fig. 9 )Executable Computer Vision Model 2024( Figure 2 ) and / or machine learning models 2022( Figure 2 ), identify and / or select to be stored in memory 9002 ( Fig. 9 ) in the plurality of medical procedures 15804 ( Fig.15 )(16004). Processing circuit 9004 ( Fig. 9 ) can output from the first medical procedure 15802 ( Fig.15 ) and a representation of imaging data from a second medical procedure 15804 ( Fig.15 ) for displaying the imaging data on the display device 9010 ( Fig. 9 ) is displayed at (16006).

[0205] In some examples, second medical procedure 15804 ( Fig.15 ) including the first medical code 15802 ( Fig.15 ) similar patient conditions. In some examples, processing circuit 9004 ( Fig. 9 ) can use propensity matching to execute computer vision models 2024( Figure 2 ) in memory 9002 ( Fig. 9 ) found in the second medical regulation 15804 ( Fig.15 ).

[0206] In some examples, processing circuit 9004 ( Fig. 9 ) can use propensity matching by performing the following operations: Fig.15 ) and the imaging data stored in the memory 9002 ( Fig. 9 ) in multiple medical procedures; and based on the first medical procedure 15802 ( Fig.15 ) and the Second Medical Code 15804( Fig.15 ) from the similarity of one or more characteristics stored in memory 9002 ( Fig. 9 ) selects a second medical procedure 15802 ( Fig.15 ) to output for use with the first medical procedure 15804 ( Fig.15 ) are displayed together.

[0207] In some examples, the patient condition may be a pathology, and the processing circuit 9004 ( Fig. 9 ) can be from the first medical procedure 15802 ( Fig.15 ) and one or more lesion characteristics stored in memory 9002 ( Fig. 9) and by comparing multiple medical procedures stored in memory 9002 ( Fig. 9 ) to find the most similar lesion from one or more lesion characteristics of multiple medical procedures stored in memory 9002 ( Fig. 9 ) in multiple cardiac catheterization medical procedures and selecting a second medical procedure 15804 ( Fig.15 ). In some examples, wherein the one or more lesion characteristics include at least one of lesion length, shape, geometry, location, extent, vessel departure angle, or degree of calcification.

[0208] In some examples, processing circuit 9004 ( Fig. 9 ) may enable the user interface 15000 ( Fig.15 ) presents to the clinician the information stored in the memory 9002 (filtered in one or more ways). Fig. 9 ) in multiple medical procedures. In some examples, processing circuit 9004 ( Fig. 9 ) may enable the user interface 15000 ( Fig.15 ) presents a clinician with the option to filter based on one or more of a patient characteristic, a patient condition, a medical tool or equipment used during a cardiac catheterization medical procedure, a performing clinician, a treatment or treatment category used, a hospital type, a tool or treatment sequence used, or a procedure length. In some examples, wherein the patient characteristic includes at least one of patient age, height, weight, sex, disease, or diagnosis. In some examples, wherein the medical tool or equipment used includes one or more of a catheter tip size or geometry.

[0209] In some examples, processing circuit 9004 ( Fig. 9 ) may enable the user interface 15000 ( Fig.15 ) in the observation window 15820( Fig.15 ) shows the first medical procedure 15802 ( Fig.15 ) and Second Medical Code 15804( Fig.15 In some examples, the processing circuit 9004 ( Fig. 9 ) may enable the user interface 15000 ( Fig.15 ) in the observation window 15820( Fig.15 ) from the second medical code 15804 ( Fig.15 ) is overlaid on the video data from the first medical procedure 15802 ( Fig.15 ) above the video data. Alternatively, in some examples, video from two medical procedures may be viewed in viewing window 15820 ( Fig.15 ) are displayed next to each other.

[0210] Fig.17is a flow chart illustrating an example technique for using an example medical system of the present disclosure for educational purposes in accordance with one or more aspects of the present disclosure. Fig. 9 System 9000 and Fig.15 The user interface 15000 is described, but Fig.16 The techniques may be used by other systems, such as system 1000 ( Figure 1 ), System 6000( Figure 6 ) or any combination of devices of system 9000 capable of performing such techniques.

[0211] Processing circuit 9004 ( Fig. 9 ) can capture user information from a user interacting with the medical system to identify the user (17002). In some examples, the processing circuit 9004 ( Fig. 9 ) can output a user login page for display to capture user information from a user interacting with the medical system to identify the user.

[0212] Processing circuitry 9004 may store data representing imaging data from a plurality of medical procedures in memory 9002 ( Fig. 9 ) (17004). Processing circuit 9004 ( Fig. 9 ) can be output to indicate that the storage is stored in the memory 9002 ( Fig. 9 ) is output (17006). After the data representing the individual medical procedure is output for display, the user is credited for viewing the individual cardiac catheterization medical procedure (17008).

