Coronary vessel reactivity determination using coronary stimulator and computer vision system

Abstract

An example medical system includes a memory configured to store angiography data of a patient, the angiography data including pre-stimulation angiography data and post-stimulation angiography data. The pre-stimulation angiography data includes at least one angiography image of a blood vessel including a patient captured prior to delivery of stimulation to the blood vessel. The post-stimulation angiography data includes at least one angiography image including the blood vessel captured after initiation of delivery of stimulation to the blood vessel. The medical system includes processing circuitry communicatively coupled to the memory. The processing circuit is configured to obtain pre-stimulation angiography data and to obtain post-stimulation angiography data. The processing circuit is configured to determine a dimensional difference of the blood vessel between the pre-stimulation angiography data and the post-stimulation angiography data and output an indication of the dimensional difference.

Description

[0001] This application claims the benefit of U.S. Provisional Application No. 63 / 586,751, filed September 29, 2023, the entire contents of which are incorporated herein by reference. Technical Field

[0002] This disclosure relates to the use of imaging during medical procedures. Background Technology

[0003] During medical procedures, clinicians can use imaging systems to visualize a patient's internal anatomy. These systems can display anatomical structures or medical devices and can be used to diagnose patient conditions or guide clinicians in navigating devices within the patient's body, such as moving a medical device to its intended location. Imaging systems use sensors to capture image data that can be displayed during medical procedures. These systems include angiography systems, computed tomography (CT) scanning systems (including coronary computed tomography angiography (CCTA) systems), fluoroscopy systems (e.g., isocentric C-arm fluoroscopy systems), intravascular ultrasound (IVUS) systems, other ultrasound imaging systems, optical coherence tomography (OCT) fractional flow reserve (FFR) systems, magnetic resonance imaging (MRI) systems, positron emission tomography (PET) systems, and other imaging systems. Summary of the Invention

[0004] Regional ischemia with non-obstructive coronary artery disease (INOCA) is a chronic condition of the coronary microvascular system. INOCA is a symptomatic condition characterized by the absence of significant flow restriction in the epicardial vessels. INOCA is associated with a higher mortality rate compared to the absence of INOCA. Currently, INOCA is primarily detected over a relatively long period due to user-unfriendly technologies and techniques unsuitable for current interventional cardiologist (IC) workflows.

[0005] INOCA encompasses several intrinsic forms: microvascular angina, vasospastic angina, and combinations of both. Guided medicine treatments specific to INOCA intrinsic forms have been clinically proven to improve INOCA symptoms. Therefore, accurately identifying the INOCA intrinsic form in a patient is desirable.

[0006] Traditionally, two tests are usually required to determine the endotype of INOCA a patient may have, allowing for the selection of an effective treatment. Currently, vasospastic angina (one of the endotypes of INOCA) is identified by intracoronary administration of the drug acetylcholine. Clinicians then measure the vascular reactivity of the coronary arteries to the drug via angiography (e.g., an acetylcholine receptor antibody (ACH) test). If the artery receiving the acetylcholine injection contracts more than 90 percent (90%), the patient is diagnosed with vasospastic angina. However, acetylcholine is often unavailable, and when it is available to confirm the presence of vasospastic angina, it is usually administered directly into the patient's coronary arteries.

[0007] Because the determination of the endotype of INOCA can influence treatment options, because acetylcholine may be relatively difficult to obtain, and because some patients may have adverse reactions to the administration of acetylcholine (such as allergic reactions), it is desirable to determine whether a particular patient has vasospastic angina without requiring the delivery of acetylcholine to the patient.

[0008] The techniques disclosed herein can provide a safer, faster, and easier identification of vasospastic angina in patients. This disclosure relates to identifying the presence of vasospastic angina in a patient and / or identifying and recommending treatment for that patient. This disclosure describes techniques for identifying vasospastic angina without the need for intracoronary medications such as acetylcholine. Such techniques may include electrical stimulation of the coronary arteries via electrodes of a device, stimulation of the sympathetic nervous system response in the coronary arteries using temperature stimulation (e.g., a cold pressurization test), and / or utilizing computer vision techniques to remove ambiguities in “eyeball” measurements to more accurately and automatically measure any response to stimulation in angiographic data.

[0009] In one example, this disclosure describes a medical system comprising: a memory configured to store angiographic data of a patient, the angiographic data including pre-stimulation angiographic data comprising at least one angiographic image of the patient's blood vessel captured prior to delivery of stimulation to the blood vessel, the angiographic data also including post-stimulation angiographic data comprising at least one angiographic image of the blood vessel captured after initiation of delivery of stimulation to the blood vessel; and processing circuitry communicatively coupled to the memory, the processing circuitry being configured to: acquire the pre-stimulation angiographic data; acquire the post-stimulation angiographic data; determine a dimensional difference in the blood vessel between the pre-stimulation angiographic data and the post-stimulation angiographic data; and output an indication of the dimensional difference.

[0010] In another example, this disclosure describes a method comprising: obtaining pre-stimulation angiographic data, the pre-stimulation angiographic data including at least one angiographic image of the vessel captured prior to delivery of stimulation to the vessel; obtaining post-stimulation angiographic data, the post-stimulation angiographic data including at least one angiographic image of the vessel captured after initiation of delivery of stimulation to the vessel; determining a dimensional difference in the vessel between the pre-stimulation angiographic data and the post-stimulation angiographic data; and outputting an indication of the dimensional difference.

[0011] In yet another example, this disclosure describes a non-transitory computer-readable medium comprising instructions that, when executed, cause processing circuitry to: acquire pre-stimulation angiographic data comprising at least one angiographic image of the vessel containing a patient captured prior to delivery of stimulation to the vessel; acquire post-stimulation angiographic data comprising at least one angiographic image of the vessel containing at least one angiographic image of the vessel captured after initiation of delivery of stimulation to the vessel; determine a dimensional difference in the vessel between the pre-stimulation angiographic data and the post-stimulation angiographic data; and output an indication of the dimensional difference.

[0012] These and other aspects of this disclosure will become apparent from the following detailed description. However, in no event should the foregoing summary be construed as a limitation on the claimed subject matter, which is defined solely by the appended claims.

[0013] The present invention is intended to provide an overview of the subject matter described herein. It is not intended to provide an exclusive or exhaustive interpretation of the devices and methods described in detail in the following drawings and description. Further details of one or more examples are set forth in the following drawings and description. Attached Figure Description

[0014] Figure 1 This is a schematic perspective view of an example of a system for determining an indication of an MVO according to one or more aspects of this disclosure.

[0015] Figure 2 yes Figure 1 A schematic diagram of an example of a computing system.

[0016] Figure 3 This is a conceptual diagram illustrating an example guidewire with electrodes according to one or more aspects of this disclosure.

[0017] Figure 4 This is a conceptual diagram illustrating an example angioplasty balloon device with electrodes according to one or more aspects of this disclosure.

[0018] Figure 5 This is a conceptual diagram illustrating an example microcatheter with electrodes according to one or more aspects of this disclosure.

[0019] Figure 6 This is a conceptual diagram illustrating an example spiral or helical microcatheter with electrodes according to one or more aspects of this disclosure.

[0020] Figure 7 This is a conceptual diagram illustrating an example guiding catheter with electrodes according to one or more aspects of this disclosure.

[0021] Figure 8 This is a conceptual diagram illustrating an example guided extension catheter with electrodes according to one or more aspects of this disclosure.

[0022] Figure 9 This is a conceptual diagram illustrating an example cold pressure test according to one or more aspects of this disclosure.

[0023] Figure 10 This is a conceptual diagram illustrating an example device for stimulating the sympathetic nervous system response to coronary arteries, according to one or more aspects of this disclosure.

[0024] Figure 11 This is a conceptual diagram illustrating an example handle that can be used with a device configured to deliver stimulation to a coronary artery vessel, according to one or more aspects of this disclosure.

[0025] Figure 12A and Figure 12B This is a conceptual diagram illustrating blood vessels before and after the delivery of stimuli.

