System for predicting rupture or detachment of vascular plaques that could lead to a stroke and / or for predicting vascular thrombosis

Abstract

A system for predicting at least partial rupture or detachment of a vascular plaque that may lead to a stroke or for analyzing the biomechanical properties of the wall of an internal jugular vein in order to predict a risk of thrombosis, comprising: a monitoring device (2) including a temperature sensor (5) configured to measure the temperature of the vascular plaque or the wall of the jugular vein, a vibration sensor (4) configured to measure mechanical waves passing through the vascular plaque and the underlying walls, a motion sensor (6) configured to measure the movements of the plaque or the wall of the jugular vein (6), an electrical sensor configured to measure a parameter related to the electrical impedance of the plaque, an acoustic wave sensor, and a computing unit (10) configured to analyze the data from the monitoring device (2) by artificial intelligence.Figure from the summary: Figure 1.

Description

Title of the invention: System for predicting rupture or detachment of vascular plaque which may lead to a stroke and / or for predicting vascular thrombosis Technical field

[0001] The present disclosure relates to a system for predicting the rupture or detachment of vascular plaque at the level of the carotid or supra-aortic trunks which may lead to a stroke.

[0002] The present disclosure relates more particularly to a system and method for ensuring continuous monitoring of the evolution of the stability of vascular plaques present in the carotid or supra-aortic trunks of a patient and for predicting the occurrence of a stroke by rupture of vascular plaque. The system is also adapted to ensure continuous monitoring of the biomechanical properties of the internal jugular vein which crosses the carotid bifurcation to predict the occurrence of vascular thrombosis. Prior art

[0003] A stroke is linked to a sudden stoppage of blood circulation to all or part of the brain.

[0004] The brain is irrigated by the internal carotid artery and by all the supraaortic trunks, providing the oxygen necessary for its functioning. A process known as atheroma causes thickening and hardening of the arteries by the accumulation of lipids, complex carbohydrates, blood and calcareous deposits in the internal wall of the arteries, and leads to the progressive formation of one or more atheromatous plaques. There are also other causes of vascular plaque formation. This is a process whose progression is accelerated by identified risk factors, namely high blood pressure, diabetes, obesity, smoking. The presence of atheromatous plaques thus causes a reduction in the diameter of the artery, which is called "stenosis", a disease more generally called atherosclerosis. This plaque can also be of post-radiation or post-inflammatory origin or due to a collagen disease.In all these cases there is a risk of rupture. The presence of vascular plaque (due to atheroma or other origins) can be detected by various techniques, vascular Doppler ultrasound, optical coherence tomography, computed tomography or Magnetic Resonance Imaging.

[0005] A dramatic consequence of plaque rupture at the carotid or supra-aortic trunks results in the migration of certain components of the plaque into the bloodstream towards the brain, thus obstructing one of the cerebral arteries. rebral, causing an ischemic stroke. One of the other possible evolutionary risks of the atheromatous plaque which is rarer is a rupture of the plaque which results in the occlusion of the internal carotid artery, causing a low-flow hemodynamic stroke in the event of non-compensation of the circle of Willis. In each case, the dramatic consequences are correlated with the detachment or rupture of part or all of the plaque. The rupture of part of the plaque or the entire plaque is most often followed by a more or less calcified fibrino-cruoric embolus which can obstruct the cerebral artery.

[0006] The current assessment based solely on the degree of stenosis is far from sufficient and does not allow for the prediction of plaque rupture and the actual risk of stroke. It is therefore essential to assess the risk of rupture of the atheromatous plaque or other origins in order to precisely define the individual risk of stroke.

[0007] Currently, the assessment of the risk of vascular plaque rupture is mainly based on the morphology and tissue composition of the plaque. Indeed, morphological parameters such as the thickness of the fibrous cap, the size of the necrotic body and stenosis (the congestion of the artery lumen) are considered as factors influencing the occurrence of plaque rupture. Ruptured plaques are characterized by a large lipid core and a thin fibrous cap and intraplaque hemorrhage. However, these indicators do not allow to reliably predict the risk of vascular plaque rupture.