[0213] In some examples, crediting a user for viewing an excerpt of a medical procedure may include granting the user at least a portion of continuing medical education (CME) credits. In some examples, processing circuit 9004 ( Fig. 9 ) may be integrated with a certification body, and as part of at least a portion of awarding CME credits to a user, processing circuit 9004 ( Fig. 9 ) may report the user's name to the authentication authority. In some examples, processing circuit 9004 ( Fig. 9 ) can be used with the display device 9010 ( Fig. 9 ) associated user interface 15000 ( Fig.15 ) presents the user with the option of filtering multiple medical procedures in one or more ways. In some examples, the processing circuit 9004 ( Fig. 9 ) may enable the user interface 15000 ( Fig.15 ) presents multiple drop-down menus for display as filter menus 15812 ( Fig.15 ).

[0214] Fig.18is a schematic perspective view of an example medical system 18000. The medical system 18000 may be Figure 1 Medical Systems 1000 and / or Figure 6 Example of Medical System 9000. Fig.18 The medical system 18000 is similar to Fig. 9 The medical system 9000 of FIG. 1 is different from the medical system 9000 described below, wherein like reference numerals indicate like elements. The medical system 18000 can perform various contrast media management functions to support Cath Lab procedures.

[0215] System 18000 includes display device 18010, table 18020, imager 18040, computing device 18050, and server 18060. System 18000 may be an example of a system for use in a Cath lab. In some examples, system 18000 may include other devices, such as Figure 1 The additional devices depicted in the figure are not shown in the figure for the sake of simplicity. Fig.18 The system 18000 may be used to diagnose and / or intervene in a patient's cardiovascular problems during a Cath Lab procedure session.

[0216] The computing device 18050 may be associated with a clinician located in a Cath Lab during a medical procedure. The computing device 18050 may be a computing device 6050 or 6052 ( Figure 6 ), and may include, for example, off-the-shelf devices such as laptop computers, desktop computers, tablet computers, smart phones, or other similar devices. Computing device 18050 includes memory 18002 and processing circuit 18004.

[0217] Although processing circuit 18004 appears Fig.18 18050, but in some examples, features attributed to processing circuit 18004 may be performed by processing circuits of any of computing device 18050, imager 18040, server 18060, other elements of system 18000, or a combination thereof. In some examples, one or more processors associated with processing circuit 18004 in computing device 18050 may be distributed and shared across any combination of computing device 18050, imager 18040, server 18060, network 18056, and display device 18010. Additionally, in some examples, processing operations or other operations performed by processing circuit 18004 may be performed by one or more processors residing remotely (such as one or more cloud servers or processors), each of which may be considered part of computing device 18050.

[0218] System 18000 includes network 18056, which is a suitable network, such as a local area network (LAN), a wide area network (WAN), a wireless mobile network, a Bluetooth network, or the Internet, including a wired network or a wireless network. In some examples, network 18056 can be a secure network, such as a hospital network, which can restrict user access.

[0219] As discussed above, the imager 18040 can be an angiographic imager or other imaging device and can be used to image the patient's body during a procedure to visualize the characteristics and location of lesions inside the patient's body. The imager 9040 can be any type of imaging device, such as a CT device, an MRI device, a fluoroscopic device, a PET device, an ultrasound device, etc.

[0220] During a Cath Lab procedure, contrast media may be injected into the patient's vasculature. The contrast media may enhance the appearance of blood and / or other components in the imaging data captured by the imager 18040. In some examples, the contrast media may be manually injected into the vasculature by a clinician (e.g., a cardiologist, a nurse, or other) via a syringe. In other examples, the system 18000 may include a contrast media injector (e.g., a power injector) that can automatically inject / dispense contrast media.

[0221] In general, the amount of contrast used in a Cath Lab procedure is a balance between using enough contrast to make the resulting images useful and not using too much to cause undesirable side effects. For example, using too much contrast can lead to a condition called contrast-induced nephropathy (CIN).

[0222] Clinicians performing Cath Lab procedures may have an overall goal for how much contrast they plan to use during a procedure. However, tracking actual contrast use versus planned contrast use may have an adverse effect on clinician workload during a procedure.

[0223] According to one or more aspects of the present disclosure, system 18000 can provide automatic tracking of contrast agent usage. For example, computing device 18050 can determine the cumulative amount of contrast agent used during a cardiac catheterization laboratory (Cath Lab) procedure and cause display device 18010 to display a graphical representation of the cumulative amount of contrast agent used during the Cath Lab procedure. Thus, system 18000 can enable a clinician to quickly determine how much contrast agent has been used (e.g., at a glance).

[0224] The graphical representation displayed at the display device 18010 can be in any suitable form. Examples include, but are not limited to, a graph (e.g., a bar graph, a pie chart, a line graph, etc.), a textual representation (e.g., a number on a display), or any other representation. As an example, the graphical representation may include a graph having a curve of the amount of contrast agent used versus time. As another example, the textual representation may include the percentage of contrast agent used relative to the expected or maximum amount of contrast agent.