[0026] Figure 13 This is a flowchart illustrating an example machine learning model verification technique according to one or more aspects of this disclosure.

[0027] Figure 14 This is a conceptual diagram illustrating an example machine learning model according to one or more aspects of this disclosure.

[0028] Figure 15 This is a conceptual diagram illustrating an example training process for a machine learning model according to one or more aspects of this disclosure. Detailed Implementation

[0029] As discussed above, INOCA is a chronic disease of the coronary microvascular system. Patients experiencing INOCA are typically stable and have not experienced ST-segment elevation myocardial infarction (STEMI). If left undiagnosed and untreated, the prognosis for patients with INOCA is an increased risk of serious cardiac events. Patients with INOCA may have a high symptom burden affecting their quality of life and may have an increased incidence of major serious cardiac events, including myocardial infarction.

[0030] Based on intraocular modeling, guided medical treatment has been clinically proven to improve INOCA symptoms. The CorMicA trial demonstrated improved outcomes when invasive coronary function tests during angina were used to stratify medical treatments.

[0031] INOCA identification can occur in a catheter insertion laboratory (Cath lab). Invasive coronary angiography can be used to initially include or exclude any obstructive coronary artery disease (CAD). When clinicians do not see obstructive CAD in the angiographic data, they can use a pressure-temperature wire to determine the fractional flow reserve (FFR) that can be used to further include or exclude obstructive CAD. Clinicians can also use a pressure-temperature wire, along with the administration of adenosine, to measure the fractional coronary flow reserve (CFR) and the microvascular resistance index (IMR).

[0032] Clinicians can then determine vascular reactivity through intracoronary infusion of acetylcholine (ACH test) and visually monitor for any reduction in vessel diameter under assessment. If the reduction is greater than 90%, the clinician can determine that vasospastic angina is present in the patient.

[0033] In healthy individuals, administration of acetylcholine to the coronary arteries typically results in a vasodilatory response, thereby increasing the diameter of the coronary arteries and increasing blood flow to the heart. However, in individuals with coronary artery disease (CAD), the response to acetylcholine may be aberrant, leading to unusual vasoconstriction and reduced blood flow to the heart.

[0034] In patients with vasospastic angina, endothelial cells may be dysfunctional and unable to release nitric oxide in response to acetylcholine. Additionally, other vasoconstrictive substances such as endothelin-1 and thromboxane A2 can be released in response to acetylcholine, further contributing to coronary artery spasm in patients with vasospastic angina. The underlying mechanisms of aberrant vascular reactivity in vasospastic angina are not fully understood but are thought to involve complex interactions of various signaling pathways and factors, including endothelial dysfunction, smooth muscle hyperresponsiveness, and autonomic nervous system dysfunction.

[0035] According to the technology disclosed herein, a medical system can utilize a stimulation device to stimulate a patient's coronary arteries to promote a measurable vascular response. In some examples, the stimulation device may be configured to deliver electrical stimulation to the coronary arteries via electrodes. In such embodiments, the stimulation device may include a device with attached electrodes, such as a guidewire, a conventional balloon angioplasty (POBA) balloon, a microcatheter, a guiding catheter, a diagnostic catheter, or a guiding extension catheter. In some examples, the stimulation device may be configured to deliver temperature stimulation to the coronary arteries to stimulate a sympathetic nervous system response. The use of stimulation can replace the need for infusion of acetylcholine into a patient's coronary arteries.

[0036] The techniques disclosed herein can also utilize computer vision techniques to analyze angiographic data, including pre-stimulation and post-stimulation angiographic images. Using computer vision techniques to determine differences (such as differences in vessel size (e.g., diameter)) between pre-stimulation and post-stimulation angiographic images can improve the accuracy of identifying vasospastic angina.

[0037] Figure 1 This is a schematic perspective view of an example of a system for determining indications for vasospastic angina according to one or more aspects of this disclosure. System 100 includes a display device 110, a platform 120, an imager 140, and a computing device 150. System 100 may be an example of a system for use in an emergency room or catheterization lab. In some examples, system 100 may include other devices, not shown for simplicity. In some examples, system 100 may also include a server 160, which may be located in the same location as other devices of system 100, or may be located elsewhere. System 100 may be used during medical procedures, such as interventional medical procedures like PCI and / or diagnostic medical procedures.

[0038] The computing device 150 may include, for example, readily available devices (such as laptop computers, desktop computers, tablet computers, smartphones, or other similar devices), or may include dedicated devices. Therefore, the computing device 150 may be an external computing device located outside the patient's body. The computing device 150 may perform various control functions regarding the imager 140. In some examples, the computing device 150 may include a guidance workstation. The computing device 150 may control the operation of the imager 140 and receive the output of the imager 140, and may receive angiographic data from the imager 140. The computing device 150 may execute computer vision models to determine dimensional differences in blood vessels in different angiographic data, such as the diameter of the blood vessel before and after stimulation is delivered to the blood vessel. As used herein, "after" can be the time during stimulation delivery or within a relatively short period of time, such as less than 30 seconds after stimulation delivery is stopped.

[0039] Display device 110 can be configured to output instructions, images, and messages related to medical procedures. For example, display device 110 can display angiographic data obtained by imager 140, indications of differences in vessel size between pre-stimulation and post-stimulation angiographic data, indications of the presence of vasospastic angina, and recommendations for treatment for the patient. Platform 120 can be, for example, an operating console or other platform suitable for medical procedures.

[0040] exist Figure 1 In the examples, an imager 140, such as an angiography imager (or other imaging device), can be used to image relevant portions of a patient's anatomy during medical procedures to visualize the anatomical structure, characteristics, and location of lesions or other problems within the patient's body by generating imaging data. Thus, the imager 140 can capture angiography data. Although primarily described herein as an angiography imager, the imager 140 can be any type of imaging device, such as an angiography device, fluoroscopy device, CT device, CCTA device, IVUS device, OCT-FFR device, MRI device, PET device, or ultrasound device. In some examples, the imager 140 may represent more than one imaging device, such as multiple of any of the aforementioned devices.

[0041] Imager 140 can image regions of interest within a patient's body. Specific regions of interest can depend on anatomy, medical protocols, and / or patient symptoms. For example, when performing cardiac medical protocols, the vascular system and / or a portion of the heart may be within the region of interest.

[0042] The stimulation device 170 may include a means configured to deliver electrical stimulation to a patient's blood vessels via electrodes and / or a means configured to deliver thermal stimulation to a patient's blood vessels. For example, the stimulation device 170 may include a guidewire, an angioplasty balloon, a microcatheter, a guiding catheter, a diagnostic catheter, a guiding extension catheter, a container of ice water, a thermal cuff, etc. Later in this disclosure, regarding... Figures 3 to 10 Various examples of the apparatus 170 are described in more detail.

[0043] The lining of blood vessels (called the endothelium) plays a crucial role in regulating vascular tone and blood flow by releasing various vasodilators and vasoconstrictors. Electrical stimulation via electrodes can induce vasodilation by stimulating the interior of the coronary artery wall. This technique, known as electrical endothelial stimulation, involves applying a low-level current (or voltage) to the endothelial cells lining the vessel to stimulate the release of nitric oxide and other vasodilators. Temperature stimulation from lowering the temperature of the patient's skin can produce similar effects to those in the coronary arteries. In patients with vasospastic angina, the endothelial cells may have an impaired response to stimulation, resulting in unusual vasoconstriction of the coronary arteries instead of the expected vasodilation.

[0044] In an example of an electrical stimulation device, the electrodes of the stimulation device 170 (which may include one or more combinations of multiple electrodes) may contact the blood vessel wall and apply a relatively low level of current (or voltage) to the endothelial cells. While the current is being delivered, the processing circuitry 204 may use a computer vision model 222 to monitor the resulting vascular reactivity and / or changes in blood flow.

