Vascular lesion risk identification system based on machine learning model

Abstract

The invention discloses a vasculopathy risk identification system based on a machine learning model. A control host acquires a blood oxygen value, a pH value, a lactic acid value and a pressure value in blood vessel blood of an evaluator and a blood vessel inner wall structure image; the blood vessel is a vein blood vessel, analyzing the tube diameter and the thickness of the intima, the medium and the adventitia based on the structure image, inputting the blood oxygen value, the pH value, the lactic acid value, the pressure value, the tube diameter and the thickness of the intima, the medium and the adventitia of the assessor into a target vein blood vessel machine learning model, and training and outputting a vein blood vessel lesion risk identification result; the blood vessel is an artery blood vessel, and the composition and form of the artery blood vessel are identified and the stenosis degree value of the vessel diameter is calculated when the vessel diameter, the thickness of the inner, middle and outer membranes and artery plaques exist in the artery blood vessel are analyzed based on the structure image; and inputting the blood oxygen value, the pH value, the lactic acid value, the pressure value, the tube diameter, the thickness of the intima, the middle and the adventitia of the blood vessel, the composition and the form of the artery plaque and the tube diameter stenosis degree value of the assessor into a target artery blood vessel machine learning model to train and output an artery blood vessel lesion risk identification result.

Description

Technical Field

[0001] The present invention relates to the field of machine learning and disease risk identification technology, and in particular to a vascular lesion risk identification system based on a machine learning model. This invention technology comes from the project: National Key R&D Program: "Research on Pan-vascular Disease Screening, System Evaluation and Prevention and Treatment System", No.: 2021YFC2500500. Background Art

[0002] Faced with the increasing burden of vascular diseases, how to use existing diagnosis and treatment resources to effectively prevent and control the risk of vascular diseases has become an important issue that needs to be urgently addressed in the current medical field. Existing intravascular ultrasound catheters can measure vascular structure images and obtain some vascular data based on vascular structure images. Doctors can determine whether patients have vascular lesion risks and which type of vascular lesion risks are involved based on vascular data and their own experience. This determination method has low accuracy and low real-time performance. Based on the existing defects, the present invention designs a vascular lesion risk identification system based on a machine learning model. Summary of the invention

[0003] In view of the problems and shortcomings of the prior art, the present invention provides a vascular lesion risk identification system based on a machine learning model.

[0004] The present invention solves the above technical problems through the following technical solutions:

[0005] The present invention provides a vascular lesion risk identification system based on a machine learning model, which includes an intravascular ultrasonic catheter and a control host, and is characterized in that a blood oxygen sensor, a pH sensor, a lactate sensor and a pressure sensor are installed on the outer surface of the intravascular ultrasonic catheter, and an ultrasonic sensor is installed on the outer surface of the front end of the intravascular ultrasonic catheter;

[0006] The control host is used to obtain the blood oxygen value, pH value, lactate value and pressure value in the blood vessels of the assessor through the blood oxygen sensor, pH sensor, lactate sensor and pressure sensor respectively, and obtain a high-resolution structural image of the inner wall of the assessor's blood vessels through the ultrasonic sensor and intravascular ultrasound technology;

[0007] The control host is also used to analyze whether the blood vessel is a vein or an artery based on the blood oxygen value and the lactate value, and when the blood vessel is analyzed to be a vein, analyze the blood vessel diameter, the thickness of the blood vessel intima, the thickness of the blood vessel middle membrane and the thickness of the blood vessel adventitia after performing image processing on the structural image; when the blood vessel is analyzed to be an artery, analyze the blood vessel diameter, the thickness of the blood vessel intima, the thickness of the blood vessel middle membrane, the thickness of the blood vessel adventitia and whether there is an arterial plaque in the artery after performing image processing on the structural image, and when the arterial plaque is analyzed to be present in the arterial vessel, identify the composition and morphology of the arterial plaque and calculate the stenosis degree value of the vessel diameter;