[0225] In some examples, computing device 18050 may provide contextual data to help the clinician better understand the amount of contrast that has been used. For example, computing device 18050 may include in the graphical representation the amount of contrast that is expected to have been used by the current point in the Cath Lab procedure. This may further help the clinician determine whether contrast administration should be slowed down (e.g., in the case where the actual amount used is greater than the expected amount used), or whether additional contrast buffer is available (e.g., in the case where the actual amount used is less than the expected amount used).

[0226] The computing device 18050 may output a comparison between the expected contrast dose and the actual contrast dose for each time range. As an example, the computing device 18050 may output a comparison of the entire procedure of contrast agent use. For example, the computing device 18050 may cause the display device 18010 to display a graphical representation of the comparison between the cumulative amount of contrast agent used so far during the cardiac catheterization laboratory procedure and the total amount of contrast agent expected / predicted to be used during the entire procedure. As another example, the computing device 18050 may output a step-by-step comparison of contrast agent use. For example, in the case where the execution of the Cath Lab procedure includes the execution of multiple steps (e.g., navigating the device to the lesion, using the device to intervene in the lesion, measuring blood flow after intervention, etc.), the computing device 18050 may determine independent target contrast agent doses for different steps, and display a graphical representation of the comparison between the target contrast agent dose of the step and the corresponding contrast dose used during the execution of the step.

[0227] In general, the rhythm of contrast agent usage may not be linearly distributed throughout the Cath Lab procedure. The execution of certain steps may utilize more contrast agent than other steps. Therefore, a linear representation of target contrast agent usage relative to the steps (e.g., 37.5% of the total contrast agent is expected to be used at the end of step 3 of 8) may be useless to the clinician. According to one or more aspects of the present disclosure, the target contrast agent dose for the steps can be different. For example, the target contrast agent dose for the navigation step may be lower than the target contrast agent dose for the measurement step. In this way, system 18000 can provide clinicians with higher quality contrast agent usage guidance.

[0228] There may be a total planned contrast agent dose (e.g., the sum of the target contrast agent doses for all steps) at the beginning of a Cath Lab procedure. However, in some cases, events may occur that cause the clinician to want to administer more contrast agent than the total planned contrast agent dose. As discussed above, CIN may be caused by administering too much contrast agent to a patient. However, different patients may be able to tolerate different amounts of contrast agent without experiencing CIN. Therefore, it may be desirable for the clinician to be able to determine how much contrast agent can still be administered to a patient without causing CIN.

[0229] According to one or more aspects of the present disclosure, the computing device 18050 may obtain the maximum contrast agent dose for the Cath Lab protocol and output a comparison between the cumulative amount of contrast agent used and the maximum contrast agent dose. For example, the computing device 18050 may cause the display device 18010 to display a graphical representation of the comparison between the cumulative amount of contrast agent used and the maximum contrast agent dose (e.g., the maximum dose is 120 cubic centimeters (cc), and 80cc is used). In this way, the computing device 18050 may enable the clinician to quickly determine how much additional contrast agent can be used without causing CIN.

[0230] As discussed above, the computing device 18050 may determine the amount of contrast agent used. Generally speaking, the computing device 18050 may determine the amount of contrast agent used via any suitable input source. As an example, the computing device 18050 may receive manual input of contrast agent use via a user interface (e.g., by a clinician or another person present, such as a nurse). For example, the manual input may indicate how many syringes of a specific capacity (e.g., 20cc) have been used. As another example, the computing device 18050 may receive data representing contrast agent use via one or more sensors (e.g., a flow meter). For example, the computing device 18050 may receive data representing contrast agent use from a sensor integrated in a contrast agent injector 18080 or a sensor connected in series between the contrast agent injector 18080 and the patient. In some examples, the connection between the sensor and the computing device 18050 may be wireless (e.g., Bluetooth, Wi-Fi, etc.). The received data may indicate a contrast agent flow rate (e.g., cc / min) or may indicate a cumulative amount of contrast agent used (e.g., cc). Where the data indicates a contrast flow rate, the computing device 18050 may determine the cumulative amount of contrast used based on the contrast flow rate (and historical flow rate data for the procedure) (eg, integrating the flow rate data).

[0231] As discussed above, the computing device 18050 may utilize an expected / predicted amount of contrast agent (e.g., when generating a graphical representation of actual contrast agent usage compared to expected contrast agent usage). In some examples, the expected amount may be a generic non-patient-specific amount (e.g., a standard amount used for navigation steps, measurement steps, etc.). In other examples, the expected amount may be patient-specific. For example, the computing device 18050 may determine a patient-specific predicted amount of contrast agent based on the attributes of the current patient. In some examples, the attributes may include imaging data of the current patient captured prior to a cardiac catheterization laboratory procedure (e.g., diagnostic angiography imaging data). For example, the computing device 18050 may determine a classification of the current patient based on the imaging data (e.g., using a machine learning model, such as described herein). The computing device 18050 may then determine a predicted amount of contrast agent for the current patient based on the amount of contrast agent used for other patients with the classification. In this way, the computing device 18050 may predict the amount of contrast agent for the current patient based on the amount of contrast agent used in similar cases.