[0045] Generally, the current used for stimulation in the example of electrical stimulation via stimulation device 170 is in the microampere range, and the frequency is in the kilohertz range. Specific amplitude and pulse parameters may vary depending on the desired effect and the type of electrode used. In some examples, the electrode and / or stimulation parameters may be configured to specifically target endothelial cells rather than smooth muscle cells.

[0046] In some examples, the computing device 150 and the stimulation device 170 may be communicatively coupled, for example, via wired, optical, or wireless communication. Therefore, in some examples, the delivery of stimulation via the stimulation device 170 may be controlled by the computing device 150. In some examples, the stimulation device 170 may be configured to notify the computing device 150 of the timing of stimulation delivery. For example, the stimulation device 170 may communicate with the computing device 150 to notify it that stimulation is starting, stimulation is being delivered, and / or stimulation is being terminated. In this way, the computing device 150 may determine which angiographic images captured by the imager 140 are pre-stimulation images and which are post-stimulation images.

[0047] The computing device 150 may be communicatively coupled to the imager 140, the stimulation device 170, the display device 110, and / or the server 160, for example, via wired, optical, or wireless communication. The server 160 may be a hospital server or a cloud-based server, located in or not located in a hospital's emergency room or catheterization lab. The server 160 may be configured to store patient imaging data (such as angiography data) or electronic healthcare or medical records. In some examples, the server 160 may be configured to perform computer vision modeling and / or perform one or more, or some, of the determinations discussed herein.

[0048] Any or any combination of computing device 150, imager 140, and / or server 160 may include a computer vision model. For example, computing device 150, imager 140, and / or server 160 may acquire pre-stimulation angiographic data and post-stimulation angiographic data, for example, via imager 140. Computing device 150, imager 140, and / or server 160 may execute a computer vision model to determine the difference in vessel size between the pre-stimulation and post-stimulation angiographic data. Computing device 150, imager 140, and / or server 160 may output an indication of the size difference. The indication of the size difference may include the percentage difference (e.g., reduction) in the diameter of the vessels represented in the pre-stimulation and post-stimulation angiographic data. If the size difference meets a threshold, such as greater than 90%, this may indicate the presence of vasospastic angina in the patient. The indication of the size difference may include an indication of the percentage difference between vessel diameters and / or an indication of the presence or absence of vasospastic angina. The computing device 150, imager 140, and / or server 160 may output indications of size differences for display on the display device 110. For example, the indications of size differences may be intended to be visually displayed for review by a clinician. In some examples, the indications may not be visual, or may include visual elements as well as other elements, such as auditory or tactile. For example, the computing device 150, imager 140, and / or server 160 may output a representation of the indications of size differences for display, for example, on the display device 110. For example, the display device 110 may overlay a representation of the indications of size differences onto a live angiographic image from the imager 140. Since multiple images of the blood vessel may be taken before stimulation is delivered to the vessel and after stimulation is initiated, in some examples, the size difference may be the largest difference in the size of the blood vessel between the angiographic images. For example, the computing device 150, imager 140, and / or server 160 may determine the size difference as the difference between the largest diameter of the blood vessel represented in the pre-stimulation image and the smallest diameter of the same blood vessel represented in the post-stimulation image.

[0049] By identifying and outputting a representation indicating the size difference of the stimulated vessel between pre-stimulation and post-stimulation angiographic data, system 100 can assist clinicians in more effectively determining whether a patient has vasospastic angina and / or how to treat potential INOCA within the patient. The techniques of this disclosure can be performed simultaneously during the same medical procedure to determine whether a patient has CAD, eliminating the need for additional diagnostic procedures to determine whether treatment is appropriate or which treatment option should be sought for the patient. Therefore, the techniques of this disclosure can improve patient outcomes because patients can be treated better in a more timely manner, and / or improve the efficiency of medical facilities because subsequent diagnostic procedures may not be necessary to determine potential treatment for the patient.

[0050] Figure 2 yes Figure 1 This is a schematic diagram of an example of a computing device 150 of system 10. The computing device 150 may include a workstation, desktop computer, laptop computer, smartphone, tablet, dedicated computing device, or any other computing device capable of performing the techniques of this disclosure.

[0051] The computing device 150 can be configured to perform processing, control, and other functions associated with the imager 140. For example... Figure 2 As shown, computing device 150 may represent multiple instances of computing devices, each of which may be associated with imager 140. Computing device 150 may include, for example, memory 202, processing circuitry 204, display 206, network interface 208, input device 210 and / or output device 212, each of which may represent any instance of a plurality of such devices within a computing system for ease of description.

[0052] Although processing circuit 204 appears Figure 2 In computing device 150, however, in some examples, features attributable to processing circuitry 204 may be executed by processing circuitry of any of computing device 150, imager 140, or server 160, or combinations thereof. In some examples, one or more processors associated with processing circuitry 204 in a computing system may be distributed and shared across any combination of computing device 150, imager 140, and server 160. Computing device 150 may be used to perform any of the techniques described in this disclosure and may, individually or in combination with other components such as computing device 150, imager 140, server 160, or components of a system including any or all of such systems / devices, form all or part of an apparatus or system configured to perform such techniques.

[0053] The memory 202 of the computing device 150 includes any non-transitory computer-readable storage medium for storing data or software that can be executed by the processing circuitry 204 and, where applicable, control the operation of the computing device 150 and / or the imager 140. It should be noted that the memory 202 may include one or more memory devices. In one or more examples, the memory 202 may include one or more solid-state storage devices, such as flash memory chips. In one or more examples, the memory 202 may include one or more mass storage devices connected to the processing circuitry 204 via a mass storage controller (not shown) and a communication bus (not shown).

[0054] While the description of computer-readable media herein refers to solid-state storage devices, those skilled in the art will understand that computer-readable storage media can include any available medium accessible by processing circuitry 204. That is, computer-readable storage media includes 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 and / or other solid-state memory technologies, CD-ROM, DVD, Blu-ray and / or other optical storage, magnetic tape cassettes, magnetic tape, disk storage and / or other magnetic storage devices, and / or any other medium that can be used to store desired information and is accessible by computing device 150. In one or more examples, computer-readable storage media may be stored in the cloud or remote storage and accessed via at least one wired or wireless connection using any suitable one or more technologies.

[0055] Memory 202 may store pre-stimulation angiographic data 214 and post-stimulation angiographic data 216. Pre-stimulation angiographic data 214 may include one or more angiographic images acquired during a medical procedure, such as from imager 140, prior to stimulation delivery to a blood vessel. In some examples, pre-stimulation angiographic data 214 may also include images acquired during a previous diagnostic angiographic medical procedure. Angiographic data may be acquired from imager 140 and stored in pre-stimulation angiographic data 214 while the medical procedure is being performed in time between stimulation deliveries. This angiographic data may be displayed via display 206 and / or display device 110 and may be used by clinicians when navigating medical devices through the patient's anatomy, such as navigating stimulation device 170 to a location within the patient's body to initiate stimulation delivery.

[0056] Post-stimulation angiographic data 216 may include one or more angiographic images acquired during a medical procedure, such as from imager 140, after stimulation has begun to be delivered to the vessel. Stimulation of a vessel in a patient with vasospastic angina may cause the vessel diameter to constrict. Therefore, it may be desirable to capture both pre-stimulation angiographic data 214 and post-stimulation angiographic data 216. In some examples, post-stimulation angiographic data 216 includes one or more angiographic images acquired while stimulation is being delivered to the vessel via stimulation device 170. In some examples, post-stimulation angiographic data 216 includes one or more angiographic images acquired after stimulation delivery has ceased (e.g., within 30 seconds of cessation of stimulation delivery).

[0057] In some examples, the delivery of stimulation may occur more than once during the medical procedure. In such cases, pre-stimulation angiography data 214 may include one or more images acquired before stimulation is delivered to each vessel to which stimulation is to be delivered, each location of the one or more vessels to which stimulation is to be delivered, etc. In such cases, pre-stimulation angiography data 216 may include one or more images acquired after stimulation has begun to be delivered to each vessel to which stimulation is to be delivered, each location of the one or more vessels to which stimulation is to be delivered, etc.