[0008] An aim of the present disclosure is to propose a system which makes it possible to continuously monitor the evolution of the state of the integrity of the vascular plaque in order to be able to predict the occurrence of a stroke caused by the rupture or detachment of plaque.

[0009] Another aim of the present disclosure is to propose a system and method for predicting the occurrence of a stroke which is not very restrictive for the patient and easy to use for the practitioner.

[0010] Another object of the present disclosure is to provide a system and method which make it possible to continuously monitor the evolution of the biomechanical properties of an internal jugular vein in order to predict the risk of vascular thrombosis. Summary

[0011] The present disclosure improves the situation.

[0012] A system is proposed for predicting at least a partial rupture or detachment of a vascular plaque which may lead to a stroke, said vascular plaque being present on an arterial wall chosen from a carotid wall and a supra-aortic trunk wall, said system comprising: - a monitoring device capable of being placed near the vascular plaque, said device comprising at least one temperature sensor configured to measure the temperature of the vascular plaque, at least one vibration sensor configured to measure mechanical waves propagated in said arterial wall, at least one vascular plaque movement sensor configured to measure movements of the vascular plaque, a memory capable of storing signals transmitted by said sensors, a communication interface, an energy source configured to power said sensors and the communication interface, - a computing unit adapted to communicate with the monitoring device and configured to analyze measurements of the vibration sensor, the temperature sensor and the vascular plaque movement sensor from the monitoring device by an artificial intelligence trained to detect whether there is a risk of at least partial rupture or detachment of the vascular plaque.

[0013] According to one embodiment, the monitoring device further comprises at least one electrical sensor configured to measure a parameter linked to the electrical impedance of the vascular plaque.

[0014] According to another embodiment, the monitoring device further comprises at least one acoustic wave sensor configured to measure acoustic waves originating from the arterial wall and / or the vascular plaque.

[0015] Thanks to the monitoring device attached directly to the patient, it is possible to monitor in real time the evolution of the integrity of the vascular plaques.

[0016] The system makes it possible to predict the risk of a stroke occurring without requiring complex and often invasive examinations of the patient.

[0017] The features set out in the following paragraphs may, optionally, be implemented, independently of one another or in combination with one another:

[0018] The monitoring device is in the form of a patch capable of being stuck to an external surface of a patient's skin.

[0019] The monitoring device is in the form of a subcutaneous implant capable of being inserted under the skin of a patient.

[0020] The temperature sensor is a sensor of thermal waves emitted by the vascular plate.

[0021] The vibration sensor comprises an accelerometer.

[0022] The vibration sensor includes a 3-axis accelerometer and a 3-axis gyroscope.

[0023] The vascular plaque motion sensor is an ultrasonic probe.

[0024] The energy source is an induction rechargeable battery.

[0025] The communication interface is selected from a short-range radio interface or a near-field communication interface.

[0026] According to another embodiment, the system further comprises at least one mobile communication device adapted to communicate remotely with the monitoring device via said communication interface which is a communication interface. short range and to transmit signals to the computing unit via a long range communication interface belonging to the computing unit.

[0027] According to one embodiment, the monitoring device comprises at least one temperature sensor and / or at least one motion sensor and / or at least one acoustic wave sensor, the measurement frequency of said sensors being adjusted to emit and / or receive a signal coming from an internal jugular vein.

[0028] According to another aspect, there is provided a method for predicting at least partial rupture or detachment of vascular plaque which may lead to a stroke, said vascular plaque being present on an arterial wall selected from a carotid wall and a supra-aortic trunk wall of a patient using a system as described above, the method comprising: - continuously acquire for a given period a plurality of signals representative of the evolution of the risk of rupture or detachment of the vascular plaque by means of the monitoring device placed near the vascular plaque, - analyze the plurality of signals in a computing unit by an artificial intelligence trained to detect the risk of at least partial rupture or detachment of the vascular plaque.