[0232] In some examples, computing device 18050 may perform clinician-agnostic contrast agent predictions. For example, computing device 18050 may predict the amount of contrast agent for the current patient regardless of the attributes of the clinician who is to perform the procedure. In other examples, computing device 18050 may perform clinician-specific contrast agent predictions. For example, computing device 18050 may predict the amount of contrast agent for the current patient based on the attributes of the clinician who is to perform the procedure.

[0233] In some examples, the computing device 18050 can generate a clinician-specific contrast amount by adjusting the clinician-unknown amount. As an example, the computing device 18050 can determine that the clinician who is to perform the procedure uses an amount that is X% less than the clinician-unknown predicted amount. In such examples, the computing device 18050 can reduce the clinician-unknown predicted amount by X% to generate a clinician-specific predicted amount. Similarly, if the clinician typically uses more than the clinician-unknown predicted amount, the computing device 18050 can increase the clinician-unknown predicted amount.

[0234] In some examples, computing device 18050 may generate a clinician-specific contrast amount by making a prediction based on similar cases performed by a particular clinician. For example, computing device 18050 may determine a predicted amount based on the amount of contrast media actually used by a particular clinician in similar cases.

[0235] As described above, the computing device 18050 may obtain the maximum contrast agent dose for the current patient and may predict the expected contrast agent usage for the current patient. In some examples, if the predicted contrast agent usage for the current patient is greater than the maximum contrast agent dose for the current patient, the computing device 18050 may output a warning or other indication to the clinician (e.g., during the planning stage). Thus, the computing device 18050 may provide advance warnings to clinicians so that clinicians may modify their plans before actually starting the procedure, which may avoid undesirable situations.

[0236] Fig.19 is a conceptual diagram illustrating an example graphical user interface (GUI) including contrast media usage data according to one or more aspects of the present disclosure. Fig.19 The GUI 19000 may be displayed on a display device (such as Fig.18 GUI 19000 is merely one example of a GUI that includes contrast agent usage data, and other arrangements are also contemplated.

[0237] like Fig.19 As shown in , GUI 19000 includes contrast media usage data for five steps of a Cath Lab protocol. A computing device, such as computing device 18050, can update GUI 19000 as the Cath Lab protocol progresses. For example, GUI 19000 may initially include only a predicted bar for step 1. Then, as contrast media is administered or at the end of execution of step 1, GUI 19000 may be updated to include an actual bar. Fig.19 As can be seen in the example of FIG1 , the amount of contrast agent actually used in step 1 is slightly less than the predicted amount. In contrast, the amount of contrast agent actually used in step 2 is slightly greater than the predicted amount. Although shown as including five steps, GUI 19000 may include contrast agent information for more or fewer steps.

[0238] In some examples, the GUI 19000 may omit contrast medium usage information for some steps. For example, the GUI 19000 may include contrast medium usage information for the current step, but may omit or summarize contrast medium usage information for previous steps. As a specific example, when executing step 3, the GUI 19000 may include an element that sums the actual amount and the predicted amount of the completed steps (e.g., steps 1 and 2) and / or the predicted amount of the subsequent steps (e.g., steps 4 and 5).

[0239] Fig. 20 is a flow chart illustrating an example technique for providing contrast agent usage data according to one or more aspects of the present disclosure. Fig.18 System 18000 and Fig.19 The GUI 19000 is described, but Fig. 20 The techniques may be used by other systems, such as system 1000 ( Figure 1 ), System 6000( Figure 6 ) or any combination of devices of system 18000 capable of performing such techniques.

[0240] Processing circuit 18004 ( Fig.18 ) can determine a cumulative amount of contrast medium used during a cardiac catheterization laboratory (Cath Lab) procedure (20002) and output a graphical representation of the cumulative amount of contrast medium used for display (20004). As described above, in some examples, the graphical representation can include a comparison between the cumulative amount of contrast medium used and an expected amount of contrast medium used. The comparison can be per-procedural, or can be per-step (e.g., Fig.19 This step-by-step comparison is shown).

[0241] Fig.21 is a conceptual diagram illustrating an example machine learning model according to one or more aspects of the present disclosure. Machine learning model 21000 may be an example of machine learning model 7022 or any other machine learning model described herein. In some examples, machine learning model 21000 may be part of computer vision model 7024 or any other computer vision model described herein. Machine learning model 21000 may be an example of a deep learning model or deep learning algorithm trained to predict contrast agent use. One or more of computing device 6050, computing device 7000 (or any other computing device described herein), and / or server 6060 (or any other server described herein) may train, store, and / or utilize machine learning model 21000, but in some examples, other devices of system 6000 (or any other system described herein) may apply input to machine learning model 21000. In some examples, other types of machine learning and deep learning models or algorithms may be utilized in other examples. For example, a convolutional neural network model of ResNet-18 may be used. Some non-limiting examples of models that can be used for transfer learning include AlexNet, VGGNet, GoogleNet, ResNet50, or DenseNet, etc. Some non-limiting examples of machine learning techniques include support vector machines, K-nearest neighbor algorithms, and multi-layer perceptrons.