[0058] Pre-stimulation angiography data 214 and post-stimulation angiography data 216 can be generated by the imager 140 of the patient's anatomy and obtained by the computing device 150 via a network interface 208 communicatively coupled to the imager 140. In some examples, the imager 140 can generate other types of imaging data, such as when the imager 140 represents more than one imaging device.

[0059] For example, pre-stimulation angiography data 214 and post-stimulation angiography data 216 can be obtained from imager 140 ( Figure 1 The processing circuit 204 can acquire angiographic images of pre-stimulation angiographic data 214 and post-stimulation angiographic data 216 from the imager 140, and store the angiographic images from the pre-stimulation angiographic data 214 and post-stimulation angiographic data 216 in memory 202, respectively. The processing circuit 204 can execute a user interface 218 to enable the display 206 (and / or Figure 1 The display device 110 presents a user interface 218 to one or more clinicians performing medical procedures. The user interface 218 may display pre-stimulation angiographic data 214 and / or post-stimulation angiographic data 216. The user interface 218 may also display one or more indications of the vascular size difference between the pre-stimulation angiographic data 214 and the post-stimulation angiographic data 216, such as an indication of the presence of vasospastic angina.

[0060] The memory 202 may also store the computer vision model 222 and the user interface 218. The computer vision model 222 may be configured, when executed by the processing circuitry 204, to compare any of the pre-stimulation angiographic data 214 with any of the post-stimulation angiographic data 216, such as to determine the size difference of the stimulated blood vessel between the pre-stimulation angiographic data 214 and the post-stimulation angiographic data 216. In some examples, the size difference is the maximum difference between the size of a specific stimulated blood vessel in the pre-stimulation angiographic data 214 and the post-stimulation angiographic data 216. For example, if the size difference between the diameters of stimulated blood vessels in the pre-stimulation angiographic data 214 and the post-stimulation angiographic data 216 is reduced by more than 90%, this may indicate that the patient has vasospastic angina. For example, the computer vision model 222 may be configured to identify blood vessels in the post-stimulation angiographic data 216 that are undergoing or have already undergone stimulation. The computer vision model 222 can also be configured to identify the same blood vessel in the pre-stimulation angiography data 214. The computer vision model 222 can be configured to determine size differences, such as the difference in diameter of the stimulated blood vessel between the pre-stimulation angiography data 214 and the post-stimulation angiography data 216. In some examples, the computer vision model 222 can be configured to determine whether the size difference meets a threshold. For example, the computer vision model 222 can be configured to determine whether the size difference is greater than (or greater than or equal to) a 90% reduction in the diameter of the blood vessel based on the application of stimulation to the blood vessel.

[0061] Processing circuit 204 can use pre-stimulation angiographic data 214 and post-stimulation angiographic data 216 to determine the presence of vasospastic angina in a patient. For example, processing circuit 204 can execute a computer vision model to determine the presence of vasospastic angina in a patient and / or determine whether the difference between the diameter of the blood vessels in pre-stimulation angiographic data 214 and post-stimulation angiographic data 216 meets a threshold, such as a reduction of more than (or greater than or equal to) 90%.

[0062] In some examples, the computer vision model 222 may include a machine learning model. In such examples, angiographic images of a patient's blood vessels may be used to train the computer vision model 222 to identify blood vessels and / or their diameters. In some examples, the computer vision model 222 may be trained to identify blood vessels in pre-stimulation angiographic data 214, corresponding to blood vessels in post-stimulation angiographic data 216, by using reference points in anatomical structures in pre-stimulation angiographic data 214 and post-stimulation angiographic data 216, which include a portion of the stimulation device 170, such as electrodes of the stimulation device 170. In some examples, the angiographic images may be annotated by a trained human to identify blood vessels and / or their diameters. In examples where the computer vision model 222 includes a machine learning model, the machine learning model may include Naive Bayes, k-nearest neighbors, random forest, support vector machine, neural network, linear regression, logistic regression, etc.

[0063] Stimulation device 170 ( Figure 2 (Not shown) can be connected or coupled (wirelessly, wired, Bluetooth, etc.) to computing device 150 via network interface 208. Computing device 150 can utilize computer vision model 222 to monitor responses to stimulation in angiographic data, for example, to compare pre-stimulation angiographic data 214 with post-stimulation angiographic data 216. This comparison may include comparing any one of one or more images (or frames) of pre-stimulation angiographic data 214 with any one of one or more images (or frames) of post-stimulation angiographic data 216. By coupling to stimulation device 170, computing device 150 can determine when stimulation is delivered, and thus determine which images of the angiographic data are pre-stimulation images and which are post-stimulation images.

[0064] In some examples, computing device 150 may prompt a clinician to use imager 140 to capture a movie of angiographic image data, or may control imager 140 to capture a movie of angiographic image data before or automatically before delivery of stimulation, and may capture a movie of angiographic image data during and / or after delivery of stimulation. Thus, processing circuitry 204 can obtain angiographic images before, during, and / or after stimulation is delivered. Processing circuitry 204 executing computer vision model 222 can compare post-stimulation angiographic data 216 with pre-stimulation angiographic data 214 to determine the extent of any reduction in the size (e.g., diameter) of the coronary artery vessels caused by stimulation. For example, processing circuitry 204 executing computer vision model 222 can determine a percentage reduction in the coronary artery vessels. Processing circuitry 204 can then output a representation of the reduction or an indication that the reduction meets a predetermined threshold. For example, a predetermined threshold may be set such that meeting the predetermined threshold indicates the presence of vasospastic angina in the patient. In some examples, the predetermined threshold may be 90%. In some examples, the predetermined threshold is met if the reduction is greater than 90%. In some examples, a predetermined threshold is met if the reduction is greater than or equal to 90%.

[0065] Processing circuitry 204 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, control of any function by processing circuitry 204 may be implemented directly or in conjunction with any suitable electronic circuitry appropriate for the specified function. Fixed-function circuitry refers to circuitry that provides specific functionality and is pre-configured for executable operations. Programmable circuitry refers to circuitry that can be programmed to perform various tasks and provide flexible functionality in executable operations. For example, programmable circuitry may execute software or firmware that causes the programmable circuitry to operate in a manner defined by the instructions of the software or firmware. Fixed-function circuitry may execute software instructions (e.g., receive or output parameters), but the type of operation performed by fixed-function circuitry is generally immutable. In some examples, one or more units within the unit may be different circuit blocks (fixed-function or programmable), and in some examples, the one or more units may be integrated circuits.

[0066] 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 circuits. Therefore, the term "processing circuit 204" as used herein may refer to one or more processors having any of the foregoing processors or processing structures, or any other structure suitable for implementing the techniques described herein. Furthermore, in some aspects, the functionality described herein may be provided within dedicated hardware or software modules configured for encoding and decoding, or incorporated into combined codecs. Additionally, these techniques may be fully implemented in one or more circuit or logic elements.

[0067] The display 206 may be touch-sensitive or voice-activated, enabling it to function as both an input and output device. Alternatively, a keyboard (not shown), a mouse (not shown), or other data input devices (e.g., input device 210) may be used.

[0068] Network interface 208 may be adapted to connect to a network, such as a local area network (LAN), wide area network (WAN), wireless mobile network, Bluetooth network, or the Internet, including wired or wireless networks. For example, computing device 150 may obtain angiographic data (e.g., pre-stimulation angiographic data 214 and post-stimulation angiographic data 216) from imager 140 during a medical procedure and / or obtain information from stimulation device 170 regarding whether stimulation is occurring (e.g., the start of stimulation, stimulation in progress, and / or the cessation of stimulation) during a medical procedure. Computing device 150 may receive updates to its software (e.g., application 217) via network interface 208. Computing device 150 may also display a notification on display 206 that a software update is available.

[0069] Input device 210 may include any device that enables a user to interact with computing device 150, 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 voice interface.

[0070] Output device 212 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.