[0029] According to one embodiment, the artificial intelligence is a neural network and the method further comprises a prior learning step comprising: - acquire a plurality of signals, called reference signals, from monitoring devices worn by a population whose risks of rupture or detachment of the vascular plaque are known, - train the neural network with said reference signals until it converges. Brief description of the drawings

[0030] Other characteristics, details and advantages will appear on reading the detailed description below, and on analyzing the attached drawings, in which: Fig.l

[0031] [Fig.l] [Fig.l] is an illustration of a system for predicting a vascular plaque rupture or detachment that may lead to a stroke according to one embodiment. Fig. 2

[0032] [Fig.2] [Fig.2] is an illustration of a prediction system according to another mode of realization. Fig. 3

[0033] [Fig.3] [Fig.3] shows the monitoring device of the prediction system po located at the level of a patient's neck, near the internal carotid artery after the bi- furcation of the common carotid, opposite the vascular plaque. Fig. 4

[0034] [Fig.4] [Fig.4] shows an example of the implementation of the monitoring device fixed on an external surface of the skin, near the carotid artery. Fig. 5

[0035] [Fig.5] [Fig.5] shows another example of the embodiment of the device of surveillance implanted under the skin, near the carotid artery. Fig. 6

[0036] [Fig.6] [Fig.6] is a flowchart representing the prediction process implemented implemented by the prediction system according to one embodiment. Fig. 7

[0037] [Fig.7] [Fig.7] is a flowchart representing the learning step for train the neural network used by the computing unit. Fig. 8

[0038] [Fig.8] [Fig.8] is a schematic illustration of the monitoring device further comprising an electrical sensor configured to measure a parameter related to the electrical impedance of the vascular plaque according to one embodiment. Fig. 9

[0039] [Fig.9] [Fig.9] is a schematic illustration of the monitoring device further comprising an acoustic wave sensor configured to measure acoustic waves originating from the arterial wall and / or the vascular plaque. Description of the embodiments

[0040] [Fig.l] illustrates an embodiment of a system 1 for predicting a risk of rupture or detachment of a vascular plaque which could lead to a stroke by plaque rupture. The system 1 comprises: - a monitoring device 2 (SURV) configured to be placed near the carotid or supra-aortic trunks of a patient in order to measure the signals representative of the mechanical stability of one or more vascular plaques (atheromas or other origins) formed on the internal wall of the carotid, - and a computing unit 10 (CALC) adapted to communicate with the monitoring device 2 and configured to analyze the signals coming from the monitoring device 2 to detect whether there is a risk of rupture of at least a portion of the vascular plaque. Typically, the computing unit 10 can be operated by a practitioner or a group of practitioners, and can collect and process data from a large number of monitoring devices 2.

[0041] The monitoring device 2 may comprise: - one or two vibration sensors 4 (SENSE 1), in particular one or more acce- lerometer(s), configured to measure mechanical waves that may be generated by the arterial pulse wave passing through the vascular plaque and underlying walls, - a temperature sensor 5 (SENS 2) configured to measure the temperature of the vascular plate, - and a plaque movement sensor 6 (SENS 3), including an ultrasonic transducer such as a piezoelectric crystal, configured to measure the movements of the vascular plaque.

[0042] The monitoring device 2 also comprises a memory 8 (MEM) capable of storing the signals transmitted by the sensors 4, 5, 6 and a communication interface 9 (INT 1). The communication interface 9 can be for example: - a short-range radio interface, particularly of the “Bluetooth”, “Wifi” or other type, - or a near-field communication interface, particularly of the NFC or RFID type.

[0043] The signals stored in the memory 8 can be transmitted to the computing unit 10 via the communication interface 9.