[0242] like Fig.21As shown in the example of , the machine learning model 21000 may include three types of layers. The three types of layers include an input layer 21002, a hidden layer 21004, and an output layer 21006. The output layer 21006 includes the output of the transfer function 21005 from the output layer 21006. The input layer 21002 represents each of the input values ​​X1 to X4 provided to the machine learning model 21000. In some examples, as described above, the input value may include any of the values ​​input into the machine learning model. For example, the input value may include contrast agent usage data, procedure data, and / or other data as described above. In addition, in some examples, the input value of the machine learning model 21000 may include additional data, such as other data that may be collected by the system 6000 (or any other system described herein) or stored in the system 6000.

[0243] Each of the input values ​​of each node in the input layer 21002 is provided to each node of the first layer of the hidden layer 21004. Fig.21 In the example of , hidden layer 21004 includes two layers, one layer having four nodes and the other layer having three nodes, but fewer or greater numbers of nodes may be used in other examples. Each input from input layer 21002 is multiplied by a weight and then added at each node of hidden layer 21004. During training of machine learning model 21000, the weight of each input is adjusted to establish a relationship between predicted contrast dose, historical contrast dose, and actual contrast dose. In some examples, one hidden layer may be incorporated into machine learning model 21000, or three or more hidden layers may be incorporated into machine learning model 21000, where each layer includes the same or different number of nodes.

[0244] The results of each node within the hidden layer 21004 are applied to a transfer function of the output layer 21006. The transfer function can be linear or non-linear, depending on the number of layers within the machine learning model 21000. Example non-linear transfer functions can be a sigmoid function or a rectifier function. The output 21007 of the transfer function can be a predicted amount of contrast agent and / or the like.

[0245] As shown in the above examples, by applying the machine learning model 21000 to input data such as imaging data 7014 and / or lesion classification 7030, the processing circuit 7004 can determine a predicted amount of contrast agent. This can improve patient treatment outcomes.

[0246] Fig. 2222000 may be used to train machine learning model 7022 (or any other machine learning model discussed herein) and / or computer vision model 7024 (or any other computer vision model discussed herein). Machine learning model 22074 (which may be an example of machine learning model 21000, machine learning model 7022, or any other machine learning model discussed herein) may be implemented using any number of models for supervised and / or reinforcement learning, such as, but not limited to, artificial neural networks, decision trees, naive Bayesian networks, support vector machines or k-nearest neighbor models, CNNs, RNNs, LSTMs, ensemble networks, to name a few examples. In some examples, one or more of computing device 6050 (or any other computing device discussed herein) and / or server 6060 (or any other server discussed herein) initially trains machine learning model 22074 based on a corpus of training data 22072. Training data 22072 may include, for example, data collected from past medical procedures (including at least one of past contrast media usage data).

[0247] When training the machine learning model 22074, the processing circuit 7004 (or any other processing circuit discussed herein) may compare 22076 the prediction or classification with the target output 22078. The processing circuit 7004 may train (learn / train 22080) the machine learning model 22074 using an error signal from the comparison. The processing circuit 7004 may generate machine learning model weights or other modifications, which the processing circuit 7004 may use to modify the machine learning model 22074. For example, the processing circuit 7004 may modify the weights of the machine learning model 21000 based on the learning / training 22080. For example, for each training instance in the training data 22072, one or more of the computing device 6050 and / or the server 6060 may modify the manner in which the predicted amount of contrast agent is determined based on the training data 22072.

[0248] The following numbered embodiments may illustrate one or more aspects of the present disclosure.

[0249] Embodiment 1A. A medical system comprising: a memory; and a processing circuit communicatively coupled to the memory, the processing circuit being configured to: determine a cumulative amount of contrast media used during a cardiac catheterization laboratory procedure; and output a graphical representation for display of the cumulative amount of contrast media used during the cardiac catheterization laboratory procedure.

[0250] Example 2A. A medical system according to Example 1A, wherein the cardiac catheterization laboratory procedure includes multiple steps, and wherein the processing circuit is further configured to: determine a target contrast agent dose for a first step of the multiple steps; determine an amount of contrast agent used during performance of the first step; and output a graphical representation of a comparison between the target contrast agent dose for the first step and the amount of contrast agent used during performance of the first step for display during the cardiac catheterization laboratory procedure.

[0251] Example 3A. A medical system according to Example 2A, wherein the processing circuit is further configured to: determine a target contrast agent dose for a second step of the plurality of steps, the target contrast agent dose for the second step being different from the target contrast agent dose for the first step; determine an amount of contrast agent used during performance of the second step; and output a graphical representation of a comparison between the target contrast agent dose for the second step and the amount of contrast agent used during performance of the second step for display during the cardiac catheterization laboratory procedure.