[0071] Application 217 may be one or more software programs stored in memory 202 and executed by processing circuitry 204 of computing device 150. Processing circuitry 204 may execute user interface 218, which may display pre-stimulation angiographic data 214, post-stimulation angiographic data 216, and / or indications 228. Indications 228 may include any indications generated by computing device 150 relating to the size difference of the stimulated vessel between pre-stimulation angiographic data 214 and post-stimulation angiographic data 216, such as the degree of reduction in vessel size (e.g., diameter), whether the reduction meets a threshold, and / or the presence of vasospastic angina. Indications 228 may include indications generated by stimulation device 170 or other devices, such as indications that stimulation is being delivered, stimulation has not been delivered, stimulation delivery has begun, and / or stimulation delivery has stopped. Indications 228 may also include indications of recommended treatment for the patient that computing device 150 may generate based on the size difference of the vessel between pre-stimulation angiographic data 214 and post-stimulation angiographic data 216. For example, based on the fact that the size difference of the stimulated blood vessel between the pre-stimulation angiography data 214 and the post-stimulation angiography data 216 meets a predetermined threshold, the computing device 150 can output instructions to the patient for recommended treatment, such as the administration of a channel blocker and / or a beta blocker.

[0072] Figure 3 This is a conceptual diagram illustrating an example guidewire with electrodes according to one or more aspects of this disclosure. The guidewire 300 may be an example of a stimulation device 170. The guidewire 300 includes a guidewire body 302 on which a plurality of electrodes, such as electrodes 304A-304B (collectively, “electrodes 304”), are disposed. Electrodes 304 may include any number of electrodes, more than one, and may be located at any position on the guidewire body 302. In some examples, one or more electrodes of the electrodes 304 may be disposed around the circumference of the guidewire body 302. In some examples, one or more electrodes of the electrodes 304 may be disposed only on a portion of the circumference of the guidewire body 302. Electrodes 304 may be configured to deliver electrical stimulation to a patient's coronary artery, for example, to the inner wall of the coronary artery. A computing device 150 may monitor the response of the coronary artery to stimulation in angiographic data from an imager 140. For example, the computing device 150 can determine the difference in coronary artery size between pre-stimulation angiography data 214 and post-stimulation angiography data 216.

[0073] Figure 4This is a conceptual diagram illustrating an example angioplasty balloon device with electrodes according to one or more aspects of this disclosure. The angioplasty balloon device 400 may be an example of a stimulation device 170. The angioplasty balloon device 400 includes a balloon portion 402, which may be inflatable and has a plurality of electrodes disposed thereon, such as electrodes 404A-404B (collectively, "electrodes 404"). Electrodes 404 may include any number of electrodes, more than one, and may be located at any position on the balloon portion 402. In some examples, one or more electrodes of the electrodes 404 may be arranged around the circumference of the balloon portion 402. For example, one or more electrodes of the electrodes 404 may be configured to stretch together with the balloon portion 402 when the balloon portion 402 is inflated. In some examples, one or more electrodes of the electrodes 404 may be disposed only on a portion of the circumference of the balloon portion 402. Electrodes 404 may be configured to deliver electrical stimulation to a patient's coronary vessels, for example, to the inner wall of a coronary artery. The computing device 150 can monitor the response of coronary arteries to stimulation in angiographic data from the imager 140. For example, the computing device 150 can determine the difference in coronary artery size between pre-stimulation angiographic data 214 and post-stimulation angiographic data 216.

[0074] Figure 5 This is a conceptual diagram illustrating an example microcatheter with electrodes according to one or more aspects of this disclosure. Microcatheter 500 may be an example of stimulation device 170. Microcatheter 500 may be used with guidewire 502 to insert guidewire 502 into a patient's coronary blood vessel. Microcatheter 500 may have multiple electrodes disposed thereon, such as electrodes 504A-504B (collectively referred to as "electrode 504"). Electrode 504 may include any number of electrodes, more than one, and may be located at any location on the exterior of microcatheter 500. In some examples, one or more electrodes of electrode 504 may be disposed around the circumference of the exterior of microcatheter 500. In some examples, one or more electrodes of electrode 504 may be disposed only on a portion of the circumference of the exterior of microcatheter 500. Electrode 504 may be configured to deliver electrical stimulation to a patient's coronary artery, for example, to the inner wall of the coronary artery. Computing device 150 may monitor the response of the coronary vessels to stimulation in angiographic data from imager 140. For example, the computing device 150 can determine the difference in coronary artery size between pre-stimulation angiography data 214 and post-stimulation angiography data 216.

[0075] Figure 6This is a conceptual diagram illustrating an example spiral or helical microcatheter with electrodes according to one or more aspects of this disclosure. Guidewire 300 may be an example of stimulation device 170. Microcatheter 600 may be used with guidewire 502 to insert guidewire 502 into a patient's coronary blood vessel. Microcatheter 600 may have multiple electrodes disposed thereon, such as electrodes 604A-604D (collectively, "electrode 604"). Electrode 604 may include any number of electrodes, more than one, and may be located anywhere on the exterior of microcatheter 600. In some examples, one or more electrodes of electrode 604 may be disposed around the circumference of the exterior of microcatheter 600. In some examples, one or more electrodes of electrode 604 may be disposed only on a portion of the circumference of the exterior of microcatheter 600. Electrode 604 may be configured to deliver electrical stimulation to a patient's coronary artery, for example, to the inner wall of the coronary artery. Computing device 150 may monitor the response of the coronary artery to stimulation in angiographic data from imager 140. For example, the computing device 150 can determine the difference in coronary artery size between pre-stimulation angiography data 214 and post-stimulation angiography data 216.

[0076] Figure 7 This is a conceptual diagram illustrating an example guiding catheter with electrodes according to one or more aspects of the present disclosure. The guiding catheter 700 may be an example of a stimulation device 170. The guiding catheter 700 may be used in conjunction with a guidewire 702 to insert the guidewire 702 into a patient's coronary blood vessel. The guiding catheter 700 may include a guiding catheter body 706 having a plurality of electrodes disposed thereon, such as electrodes 704A-704B (collectively, "electrodes 704"). Electrodes 704 may include any number of electrodes, more than one, and may be located at any location on the exterior of the guiding catheter 700. In some examples, one or more of the electrodes 704 may be disposed around the circumference of the exterior of the guiding catheter 700. In some examples, one or more of the electrodes 704 may be disposed only on a portion of the circumference of the exterior of the guiding catheter 700. In some examples, electrodes 704 may be configured to physically contact the inner surface 710 of the coronary vessel when the guiding catheter 700 is inserted into the coronary vessel. Electrodes 704 may be configured to deliver electrical stimulation to the patient's coronary vessel, for example, to the inner wall of the coronary vessel. The computing device 150 can monitor the response of coronary vessels to stimulation in angiographic data from the imager 140. For example, the computing device 150 can determine the difference in coronary artery size between pre-stimulation angiographic data 214 and post-stimulation angiographic data 216.

[0077] Figure 8This is a conceptual diagram illustrating an example guide extension catheter with electrodes according to one or more aspects of this disclosure. The guide extension catheter 800 may be an example of a stimulation device 170. The guide extension catheter 800 may be used in conjunction with a guidewire 802 to assist in inserting the guidewire 802 into a patient's coronary blood vessel. The guide extension catheter 800 may include a guide extension catheter body 806 on which a plurality of electrodes, such as electrodes 804A-804B (collectively, "electrodes 804"), are disposed. Electrodes 804 may include any number of electrodes, more than one, and may be located anywhere on the exterior of the guide extension catheter 800. In some examples, one or more of the electrodes 804 may be disposed around the circumference of the exterior of the guide extension catheter 800. In some examples, one or more of the electrodes 804 may be disposed only on a portion of the circumference of the exterior of the guide extension catheter 800. Electrodes 804 may be configured to deliver electrical stimulation to a patient's coronary artery, for example, to the inner wall of the coronary artery. A computing device 150 may monitor the response of the coronary artery to stimulation in angiographic data from an imager 140. For example, the computing device 150 can determine the difference in coronary artery size between pre-stimulation angiography data 214 and post-stimulation angiography data 216.