[0044] The computing unit 10 is configured to analyze the signals, in particular by a trained artificial intelligence, and to detect the occurrence of a stroke by rupture of a vascular plaque. The computing unit 10 comprises, for example, a central processing unit 12 (CPU) in which the trained neural network is stored. The computing unit 10 also comprises a communication interface 11 (INT 2) for receiving the signals from the monitoring device 2. The communication interface 11 and the central processing unit 12 may optionally be remote from each other. The central processing unit 12 may optionally be a server.

[0045] The function of sensors 4, 5 and 6 is detailed below.

[0046] The rupture or detachment of the vascular plaque attached to the internal wall of the artery can be caused by a mechanical wave generated for example by the arterial pulse wave. The mechanical wave comprises a shear component and a compression component, and can be likened to a seismic wave. By measuring the mechanical waves, the vibration sensor 4 is able to measure a signal representative of the biomechanical properties of the vascular plaque and the underlying walls. The vibration sensor 4 can comprise at least one accelerometer. For example, the vibration sensor can comprise a 3-axis accelerometer and a 3-axis gyroscope. The data representative of the signal measured by the vibration sensor 4 are stored in the memory 8.

[0047] The temperature sensor 5 is capable of measuring a local temperature variation representative of the stability of the plate. Indeed, inflammation is one of the main characteristics of an unstable plaque. The intensity of the inflammatory process results in a rise in temperature in the plaque. According to an exemplary embodiment, the temperature sensor 5 may be configured to measure a thermal wave emitted by the plaque in the microwave frequency range. Such a sensor may comprise, for example, an antenna for detecting said thermal waves emitted by the vascular plaque. The data representative of the signal measured by the temperature sensor 5 are stored in the memory 8.

[0048] The plate movement sensor 6 is a piezoelectric sensor configured to send ultrasound to detect in real time the movements of the plate and define whether they are regular or irregular and anarchic which translate an arrhythmia or a phenomenon of disturbance of the arterial pulse wave. The data representative of the signal measured by the movement sensor 6 are stored in the memory 8.

[0049] In relation to [Fig.2], a prediction system according to another mode of rea lization is described. It further comprises a mobile communication device 13 communicating with the monitoring device 2 via a short-range communication interface. The mobile communication device 13 comprises an application via which the patient, wearing the monitoring device, can retrieve the signals stored in the memory 8. The monitoring device transmits the signals to the mobile communication device 13 via its communication interface 9 which is a short-range interface.

[0050] The mobile communication device 13 can also be put into communication with the computing unit 10. The mobile communication device can for example transmit the recovered signals via a communication interface of the 2G, 3G, 4G or 5G type. The computing unit 10 can recover the signals via its communication interface 11 which is a long-range interface.

[0051] The mobile communication device 13 may be, for example, a smart phone or a smart watch.

[0052] The monitoring device 2 is a stand-alone device. The monitoring device 2 comprises an energy source 7 (BATT) which can for example be rechargeable by an external device, in particular by induction. The battery and the electronic components of the data acquisition device can be chosen so that the autonomy of the device is at least equal to one month.

[0053] [Fig.8] illustrates a schematic view of a monitoring device 20 according to another embodiment in which it comprises, in addition to the three other sensors 4, 5, 6 described above, a fourth sensor which is an electrical sensor 24 (SENS 4).

[0054] The electrical sensor 24 is configured to measure the electrical impedance of the vascular plaque which reflects a level of plaque stiffness which has been identified as influencing vascular plaque stability. The vascular plaque and the medium surrounding behave like a dielectric medium. By measuring the impedance, it is possible to extract the dielectric parameters, namely the conductivity and permittivity of this medium, representative of the level of rigidity of the plaque. According to an exemplary embodiment, the electrical impedance sensor is configured to apply a current and measure a potential difference. Knowing the voltage and current, the value of the impedance of the vascular plaque is determined from Ohm's law. Continuous measurement of the impedance makes it possible to monitor the evolution of the state of rigidity of the plaques. The data representative of the signal measured by the impedance sensor 24 are stored in the memory 8.