[0252] Embodiment 4A. The medical system of Embodiment 3A, wherein the target contrast agent dose for the second step is greater than the target contrast agent dose for the first step.

[0253] Embodiment 5A. The medical system of Embodiment 4A, wherein the first step comprises a navigation step and the second step comprises a measurement step.

[0254] Example 6A. A medical system according to any one of Examples 1A to 5A, wherein, in order to determine the cumulative amount of contrast agent used, the processing circuit is configured to determine the cumulative amount of contrast agent used at a specific point in the cardiac catheterization laboratory procedure, and wherein the processing circuit is further configured to: determine the amount of contrast agent predicted to have been used at the specific point; and output for display during the cardiac catheterization laboratory procedure a graphical representation of a comparison between the amount of contrast agent predicted to have been used by the specific point and the cumulative amount of contrast agent used at the specific point.

[0255] Embodiment 7A. The medical system of any one of Embodiments 1A to 6A, wherein, to determine the cumulative amount of contrast agent used, the processing circuit is configured to: receive manual input of the cumulative amount of contrast agent used via a user interface.

[0256] Embodiment 8A. The medical system of any one of Embodiments 1A to 6A, wherein, to determine the cumulative amount of contrast agent used, the processing circuit is configured to: receive data representing the cumulative amount of contrast agent used via a flow meter.

[0257] Embodiment 9A. The medical system of Embodiment 8A further comprising: a wireless communication unit, wherein, to receive the data, the processing circuit is configured to receive the data from the flow meter via the wireless communication unit.

[0258] Embodiment 10A. A medical system according to any one of Embodiments 1A to 9A, wherein the graphical representation of the cumulative amount of contrast agent used includes one or more of the following: a graph showing the cumulative amount of contrast agent used; and a textual representation of the cumulative amount of contrast agent used.

[0259] Embodiment 11A. The medical system of Embodiment 10A, wherein the graph comprises a plot of the amount of contrast agent used over time.

[0260] Example 12A. A medical system according to any one of Examples 1A to 11A, wherein the processing circuit is further configured to: obtain a maximum contrast agent dose for the cardiac catheterization laboratory procedure for the current patient; and output a graphical representation for display of a comparison between the cumulative amount of contrast agent used and the maximum contrast agent dose during the cardiac catheterization laboratory procedure.

[0261] Embodiment 13A. The medical system of any one of Embodiments 1A to 12A, further comprising: a syringe configured to inject a contrast agent into a patient, wherein the processing circuit is further configured to control the syringe.

[0262] Embodiment 14A. The medical system of Embodiment 13A, wherein, to control the injector, the processing circuit is configured to control the injector based on a cumulative amount of the contrast agent used.

[0263] Example 15A. A medical system according to any one of Examples 1A to 14A, wherein the processing circuit is configured to: determine a predicted amount of contrast agent for the current patient to be used for the cardiac catheterization laboratory procedure; and output for display a graphical representation of a comparison between the predicted amount of contrast agent and the cumulative amount of contrast agent used during the cardiac catheterization laboratory procedure.

[0264] Embodiment 16A. A medical system according to Embodiment 15A, wherein, to determine the predicted amount of contrast agent, the processing circuit is configured to: determine the predicted amount of contrast agent based on imaging data of the current patient captured prior to a cardiac catheterization laboratory procedure.

[0265] Embodiment 17A. The medical system of Embodiment 16A, wherein the imaging information captured prior to the cardiac catheterization laboratory procedure includes diagnostic angiographic imaging information.

[0266] Example 18A. A medical system according to Example 16A or Example 17A, wherein, in order to determine the predicted amount of contrast agent based on the imaging data, the processing circuit is configured to: determine the classification of the current patient based on the imaging data; and determine the predicted amount of contrast agent for the current patient based on the amount of contrast agent used for other patients with the classification.

[0267] Embodiment 1B. A medical system comprising: a memory; and a processing circuit communicatively coupled to the memory, the processing circuit being configured to: determine a cumulative amount of contrast media used during a cardiac catheterization laboratory procedure; and output a graphical representation for display of the cumulative amount of contrast media used during the cardiac catheterization laboratory procedure.

[0268] Example 2B. A medical system according to Example 1B, wherein the cardiac catheterization laboratory procedure includes multiple steps, and wherein the processing circuit is further configured to: determine a target contrast agent dose for a first step of the multiple steps; determine an amount of contrast agent used during performance of the first step; and output a graphical representation of a comparison between the target contrast agent dose for the first step and the amount of contrast agent used during performance of the first step for display during the cardiac catheterization laboratory procedure.

[0269] Example 3B. A medical system according to Example 2B, wherein the processing circuit is further configured to: determine a target contrast agent dose for a second step of the plurality of steps, the target contrast agent dose for the second step being different from the target contrast agent dose for the first step; determine an amount of contrast agent used during performance of the second step; and output a graphical representation for display during the cardiac catheterization laboratory procedure of a comparison between the target contrast agent dose for the second step and the amount of contrast agent used during performance of the second step.