[0078] Figures 3 to 8 Examples are illustrated as exemplary devices configured to deliver electrical stimulation to coronary arteries via electrodes. It should be understood that additional or alternative devices may be used to deliver electrical stimulation to coronary arteries via electrodes and are still within the scope of this disclosure.

[0079] In some examples, the stimulation device 170 may be a device configured to deliver a type of stimulation other than electrical stimulation. For example, the stimulation device 170 may be a device configured to deliver temperature stimulation, such as a cold pressurization test or other techniques for stimulating a sympathetic nervous system response that may cause coronary artery vasoconstriction.

[0080] Figure 9 This is a conceptual diagram illustrating an example cold pressurization test according to one or more aspects of this disclosure. Figure 9 In the example, the stimulation device 170 may include a container 904 of ice water 902. For example, a cold pressurization test may include placing a part of the body (e.g., a hand 900, such as the left hand) into a cold or chilled substance (such as ice water 902 held in container 904), or otherwise lowering the ambient temperature of that part of the body, typically for about 1-2 minutes, while measuring changes in blood pressure, heart rate, skin conductance, skin temperature, and / or other physiological parameters, for example, via one or more sensors (not shown). The act of placing the hand 900 into ice water 902 may stimulate a sympathetic nervous system response, thereby causing coronary artery constriction or narrowing and increasing blood pressure.

[0081] In one example, during a cold compression test, a device 906 (such as a finger cot or ring) may be placed on the patient's finger. The device may be configured to sense when the cold compression test begins and to indicate or determine when the system 100 should record sensed parameters and / or capture angiographic data. For example, the device 906 may be configured to sense physiological markers on the skin, such as temperature and / or electrical conductivity, based on which the device 906 and / or other devices of the system 100 can determine that the cold compression test has begun.

[0082] Device 906 may be coupled (e.g., wirelessly, wired, Bluetooth, etc.) to computing device 150 via network interface 208, for example, so that computing device 150 can obtain from device 906 a sensed physiological marker and / or an indication that a cold compression test has begun. In some examples, device 906 may similarly be coupled to imager 140. Because computing device 150 and / or imager 140 may be coupled to device 906, system 100 can determine when to apply stimulation affected by the cold compression test. In some examples, computing device 150 and / or imager 140 may prompt a clinician to capture a montage of angiographic data based on the applied cold compression test, or the imager 140 may be automatically controlled to capture a montage of angiographic data. The system may capture angiographic data before, during, and / or after stimulating a sympathetic nervous system response.

[0083] Figure 10 This is a conceptual diagram illustrating an example device for stimulating the sympathetic nervous system response to coronary arteries, according to one or more aspects of this disclosure. Figure 10 In the examples, devices such as cuff 1000 can be used to replicate or simulate cold compression testing. Although described as a cuff, the device can take other forms, such as gloves, patches, sleeves, etc. Cuff 1000 can be placed on the patient's wrist 1002, or in other examples, on another location on the patient. Cuff 1000 can be configured to cause a sudden drop in skin temperature (e.g., a relatively cold temperature), similar to... Figure 9 The sudden drop in temperature of the ice water 902 during the cold pressurization test promotes stimulation of the sympathetic nervous system. The cuff 1000 can be similar to... Figure 9 The container 904 is coupled to the computing device 150 and / or the imager 140.

[0084] Figure 11 This is a conceptual diagram illustrating an example handle that can be used with a device configured to deliver stimulation to a coronary artery vessel, according to one or more aspects of this disclosure. The computing device 1100 may be a reference. Figures 3 to 8 or Figure 10 Example device of any of the computing devices described.

[0085] Device 1100 may include a handle 1102 and a body 1104. The handle 1102 can be used to control the delivery of stimulation by device 1100. For example, the handle 1102 may include telemetry circuitry 1106, which may be configured to communicate with computing device 150 via network interface 208. Telemetry circuitry 1106 may be configured similarly to network interface 208 and may facilitate communication between device 1100 and computing device 150. In some examples, processing circuitry 204 may send commands to telemetry circuitry 1106 via network interface 208 to initiate and / or terminate the delivery of stimulation. In such examples, processing circuitry 204 may identify an angiographic image obtained before sending the command as pre-stimulation angiographic data 214 and an angiographic image obtained after sending the command as post-stimulation angiographic data 216.

[0086] Alternatively or additionally, the handle 1102 may include a stimulation button 1108. By pressing the stimulation button 1108, a clinician can initiate and / or terminate the delivery of stimulation, such as Figures 3 to 8 Examples of electrical stimulation or Figure 10 The example is a temperature-induced stimulus. In such an example, telemetry circuitry 1106 may send an instruction (such as a message) to network interface 208 to instruct computing device 150 when to deliver the stimulus. In this way, processing circuitry 204 may use the information received in the instruction from device 1100 to determine which angiographic images are pre-stimulation angiographic data 214 and which angiographic images are post-stimulation angiographic data 216.

[0087] Figures 12A to 12B This is a conceptual diagram illustrating blood vessels before and after the delivery of a stimulus. Figure 12A In the image, vessel 1200 represents the vessel depicted in the pre-stimulation angiography data 214. Vessel 1200 may be captured by the imager 140. Figure 12A The image shown is of a blood vessel to which stimulation was subsequently applied. Although for simplicity... Figure 12A Although not shown in the image, in some examples, at least a portion of the stimulation device 170 may be present in or around the blood vessel 1200 when the image is captured. In some examples, no portion of the stimulation device 170 may be present in or around the blood vessel 1200 when the image is captured.

[0088] Before the stimulus is delivered to the blood vessel 1200, the blood vessel 1200 may have a diameter 1202. In some examples, the processing circuitry 204 may execute a computer vision model 222 to determine the value of the diameter 1202.

[0089] exist Figure 12BIn the text, vessel 1204 represents the same vessel as vessel 1200, but in the angiography data 216 after stimulation. Therefore, vessel 1204 is the same vessel as vessel 1200, but after stimulation of the vessel is initiated. Although for simplicity... Figure 12B Although not shown in the diagram, in some examples, at least a portion of the stimulating device 170 may be present in the blood vessel 1204 when the image is captured. For example, the stimulating device 170 may actively deliver stimulation to a portion of the blood vessel 1204 when the image is captured.

[0090] Therefore, the size difference between blood vessel 1200 and blood vessel 1204 can be determined based on diameters 1202 and 1206. In an example where the size difference is determined as a percentage to be compared with, for example, a predetermined threshold (such as 90%), the processing circuit 204 can determine the size difference as (diameter 1202 - diameter 1206) / diameter 1202.

[0091] Figure 13 This is a flowchart illustrating an example technique for determining an MVO according to one or more aspects of this disclosure. The following describes processing circuitry 204. Figure 13 The technology, but such technology can be developed by Figure 1 The processing circuitry of the device depicted or capable of performing such technology may be used to perform the technique.

[0092] Processing circuitry 204 can acquire pre-stimulation angiographic data 214, which includes at least one angiographic image (1300) of a vessel containing the patient, captured before stimulation is delivered to the vessel. For example, imager 140 can capture one or more angiographic images of a vessel containing the vessel before system 100 delivers electrical and / or thermal stimulation to the vessel. Processing circuitry 204 can acquire one or more angiographic images from imager 140, for example, via network interface 208.

[0093] Processing circuitry 204 can acquire post-stimulation angiography data 216, which includes at least one angiography image (1302) of a vessel captured after initiation of stimulation to the vessel. For example, imager 140 can capture one or more angiography images of a vessel that has received electrical and / or thermal stimulation by stimulation device 170 after initiation of stimulation to the vessel. Processing circuitry 204 can acquire one or more angiography images from imager 140, for example, via network interface 208.