[0055] [Fig.9] illustrates a schematic view of a monitoring device 30 according to another embodiment in which it comprises, in addition to the four sensors 4, 5, 6, 24 of [Fig.8], a fifth passive acoustic wave sensor 35 configured to measure the acoustic waves coming from the arterial wall and the plaque.

[0056] It should be noted that the prediction system can also predict a risk of rupture or detachment of a vascular plaque from one or more signals measured by the five sensors 4, 5, 6, 24, 35 of the monitoring device. The monitoring device can therefore comprise any one or more, or any combination of the five sensors 4, 5, 6, 24, 35 described above to measure a signal representative of the mechanical stability of the vascular plaque.

[0057] According to an exemplary embodiment, the monitoring device comprises only one of the four sensors among the five sensors 4, 5, 6, 24, 35 described above.

[0058] According to another exemplary embodiment, the monitoring device may comprise a combination of two sensors from among the five sensors 4, 5, 6, 24, 35 described above.

[0059] According to yet another exemplary embodiment, the monitoring device may comprise a combination of three sensors from among the five sensors 4, 5, 6, 24, 35 described above.

[0060] According to another exemplary embodiment, the monitoring device may comprise a combination of four sensors from among the five sensors 4, 5, 6, 24, 35 described above.

[0061] Advantageously, the measurement frequency of the sensors of the monitoring device can be adjusted to transmit and / or receive signals from a region of interest located at a certain depth under the skin.

[0062] The combination of sensors varies depending on clinical needs.

[0063] In the context of the use of the monitoring device for monitoring the evolution over time of the biomechanical properties of a vascular plaque and / or of an underlying wall of the carotid, the monitoring device may comprise for example a vibration sensor 4 and / or a temperature sensor 5 and / or a movements 6 and / or an electrical sensor 24 and / or an acoustic wave sensor 35 configured to transmit and / or receive signals from a vascular plaque and / or an underlying wall of the carotid artery which are located at a variable depth from the surface of the skin, generally between 1-3 cm under the skin. The measurement frequency of the vibration sensor 4, the temperature sensor 5, the movement sensor 6, the electrical sensor 24 and the acoustic wave sensor 35 are adjusted to transmit and / or receive the signal from the vascular plaque and / or the underlying wall of the carotid artery.

[0064] In the context of using the monitoring device to measure changes in the biomechanical properties of the internal jugular vein, the monitoring device may comprise, for example, a temperature sensor 5 and / or a motion sensor 6 and / or an acoustic wave sensor 35 configured to transmit and / or receive signals from an internal jugular vein which is located at a variable depth from the surface of the skin depending on the anatomy of the patient, generally between 1 and 3 cm under the skin. The measurement frequency of the temperature sensor 5, the motion sensor 6 and the acoustic wave sensor 35 are different and set to transmit and / or receive the signal from the jugular vein.

[0065] [Fig.3] illustrates a schematic view of a common carotid 100 which divides into two at the level of a patient's neck: internal carotid artery 101 which will supply the brain and the external carotid artery 102 which will supply the neck and face. The monitoring device 2 as described above can be placed opposite the internal carotid just after the bifurcation of the common carotid so as to continuously measure the different signals emitted by one or more plaques present on the internal wall of the internal carotid.

[0066] Also shown schematically in [Fig. 3] is an internal jugular vein 107 which is a deep vein of the neck, in contact with the internal carotid artery. There is an internal jugular vein on each side of the neck. The internal jugular vein runs vertically downwards, lateral to the internal carotid artery, then to the common carotid artery.

[0067] Generally, the positioning of the device can be adjusted according to clinical needs. According to another embodiment, it is also possible to position a second monitoring device or even several devices to monitor not only the internal carotid but also its branches or any other artery of the supra-aortic trunks. For example, it is possible to position a second monitoring device just before the bifurcation of the common carotid 100.