[0270] Embodiment 4B. The medical system of Embodiment 3B, wherein the target contrast agent dose for the second step is greater than the target contrast agent dose for the first step.

[0271] Embodiment 5B. The medical system of Embodiment 4B, wherein the first step comprises a navigation step and the second step comprises a measurement step.

[0272] Example 6B. A medical system according to Example 1B, wherein, in order to determine the cumulative amount of contrast agent used, the processing circuit is configured to determine the cumulative amount of contrast agent used at a specific point in the cardiac catheterization laboratory procedure, and wherein the processing circuit is further configured to: determine the amount of contrast agent predicted to have been used by the specific point; and output a graphical representation for display during the cardiac catheterization laboratory procedure of a comparison between the amount of contrast agent predicted to have been used by the specific point and the cumulative amount of contrast agent used at the specific point.

[0273] Embodiment 7B. The medical system of Embodiment 1B, wherein, to determine the cumulative amount of contrast agent used, the processing circuit is configured to: receive manual input of the cumulative amount of contrast agent used via a user interface.

[0274] Embodiment 8B. The medical system of Embodiment 1B, wherein, to determine the cumulative amount of contrast agent used, the processing circuit is configured to: receive data representing the cumulative amount of contrast agent used via a flow meter.

[0275] Embodiment 9B. The medical system of Embodiment 8B further comprises: a wireless communication unit, wherein, in order to receive the data, the processing circuit is configured to receive the data from the flow meter via the wireless communication unit.

[0276] Embodiment 10B. The medical system of Embodiment 1B, wherein the graphical representation of the cumulative amount of contrast agent used comprises one or more of: a graph showing the cumulative amount of contrast agent used; and a textual representation of the cumulative amount of contrast agent used.

[0277] Embodiment 11B. The medical system of Embodiment 10B, wherein the graph comprises a plot of the amount of contrast agent used over time.

[0278] Example 12B. A medical system according to Example 1B, wherein the processing circuit is further configured to: obtain a maximum contrast agent dose for the cardiac catheterization laboratory procedure for the current patient; and output a graphical representation for display of a comparison between the cumulative amount of contrast agent used and the maximum contrast agent dose during the cardiac catheterization laboratory procedure.

[0279] Embodiment 13B. The medical system of Embodiment 1B further comprises: a syringe configured to inject a contrast agent into a patient, wherein the processing circuit is further configured to control the syringe.

[0280] Embodiment 14B. The medical system of Embodiment 13B, wherein, to control the injector, the processing circuit is configured to control the injector based on a cumulative amount of the contrast agent used.

[0281] Example 15B. A medical system according to Example 1B, wherein the processing circuit is configured to: determine a predicted amount of contrast agent for the current patient to be used for the cardiac catheterization laboratory procedure; and output a graphical representation for display of a comparison between the predicted amount of contrast agent and the cumulative amount of contrast agent used during the cardiac catheterization laboratory procedure.

[0282] Embodiment 16B. A medical system according to Embodiment 15B, wherein, to determine the predicted amount of contrast agent, the processing circuit is configured to: determine the predicted amount of contrast agent based on imaging data of the current patient captured prior to a cardiac catheterization laboratory procedure.

[0283] Embodiment 17B. The medical system of Embodiment 16B, wherein the imaging information captured prior to the cardiac catheterization laboratory procedure includes diagnostic angiographic imaging information.

[0284] Example 18B. A medical system according to Example 16B, wherein, in order to determine the predicted amount of contrast agent based on the imaging data, the processing circuit is configured to: determine the classification of the current patient based on the imaging data; and determine the predicted amount of contrast agent for the current patient based on the amount of contrast agent used for other patients with the classification.

[0285] The technology described in the present disclosure may be implemented at least in part in hardware, software, firmware, or any combination thereof. For example, various aspects of the technology may be implemented in one or more processors or processing circuits, including one or more microprocessors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or any other equivalent integrated or discrete logic circuit systems, and any combination of such components. The terms "controller," "processor," or "processing circuit" may generally refer to a single aforementioned logic circuit system or a combination of the aforementioned logic circuit system with other logic circuit systems, or any circuit system in any other equivalent circuit system. A control unit including hardware may also perform one or more of the technologies disclosed herein. Such hardware, software, and firmware may be implemented in the same device or in an independent device to support the various operations and functions described in the present disclosure. In addition, any described unit, circuit, or component may be implemented together, or independently implemented as a discrete but interoperable logic device. Describing different features as circuits or units is intended to highlight different functional aspects, and does not necessarily imply that such circuits or units must be implemented by independent hardware components or software components. Conversely, functionality associated with one or more circuits or units may be performed by separate hardware or software components, or integrated within common or separate hardware or software components.