[0094] Processing circuitry 204 can determine the vascular size difference (1304) between pre-stimulation angiographic data 214 and post-stimulation angiographic data 216. For example, processing circuitry 204 can execute computer vision model 222 to determine the vascular size in pre-stimulation angiographic data 214 and post-stimulation angiographic data 216 and / or determine size differences, such as (diameter 1202 - diameter 1206) / diameter 1202 or another measure of the size difference between diameter 1202 and diameter 1206.

[0095] Processing circuit 204 can output an indication of dimensional difference (1306). For example, processing circuit 204 can control output device 212, display 206 and / or network interface 208 to output an indication of dimensional difference, such as an indication of (diameter 1202 - diameter 1206) / diameter 1202.

[0096] In some examples, as part of determining the size difference, processing circuitry 204 is configured to execute computer vision model 222. In some examples, processing circuitry 204 is also configured to determine whether the size difference meets a threshold; and based on determining that the size difference meets the threshold, output an indication that the size difference meets the threshold. In some examples, the size difference is the percentage reduction in the diameter of the vessel between pre-stimulation angiographic data 214 and post-stimulation angiographic data 216, and the threshold is a predetermined percentage, such as 90%. In some examples, the indication that the size difference meets the threshold includes an indication of the presence of vasospastic angina. In some examples, processing circuitry 204 is also configured to output a treatment recommendation for the patient based on determining that the size difference meets the threshold.

[0097] In some examples, processing circuitry 204 is also configured to control a device (e.g., stimulation device 170) to deliver a stimulus. In some examples, processing circuitry 204 is further configured to receive an instruction from the device to deliver a stimulus. In some examples, the stimulus includes at least one of electrical stimulation or temperature stimulation. In some examples, the blood vessel is a coronary artery.

[0098] Figure 14 This is a conceptual diagram illustrating an example machine learning model according to one or more aspects of this disclosure. Machine learning model 1400 may be an example of computer vision model 222. Machine learning model 1400 may be an example of a neural network (such as a convolutional neural network or other machine learning model) trained to compare pre-stimulation angiographic data 214 with post-stimulation angiographic data 216 to determine the size difference between the stimulated blood vessels in the pre-stimulation angiographic data 214 and post-stimulation angiographic data 216. For example, this size difference may be the difference in the size (e.g., diameter) of the blood vessel before and after stimulation.

[0099] One or more of computing devices 150 and / or servers 160 may train, store, and / or utilize machine learning model 1400, but in some examples, other devices of system 100 may apply input to machine learning model 1400. In some examples, various types of machine learning models or algorithms and deep learning models or algorithms may be utilized. For example, convolutional neural network models, such as ResNet-18, may be used. Some non-limiting examples of models that can be used for pass-through learning include AlexNet, VGGNet, GoogleNet, ResNet50, or DenseNet. Some non-limiting examples of machine learning techniques include support vector machines, Naive Bayes, k-nearest neighbors, multilayer perceptrons, random forests, neural networks, convolutional neural networks, recurrent neural networks, ensemble networks, decision trees, linear regression, logistic regression, long short-term memory, etc.

[0100] like Figure 14 As shown in the example, the machine learning model 1400 may include three types of layers. These three types of layers include an input layer 1402, a hidden layer 1404, and an output layer 1406. The output layer 1406 includes the output of the transfer function 1405 from the output layer 1406. The input layer 1402 represents each of the input values ​​X1 to X4 provided to the machine learning model 1400. In some examples, as described above, the input values ​​may include any of the values ​​input into the machine learning model. For example, as described above, the input values ​​may include angiographic data 214 and / or non-angiographic data 216.

[0101] Each input value from the input values ​​for each node in input layer 1402 is provided to each node in the first layer of hidden layer 1404. Figure 14 In the example, hidden layer 1404 comprises two layers, one with four nodes and the other with three nodes, but in other examples, fewer or more nodes may be used. Each input from input layer 1402 is multiplied by a weight, and then summed at each node in hidden layer 1404. During training of machine learning model 1400, the weights used for each input are adjusted to establish a relationship between blood vessels and / or their sizes in pre-stimulation angiography data 214 and post-stimulation angiography data 216. In some examples, one hidden layer may be incorporated into machine learning model 1400, or three or more hidden layers may be incorporated into machine learning model 1400, where each layer comprises the same or different numbers of nodes.

[0102] The results of each node within hidden layer 1404 are applied to the transfer function of output layer 1406. The transfer function can be linear or nonlinear, depending on the number of layers within the machine learning model 1400. An example nonlinear transfer function could be a sigmoid function or a rectified function. The output 1407 of the transfer function can be a classification, i.e., whether a specific vessel in pre-angiography data 214 is the same vessel in post-angiography data 216 after stimulation is delivered.

[0103] As illustrated in the example above, by applying the machine learning model 1400 to input data such as pre-stimulation angiography data 214 and / or post-stimulation angiography data 216, the processing circuit 204 is able to determine differences in the size of the stimulated vessels in the pre-stimulation angiography data 214 and / or post-stimulation angiography data 216. Such size differences can indicate the likelihood and / or severity of vasospastic angina in a patient. This improves the clinician's ability to determine whether and / or how to treat patients with vasospastic angina, thereby improving the patient's quality of life and / or reducing the likelihood of the patient experiencing cardiac events.

[0104] Figure 15 This is a conceptual diagram illustrating an example training process for a machine learning model according to one or more aspects of this disclosure. Process 1570 can be used to train machine learning model 1400. Machine learning model 1574 (which may be an example of machine learning model 1400) can be implemented using any number of models for supervised learning and / or reinforcement learning, such as, but not limited to, artificial neural networks, convolutional neural networks, recurrent neural networks, decision trees, Naive Bayes networks, support vector machines, k-nearest neighbor models, ensemble networks, to name just a few.

[0105] In some examples, one or more of the computing device 150 and / or server 160 initially train the machine learning model 1574 based on a corpus of training data 1572. Training data 1572 may include, for example, angiographic images, such as fluorescence fluoroscopy with contrast. In some examples, training data 1572 may include annotations indicating the same vessels in different angiographic images. In some examples, the different angiographic images may include pre-stimulation and post-stimulation angiographic data from multiple patients.

[0106] When training the machine learning model 1574, the processing circuitry of system 100 can compare the prediction or classification with the target output 1578 1576. Processing circuitry 204 can use the error signal from this comparison to train (learn / train 1580) the machine learning model 1574. Processing circuitry 204 can generate machine learning model weights or other modifications, which can be used to modify the machine learning model 1574. For example, processing circuitry 204 can modify the weights of the machine learning model 1574 based on learning / training 1280. For example, one or more of computing devices 150 and / or servers 160 can modify, for each training instance in training data 1572, the method for identifying the same vessel and / or determining its size differences in pre-stimulation angiography data 214 and post-stimulation angiography data 216 based on training data 1572.

[0107] The techniques described in this disclosure can be implemented, at least in part, in hardware, software, firmware, or any combination thereof. For example, various aspects of the described techniques can be implemented within 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 circuits, and any combination of such components. The terms “controller,” “processor,” or “processing circuit” generally refer to any of the aforementioned logic circuits individually or in combination with other logic circuits, or any circuit in any other equivalent circuit. A control unit including hardware can also perform one or more of the techniques disclosed herein. Such hardware, software, and firmware can be implemented within the same device or in separate devices to support the various operations and functions described in this disclosure. Furthermore, any of the described units, circuits, or components can be implemented together or independently as discrete but interoperable logic devices. 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 separate hardware or software components. Conversely, the functionality associated with one or more circuits or units may be performed by independent hardware or software components, or integrated within common or independent hardware or software components.

[0108] The techniques described in this disclosure can also be embedded or encoded in a computer-readable medium (such as a computer-readable storage medium) containing instructions. Instructions embedded or encoded in a computer-readable storage medium can cause a programmable processor or other processor to perform the method, for example, when executing those instructions. The computer-readable storage medium may include random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), or electrically erasable programmable read-only memory (EEPROM) or other computer-readable media.

[0109] This disclosure includes the following non-limiting embodiments.