[0008] The control host is also used for inputting the evaluator's blood oxygen value, pH value, lactate value, pressure value, blood vessel diameter, intima thickness, media thickness and adventitia thickness into the target venous blood vessel machine learning model for training and learning, and outputting a venous vascular disease risk identification result, the venous vascular disease risk identification result being no venous vascular disease risk, deep vein valve insufficiency risk and deep vein thrombosis risk;

[0009] The control host is also used to input the evaluator's blood oxygen value, pH value, lactate value, pressure value, blood vessel diameter, intima thickness, media thickness, adventitia thickness, composition and morphology of arterial plaques when arterial plaques are present, and degree of stenosis of the vessel diameter into the target arterial machine learning model for training and learning, and output an arterial vascular disease risk identification result, and the arterial vascular disease risk identification result is no arterial vascular disease risk, atherosclerosis risk, aneurysm risk, thromboangiitis obliterans risk and arterial dissection risk.

[0010] The positive and progressive effects of the present invention are:

[0011] The vascular lesion risk identification system based on the machine learning model designed by the present invention can accurately analyze the patient's vascular lesion risk identification results. The vascular lesion risk identification results include venous vascular lesion risk identification results and arterial vascular lesion risk identification results. The present invention has high accuracy and high real-time performance, and improves detection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 This is a structural block diagram of a vascular lesion risk identification system based on a machine learning model according to a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0013] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0014] like Figure 1 As shown, this embodiment provides a vascular lesion risk identification system based on a machine learning model, which includes an intravascular ultrasonic catheter 100 and a control host 200. A blood oxygen sensor 10, a pH sensor 20, a lactate sensor 30 and a pressure sensor 40 are installed on the outer surface of the intravascular ultrasonic catheter 100, and an ultrasonic sensor 50 is installed on the front end outer surface of the intravascular ultrasonic catheter 100.

[0015] The control host 200 is used to obtain the blood oxygen value, pH value, lactate value and pressure value in the blood vessels of the evaluator through the blood oxygen sensor 10, pH sensor 20, lactate sensor 30 and pressure sensor 40 respectively, and obtain a high-resolution structural image of the inner wall of the evaluator's blood vessels through the ultrasonic sensor 50 and intravascular ultrasound technology.

[0016] The control host 200 is also used to analyze whether the blood vessel is a vein or an artery based on the blood oxygen value and the lactate value. When the blood vessel is analyzed to be a vein, the structure image is processed and then the blood vessel diameter (tube diameter), the thickness of the vascular endothelium, the thickness of the vascular middle membrane and the thickness of the vascular adventitia are analyzed; when the blood vessel is analyzed to be an artery, the structure image is processed and then the blood vessel diameter, the thickness of the vascular endothelium, the thickness of the vascular middle membrane, the thickness of the vascular adventitia and whether there is an arterial plaque in the artery are analyzed; when the arterial plaque is analyzed to be present in the arterial vessel, the composition and morphology of the arterial plaque are identified and the degree of stenosis of the tube diameter = (blood vessel diameter - cross-sectional area of ​​arterial plaque) / blood vessel diameter are calculated.

[0017] In this embodiment, based on the characteristics of blood oxygen and lactate values ​​in venous blood vessels and arterial blood vessels, in venous blood vessels, the blood oxygen value is low and the lactate value is high, and in arterial blood vessels, the blood oxygen value is high and the lactate value is low, and thus it can be analyzed whether the blood vessel is a venous blood vessel or an arterial blood vessel. Different data are collected for venous blood vessels and arterial blood vessels.

[0018] Among them, identifying the composition of arterial plaques specifically includes constructing an arterial plaque composition identification model and identifying the composition of arterial plaques based on the constructed arterial plaque composition identification model.