[0068] With reference to [Fig.4], the monitoring device 2 is in the form of a patch 3 having an adhesive surface which allows the device to be stuck to an area of ​​the outer surface of the skin 106 of the neck, near the carotid artery 100.

[0069] With reference to [Fig. 5], the monitoring device 2 may also be a subcutaneous implantable device. The monitoring device 2 comprises, for example, a casing made of a biocompatible material. The monitoring device is placed in an implantable probe. It may be placed under the skin 106, near the carotid artery 100. It is placed by the practitioner who performs a detachment under the skin. The device is placed in contact with the muscle 104 under the skin.

[0070] One aspect of the present disclosure is to enable the system 1 to be used to continuously monitor changes in the biomechanical properties of an internal plaque and / or an underlying wall of the carotid artery. In this use, the monitoring device may comprise a vibration sensor 4 and / or a temperature sensor.

[0071] The monitoring method may be performed in a non-invasive manner by placing the monitoring device on a patient's skin, near the carotid artery.

[0072] The monitoring method may also be implemented by placing the monitoring device under the skin of a patient, near the carotid artery.

[0073] With reference to [Fig.6], a method implementing a system for predicting a risk of plaque rupture or detachment which could lead to a stroke is described below.

[0074] In step E1, the vibration sensor 4 and / or the temperature sensor 5 and / or the motion sensor 6 and / or the electrical sensor 24 and / or the acoustic wave sensor 35 are adjusted so as to operate in a frequency range suitable for transmitting and / or receiving signals from a vascular plaque and / or an underlying wall of the carotid.

[0075] In step E1, the vibration sensor 4 and / or the temperature sensor 5 and / or the motion sensor 6 and / or the electrical sensor 24 and / or the acoustic wave sensor 35 of the monitoring device measure the signals coming from the plate for a determined period. The duration varies between a few hours and a few weeks. The sensors are for example previously programmed to operate for a determined period. This duration is determined by the practitioner according to clinical needs.

[0076] According to an exemplary embodiment, the three sensors 4, 5 and 6 of the monitoring device measure the signals coming from the plaque and / or the wall of the carotid artery for a determined duration.

[0077] According to another exemplary embodiment, the four sensors 4, 5, 6, 24 of the monitoring device measure the signals coming from the plaque and / or the wall of the carotid artery for a determined duration.

[0078] According to yet another embodiment, the five sensors 4, 5, 6, 24, 35 of the monitoring device measure the signals coming from the plaque and / or the wall of the carotid artery for a determined duration.

[0079] In step E2, the signals are stored in memory 8.

[0080] In step E3, the signals stored in the memory 8 of the monitoring device are sent to the computing unit 10 via the communication interface 9.

[0081] In step E4, the calculation unit 10 analyzes the signals by means of an artificial intelligence trained to detect a risk of plaque rupture or detachment that could lead to a stroke. The artificial intelligence comprises a neural network trained to determine from the collected signals whether the plaque(s) present in the carotid artery present a risk of rupture.

[0082] Steps E3 and E4 are for example carried out at the practitioner's home. It is thus possible for the practitioner to obtain a reliable prediction on the risk of the occurrence of a stroke by plaque rupture in the patient even if the latter does not present any symptoms of the pathology.

[0083] Alternatively, in step E3, the patient may also use a mobile communication device 13 to communicate with the monitoring device periodically via a short-range communication interface. The device is, for example, a smartphone. It comprises, for example, an application by which the patient can interrogate the monitoring device to receive the signals stored in the memory 8 of the monitoring device. The communication device 13 then transmits the signals via a 4G or 5G network to the computing unit 10. Thus, a diagnosis of the evolution of the stability state of the plaques can be established remotely by the practitioner periodically, for example once a week.

[0084] According to one embodiment and with reference to [Fig.7], when the artificial intelligence is a neural network, the method may further comprise a preliminary learning step for training the artificial intelligence so as to determine from the collected signals whether the plaque(s) present in the carotid present a risk of rupture or detachment.