[0110] Example 1. A medical system comprising: a memory configured to store angiographic data of a patient, the angiographic data including pre-stimulation angiographic data comprising at least one angiographic image of the blood vessel captured prior to delivery of stimulation to the blood vessel, the angiographic data further including post-stimulation angiographic data comprising at least one angiographic image of the blood vessel captured after initiation of delivery of stimulation to the blood vessel; and processing circuitry communicatively coupled to the memory, the processing circuitry configured to: acquire the pre-stimulation angiographic data; acquire the post-stimulation angiographic data; determine a dimensional difference of the blood vessel between the pre-stimulation angiographic data and the post-stimulation angiographic data; and output an indication of the dimensional difference.

[0111] Example 2. The medical system according to Example 1, wherein, as part of determining the size difference, the processing circuitry is configured to execute a computer vision model.

[0112] Example 3. The medical system according to Example 1 or Example 2, wherein the processing circuit is further configured to: determine whether the size difference meets a threshold; and based on determining that the size difference meets the threshold, output an indication that the size difference meets the threshold.

[0113] Example 4. The medical system according to Example 3, wherein the size difference is the percentage reduction in the diameter of the blood vessel between the pre-stimulation angiography data and the post-stimulation angiography data, and wherein the threshold is a predetermined percentage.

[0114] Example 5. The medical system according to Example 3 or Example 4, wherein the indication that the size difference meets the threshold includes an indication of the presence of vasospastic angina.

[0115] Example 6. A medical system according to any one of Examples 3 to 5, wherein the processing circuit is further configured to: output a treatment recommendation for the patient based on determining that the size difference meets the threshold.

[0116] Example 7. The medical system according to any one of Examples 1 to 6, wherein the processing circuit is further configured to: control the device to deliver electrical stimulation.

[0117] Example 8. The medical system according to Example 7, wherein the processing circuit is further configured to receive an instruction from the device to deliver electrical stimulation.

[0118] Example 9. A medical system according to any one of Examples 1 to 8, wherein the stimulation includes at least one of electrical stimulation or temperature stimulation.

[0119] Example 10. A medical system according to any one of Examples 1 to 9, wherein the blood vessel is a coronary artery.

[0120] Example 11. A method comprising: obtaining pre-stimulation angiographic data by a processing circuit of an external computing device, the pre-stimulation angiographic data including at least one angiographic image of the vessel containing a patient captured prior to delivery of stimulation to the vessel; obtaining post-stimulation angiographic data by the processing circuit, the post-stimulation angiographic data including at least one angiographic image of the vessel containing at least one angiographic image of the vessel captured after initiation of delivery of stimulation to the vessel; determining a size difference of the vessel between the pre-stimulation angiographic data and the post-stimulation angiographic data by the processing circuit; and outputting an indication of the size difference by the processing circuit.

[0121] Example 12. The method according to Example 11, wherein determining the size difference includes: performing a computer vision model.

[0122] Example 13. The method according to Example 11 or Example 12, the method further includes: determining by the processing circuit whether the size difference meets a threshold; and based on determining that the size difference meets the threshold, outputting by the processing circuit an indication that the size difference meets the threshold.

[0123] Example 14. The method according to Example 13, wherein the size difference is the percentage reduction in the diameter of the blood vessel between the pre-stimulation angiography data and the post-stimulation angiography data, and wherein the threshold is a predetermined percentage.

[0124] Example 15. The method according to Example 13 or Example 14, wherein the indication that the size difference meets the threshold includes an indication of the presence of vasospastic angina.

[0125] Example 16. The method according to any one of Examples 13 to 15, the method further comprising: based on determining that the size difference satisfies the threshold, the processing circuit outputs a treatment recommendation for the patient.

[0126] Example 17. The method according to any one of Examples 11 to 16, the method further comprising: delivering electrical stimulation by the processing circuit control device.

[0127] Example 18. The method according to Example 17, the method further comprising: receiving an instruction for delivering electrical stimulation from the device by the processing circuit.

[0128] Example 19. The method according to any one of Examples 11 to 18, wherein the stimulation includes at least one of electrical stimulation or temperature stimulation.

[0129] Example 20. A non-transitory computer-readable storage medium comprising instructions that, when executed, cause processing circuitry to: acquire pre-stimulation angiographic data, the pre-stimulation angiographic data including at least one angiographic image of the vessel captured prior to delivery of stimulation to the vessel; acquire post-stimulation angiographic data, the post-stimulation angiographic data including at least one angiographic image of the vessel captured after initiation of delivery of stimulation to the vessel; determine a dimensional difference of the vessel between the pre-stimulation angiographic data and the post-stimulation angiographic data; and output an indication of the dimensional difference.

Claims

1. A medical system, the medical system comprising: A memory configured to store a patient's angiographic data, the angiographic data including pre-stimulation angiographic data comprising at least one angiographic image of the patient's vessel captured prior to delivery of stimulation to the vessel, and post-stimulation angiographic data comprising at least one angiographic image of the vessel captured after initiation of delivery of stimulation to the vessel; and Processing circuitry, communicatively coupled to the memory, is configured to: Obtain the pre-stimulation angiography data; Obtain the angiography data after the stimulation; Determine the difference in vessel size between the pre-stimulation angiographic data and the post-stimulation angiographic data; and Output an indication of the size difference.

2. The medical system of claim 1, wherein, as part of determining the size difference, the processing circuitry is configured to execute a computer vision model.

3. The medical system according to claim 1 or claim 2, wherein the processing circuit is further configured to: Determine whether the size difference meets a threshold; and Based on determining that the size difference meets the threshold, an indication that the size difference meets the threshold is output.

4. The medical system of claim 3, wherein the size difference is the percentage reduction in the diameter of the blood vessel between the pre-stimulation angiography data and the post-stimulation angiography data, and wherein the threshold is a predetermined percentage.

5. The medical system of claim 3 or claim 4, wherein the indication that the size difference meets the threshold includes an indication of the presence of vasospastic angina.

6. The medical system according to any one of claims 3 to 5, wherein the processing circuitry is further configured to: output a treatment recommendation for the patient based on determining that the size difference meets the threshold.

7. The medical system according to any one of claims 1 to 6, wherein the processing circuit is further configured to: control the device to deliver electrical stimulation.

8. The medical system of claim 7, wherein the processing circuitry is further configured to receive an instruction from the device to deliver electrical stimulation.

9. The medical system according to any one of claims 1 to 8, wherein the stimulation comprises at least one of electrical stimulation or temperature stimulation.

10. The medical system according to any one of claims 1 to 9, wherein the blood vessel is a coronary artery.

11. A method, the method comprising: Pre-stimulation angiography data is obtained by the processing circuitry of an external computing device, the pre-stimulation angiography data including at least one angiography image of the blood vessel captured before the delivery of stimulation to the blood vessel. The processing circuit obtains post-stimulation angiography data, which includes at least one angiography image of the blood vessel captured after the initiation of stimulation to the blood vessel. The processing circuit determines the difference in vessel size between the pre-stimulation angiography data and the post-stimulation angiography data; and The processing circuit outputs an indication of the size difference.

12. The method of claim 11, wherein determining the size difference comprises: Execute computer vision models.

13. The method according to claim 11 or claim 12, further comprising: The processing circuit determines whether the size difference meets the threshold. as well as Based on the determination that the size difference meets the threshold, the processing circuit outputs an indication that the size difference meets the threshold.

14. The method of claim 13, wherein the size difference is the percentage reduction in the diameter of the vessel between the pre-stimulation angiographic data and the post-stimulation angiographic data, and wherein the threshold is a predetermined percentage.

15. A non-transitory computer-readable storage medium storing instructions, said instructions causing processing circuitry to: Obtain pre-stimulation angiography data, which includes at least one angiography image of the vessel containing the patient captured before stimulation is delivered to the vessel. Obtain post-stimulation angiography data, which includes at least one angiography image of the vessel captured after initiating the delivery of stimulation to the vessel. Determine the difference in vessel size between the pre-stimulation angiographic data and the post-stimulation angiographic data; and Output an indication of the size difference.

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