[0019] Construct an arterial plaque composition recognition model: collect high-resolution structural images of the inner wall of the arterial blood vessels of A historical patients by using an ultrasonic sensor 50 and intravascular ultrasound technology, perform image processing on the structural image of each historical patient and then perform plaque image segmentation to obtain a plaque segmentation image of each historical patient, perform feature extraction on each plaque segmentation image to obtain a plurality of corresponding plaque composition features, and mark the specific composition markers of the arterial plaque of each historical patient, which include lipid core composition markers, necrotic substance composition markers, fibrous cap composition markers, inflammatory cell composition markers, calcium deposition composition markers, and smooth muscle cell composition markers. Construct a plaque composition feature set for each historical patient, the plaque composition feature set is composed of a plurality of plaque composition features and corresponding arterial plaque specific composition markers, construct a plaque composition training sample set and a plaque composition verification sample set, the plaque composition training sample set includes the plaque composition feature set of A1 historical patients, and the plaque composition verification sample set includes the plaque composition feature set of A2 historical patients, A=A1+A2, A, A1 and A2 are all positive integers and A1 is much larger than A2. A random forest regression model was selected to construct an arterial plaque composition recognition model. The plaque composition training sample set was input into the arterial plaque composition recognition model for model training. The plaque composition verification sample set was input into the trained arterial plaque composition recognition model for model verification to construct a target arterial plaque composition recognition model.

[0020] For example, the plaque composition feature sets of historical patients with a proportion of 80% constitute the plaque composition training sample set, and the plaque composition feature sets of historical patients with a proportion of 20% constitute the plaque composition verification sample set.

[0021] The composition of the arterial plaque is identified based on the constructed arterial plaque composition recognition model: when the existence of arterial plaque in the arterial blood vessel is analyzed, the image after the evaluator's image processing is subjected to plaque image segmentation to obtain the evaluator's plaque segmentation image, and the plaque segmentation image is subjected to feature extraction to obtain the evaluator's multiple plaque composition features, and the evaluator's multiple plaque composition features are input into the target arterial plaque composition recognition model to identify the composition of the arterial plaque.

[0022] Identifying the morphology of arterial plaques specifically includes constructing an arterial plaque morphology recognition model and identifying the morphology of arterial plaques based on the constructed arterial plaque morphology recognition model.

[0023] Constructing an arterial plaque morphology recognition model: extracting features from the plaque segmentation images of each historical patient to obtain corresponding multiple plaque morphology features, and marking the specific morphological markers of the arterial plaques of each historical patient, which include stable plaque markers, unstable plaque markers, vulnerable plaque markers, calcified plaque markers, fibrous plaque markers, and thrombotic plaque markers. Constructing a plaque morphology feature set for each historical patient, the plaque morphology feature set consists of multiple plaque morphology features and corresponding specific morphological markers of arterial plaques, constructing a plaque morphology training sample set and a plaque morphology verification sample set, the plaque morphology training sample set includes the plaque morphology feature set of A1 historical patients, and the plaque morphology verification sample set includes the plaque morphology feature set of A2 historical patients. Selecting a random forest regression model to construct an arterial plaque morphology recognition model, inputting the plaque morphology training sample set into the arterial plaque morphology recognition model for model training, and inputting the plaque morphology verification sample set into the trained arterial plaque morphology recognition model for model verification, and constructing a target arterial plaque morphology recognition model.

[0024] The morphology of arterial plaques is identified based on the constructed arterial plaque morphology recognition model: when the existence of arterial plaques in arterial blood vessels is analyzed, plaque image segmentation is performed on the image processed by the evaluator to obtain the evaluator's plaque segmentation image, feature extraction is performed on the plaque segmentation image to obtain the evaluator's multiple plaque morphology features, and the evaluator's multiple plaque morphology features are input into the target arterial plaque morphology recognition model to identify the morphology of arterial plaques.

[0025] The control host 200 is also used to input the evaluator's blood oxygen value, pH value, lactate value, pressure value, blood vessel diameter, vascular intima thickness, vascular media thickness and vascular adventitia thickness into the target venous blood vessel machine learning model for training and learning, and output a venous vascular disease risk identification result, which is no venous vascular disease risk, deep vein valve insufficiency risk and deep vein thrombosis risk.