[0085] More specifically, the learning step may comprise the following sub-steps.

[0086] In a sub-step E01, a plurality of monitoring devices are used to collect signals from a population whose risk of vascular plaque rupture is known. The signals, called reference signals, are stored on a server.

[0087] In a sub-step E02, the neural network is trained with the reference signals until it converges. The trained neural network is then stored in the computing unit 10, in particular in the central unit 12.

[0088] Thanks to the continuous acquisition of a set of signals representative of the evolution of the stability of the vascular plaque and the use of artificial intelligence to analyze the signals obtained, the system of the present disclosure makes it possible to predict the risk of plaque rupture that could lead to the occurrence of a stroke. The present system can be used by the practitioner, in addition to morphological and histological studies, to monitor and locally evaluate the evolution of the risk of plaque rupture and thus prevent serious clinical consequences such as stroke. The system of the present disclosure thus makes it possible to quickly identify individuals who need interventions, even in the absence of pathological symptoms.

[0089] Another aspect of the present disclosure is to enable the system to be used to continuously monitor changes in the biomechanical properties of an internal jugular vein which is located deeper under the skin than the carotid artery. In this use, the monitoring device may comprise a temperature sensor 5, a motion sensor 6 and an acoustic wave sensor 35.

[0090] The monitoring method may be performed in a non-invasive manner by placing the monitoring device on a patient's skin, near the internal jugular vein.

[0091] The monitoring method may also be implemented by placing the monitoring device under the skin of a patient, near the internal jugular vein.

[0092] With reference to [Fig. 6], in step E1, the temperature sensor 5, the motion sensor 6 and the acoustic wave sensor 35 of the monitoring device 2 are set to operate in a suitable frequency range that allows signals from the internal jugular vein to be transmitted and / or received for a determined period of time.

[0093] The duration of the measurement varies from a few hours to a few weeks. The sensors are, for example, pre-programmed to operate for a specific period. This duration is determined by the practitioner according to clinical needs. The signals collected are representative of changes in the biomechanical properties of the internal jugular vein over time.

[0094] In step E2, the signals are stored in memory 8.

[0095] In step E3, the signals stored in the memory 8 of the monitoring device are sent to the computing unit 10 via the communication interface 9.

[0096] In step E4, the computing unit 10 analyzes the signals by means of an artificial intelligence trained to detect a risk of vascular thrombosis. The artificial intelligence comprises a neural network trained to determine from the collected signals whether the modifications of the biomechanical properties of the internal jugular vein present a risk of vascular thrombosis.

[0097] Steps E3 and E4 are for example carried out at the practitioner's premises. It is thus possible for the practitioner to obtain a reliable prediction on the risk of the occurrence of vascular thrombosis in the patient even if the latter does not present any symptoms of the pathology.

[0098] Alternatively, in step E3, the patient may also use a mobile communication device 13 to communicate with the monitoring device periodically via a short-range communication interface. The device is, for example, a smartphone. It comprises, for example, an application by which the patient can interrogate the monitoring device to receive the signals stored in the memory 8 of the monitoring device. The communication device 13 then transmits the signals via a 4G or 5G network to the computing unit 10. Thus, it is possible to monitor the evolution of the biomechanical properties of the internal jugular vein remotely by the practitioner periodically, for example once a week.

[0099] According to one embodiment and with reference to [Fig.7], when the artificial intelligence is a neural network, the method may further comprise a preliminary learning step for training the artificial intelligence so as to determine from the signals collected over time to characterize a risk of occurrence of vascular thrombosis.

[0100] More specifically, the learning step may comprise the following sub-steps.

[0101] In a sub-step E01, a plurality of monitoring devices are used to collect signals from a population whose risk of vascular thrombosis is known. The signals, called reference signals, are stored on a server.

[0102] In a sub-step E02, the neural network is trained with the reference signals until it converges. The trained neural network is then stored in the computing unit 10, in particular in the central unit 12.