[0286] The techniques described in the present disclosure may also be embedded or encoded in a computer-readable medium (such as a computer-readable storage medium) containing instructions. The instructions embedded or encoded in the computer-readable storage medium may cause a programmable processor or other processor to perform the method, for example, when executing these instructions. The computer-readable storage medium may include a random access memory (RAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), or an electrically erasable programmable read-only memory (EEPROM) or other computer-readable medium.

[0287] Various embodiments have been described. These and other embodiments are within the scope of the following claims.

Claims

1. A medical system, comprising: Memory; and a processing circuit, the processing circuit being communicatively coupled to the memory, the processing circuit being configured to: Determine the cumulative amount of contrast used during cardiac catheterization laboratory procedures; as well as A graphical representation of the cumulative amount of contrast media used during the cardiac catheterization laboratory procedure is output for display.

2. The medical system according to claim 1, wherein: The cardiac catheterization laboratory protocol comprises a plurality of steps, and wherein the processing circuit is further configured to: determining a target contrast agent dose for a first step in the plurality of steps; determining an amount of contrast agent used during performance of said first step; and A graphical representation of a comparison between a target contrast agent dosage for a first step and an amount of contrast agent used during performance of the first step is output for display during the cardiac catheterization laboratory procedure.

3. The medical system according to claim 2, wherein: The processing circuit is further configured to: determining a target contrast agent dose for a second step of the plurality of steps, the target contrast agent dose for the second step being different from the target contrast agent dose for the first step; determining an amount of contrast agent used during performance of said second step; as well as A graphical representation of a comparison between a target contrast agent dosage for a second step and an amount of contrast agent used during performance of the second step is output for display during the cardiac catheterization laboratory procedure.

4. The medical system according to claim 3, wherein: The target contrast agent dose for the second step is greater than the target contrast agent dose for the first step.

5. The medical system according to claim 4, wherein: The first step comprises a navigation step and the second step comprises a measurement step.

6. The medical system according to any one of claims 1 to 5, wherein: To determine the cumulative amount of contrast media used, the processing circuit is configured to determine the cumulative amount of contrast media used at a particular point in the cardiac catheterization laboratory protocol, and wherein the processing circuit is further configured to: determining an amount of contrast agent predicted to have been used by the particular point; and A graphical representation of a comparison between an amount of contrast media predicted to have been used by a particular point and a cumulative amount of the contrast media used at the particular point is output for display during the cardiac catheterization laboratory procedure.

7. The medical system according to any one of claims 1 to 6, wherein: To determine the cumulative amount of contrast agent used, the processing circuit is configured to: Manual input of the cumulative amount of contrast media used is received via a user interface.

8. The medical system according to any one of claims 1 to 6, wherein: To determine the cumulative amount of contrast agent used, the processing circuit is configured to: Data indicative of a cumulative amount of the contrast agent used is received via a flow meter.

9. The medical system according to claim 8, further comprising: A wireless communication unit, wherein, in order to receive the data, the processing circuit is configured to receive the data from the flow meter via the wireless communication unit.

10. The medical system according to any one of claims 1 to 9, wherein: The graphical representation of the cumulative amount of contrast agent used includes one or more of the following: a graph showing the cumulative amount of said contrast agent used; and A textual representation of the cumulative amount of the contrast agent used.

11. The medical system according to claim 10, wherein: The graph includes a plot of the amount of contrast agent used versus time.

12. The medical system according to any one of claims 1 to 11, wherein: The processing circuit is further configured to: obtaining the maximum contrast medium dose for the cardiac catheterization laboratory procedure for the current patient; as well as A graphical representation of the cumulative amount of contrast media used compared to the maximum contrast media dose is output for display during the cardiac catheterization laboratory procedure.

13. The medical system according to any one of claims 1 to 12, further comprising: a syringe configured to inject a contrast medium into the patient, Wherein the processing circuit is further configured to control the injector.

14. The medical system according to claim 13, wherein: To control the injector, the processing circuit is configured to control the injector based on the cumulative amount of contrast agent used.

15. The medical system according to any one of claims 1 to 14, wherein the processing circuit is configured to: determining a pre-measurement of contrast media for the cardiac catheterization laboratory procedure for the current patient; and A graphical representation of a comparison between the predicted amount of contrast media and the cumulative amount of contrast media used is output for display during the cardiac catheterization laboratory procedure.

16. The medical system of claim 15, wherein: To determine the contrast agent predicted amount, the processing circuit is configured to: The contrast agent pre-dosage is determined based on imaging information of the current patient captured prior to the cardiac catheterization laboratory procedure.

17. The medical system of claim 16, wherein: The imaging information captured prior to the cardiac catheterization laboratory procedure includes diagnostic angiographic imaging information.

18. The medical system of claim 17, wherein: To determine the predicted amount of contrast agent based on the imaging data, the processing circuit is configured to: determining a classification of the current patient based on the imaging data; and The predicted amount of contrast medium for the current patient is determined based on amounts of contrast medium for other patients having the classification.

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