[0103] Thanks to the continuous acquisition of a set of signals representative of the evolution of the modifications of the biomechanical properties of the internal jugular vein and the use of artificial intelligence to analyze the signals obtained, the system of the present disclosure also makes it possible to predict the risk of formation of a blood clot in the internal jugular vein of the neck, which could lead to the occurrence of vascular thrombosis. The method described below makes it possible to help the practitioner, in addition to known examinations, for example an ultrasound Doppler which makes it possible to visualize the clot, to evaluate the risk of jugular thrombosis, in order to be able to take care of the patient quickly.

[0104] Furthermore, the use of the system is not limited solely to predicting stroke. It can be implemented in a patient after the occurrence of a stroke to predict the risk of recurrence after treatment of vascular plaques.

[0105] Although the system of the present invention has particular advantages for The system can be applied in general to predict the occurrence of a stroke, to detect patients at risk of developing cardiovascular diseases early. The system can also be applied in a clinical trial to evaluate the effectiveness of a new treatment for vascular plaques.

Claims

Claims

1. System for predicting (1) an at least partial rupture or detachment of a vascular plaque that may lead to a stroke, said vascular plaque being present on an arterial wall chosen from a carotid wall and a supra-aortic trunk wall, said system comprising: - a monitoring device (2) capable of being placed near the vascular plaque, said device comprising at least one temperature sensor (5) configured to measure the temperature of the vascular plaque, at least one vibration sensor (4) configured to measure mechanical waves propagated in said arterial wall, at least one vascular plaque movement sensor (6) configured to measure movements of the vascular plaque, a memory (8) capable of storing signals transmitted by said sensors, a communication interface (9),an energy source (7) configured to power said sensors and the communication interface (9), - a computing unit (10) adapted to communicate with the monitoring device (2) and configured to analyze measurements of the vibration sensor (4), the temperature sensor (5) and the vascular plaque movement sensor (6) coming from the monitoring device (2) by an artificial intelligence trained to detect if there is a risk of at least partial rupture or detachment of the vascular plaque.,

2. The system of claim 1, wherein the monitoring device (20) further comprises at least one electrical sensor (24) configured to measure a parameter related to the electrical impedance of the vascular plaque.

3. The system of claim 1 or 2, wherein the monitoring device (30) further comprises at least one acoustic wave sensor (35) for measuring acoustic waves originating from the arterial wall and / or vascular plaque.

4. System according to one of claims 1 to 3, in which the monitoring device (2, 20, 30) is in the form of a patch capable of being stuck to an external surface of the skin (106) of a patient.

5. System according to one of claims 1 to 3, in which the monitoring device (2, 20, 30) is in the form of a subcutaneous implant capable of being inserted under the skin of a patient.

6. System according to one of claims 1 to 5, in which the temperature sensor (5) is a sensor of thermal waves emitted by the vascular plaque.

7. System according to one of claims 1 to 6, in which the vibration sensor (4) comprises an accelerometer.

8. The system of claim 7, wherein the vibration sensor (4) comprises a 3-axis accelerometer and a 3-axis gyroscope.

9. System according to one of claims 1 to 8, in which the motion sensor of the vascular plaque (6) is an ultrasonic probe.

10. System according to one of claims 1 to 9, in which the energy source (7) is an induction rechargeable battery.

11. System according to one of claims 1 to 10, in which the communication interface (9) is chosen from a short-range radio interface or a near-field communication interface.

12. System according to one of claims 1 to 11, further comprising at least one mobile communication device (13) adapted to communicate remotely with the monitoring device (2) via said communication interface (9) which is a short-range interface and to transmit signals to the computing unit (10) via a long-range communication interface (11) belonging to the computing unit (10).

13. System according to claim 3, wherein the monitoring device comprises at least one temperature sensor (5) and / or at least one motion sensor (6) and / or at least one acoustic wave sensor (35), the measurement frequency of said sensors being adjusted to transmit and / or receive a signal from an internal jugular vein.

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