[0026] Among them, constructing a target venous machine learning model includes: obtaining the blood oxygen value, pH value, lactate value and pressure value in the blood vessels of B historical vein testers through blood oxygen sensors, pH sensors, lactate sensors and pressure sensors respectively, obtaining high-resolution structural images of the inner walls of the blood vessels of B historical vein testers through ultrasonic sensors and intravascular ultrasound technology, analyzing the blood vessel diameter, vascular intima thickness, vascular middle membrane thickness and vascular adventitia thickness after image processing of the structural image of each historical vein tester, and marking the venous vascular lesion markers of each historical vein tester, and these venous vascular lesion markers include a venous vascular lesion-free marker, a deep vein valve insufficiency marker and a deep vein thrombosis marker.

[0027] A venous vascular feature set is constructed for each historical vein tester, and the venous vascular feature set is composed of blood oxygen value, pH value, lactate value, pressure value, vascular diameter, vascular intima thickness, vascular middle membrane thickness and vascular adventitia thickness as well as corresponding venous vascular lesion markers; a venous vascular sample set is constructed, and the venous vascular sample set includes the venous vascular feature sets of B historical vein testers, where B is a positive integer.

[0028] A venous vascular machine learning model was constructed based on a convolutional neural network. The particle swarm algorithm and genetic algorithm were used to optimize the hyperparameters of the venous vascular machine learning model. The venous vascular sample set was used to train the venous vascular machine learning model. The optimal hyperparameters were obtained through continuous iteration, and the target venous vascular machine learning model was constructed using the optimal hyperparameters.

[0029] Specifically: build a venous machine learning model based on a convolutional neural network, initialize the hyperparameters of the venous machine learning model, set the number of particles L in the particle population, the maximum number of iterations T, and the particle search space dimension D corresponding to the hyperparameters, each particle represents a set of hyperparameters, and initialize the speed and position of each particle.

[0030] Based on any particle as a hyperparameter, a corresponding venous machine learning model is constructed, and the venous machine learning model corresponding to the particle is trained using the venous sample set, and the corresponding fitness value is calculated using the fitness function. Based on this, the fitness value of each particle is calculated, and the maximum fitness value of each particle is used as the optimal fitness. The current position of each particle is used as the individual optimal position of each particle, and the current position of the particle corresponding to the optimal fitness is used as the group optimal position of the particle population. The fitness function MSE(X) stands for mean square error.

[0031] Update the velocity and position of each particle:

[0032]

[0033] X ld (t+1)=X ld (t)+V ld (t+1)

[0034]

[0035] Among them, V ld (t) and X ld (t) represent the current particle speed and position, V ld (t+1) and X ld(t+1) represents the updated particle speed and position, l represents the lth particle, d represents the dth dimension of the search space, t represents the current iteration number, W(t) represents the adaptive inertia weight, W min1 Indicates the minimum inertia weight, W max1 Indicates the maximum inertia weight, C1 and C2 indicate the acceleration factor, Xpb ld Indicates the optimal position of the historical individual, and its corresponding fitness value is the optimal fitness value of the historical individual, Xgb d It represents the optimal position of the historical group, and its corresponding fitness value is the optimal fitness value of the historical group.

[0036] Update the historical individual optimal position and the historical group optimal position: use the fitness function to calculate the fitness value of each particle after the update, compare the updated fitness value of each particle with the corresponding historical individual optimal fitness value, if the updated fitness value of any particle is greater than the corresponding historical individual optimal fitness value, then update the historical individual optimal fitness value of the particle = the updated fitness value of the particle, and update the historical individual optimal position of the particle = the updated position of the particle, otherwise do not update; then take the maximum value of the historical individual optimal fitness values ​​of the current particles as the current optimal fitness value, if the current optimal fitness value is greater than the historical group optimal fitness value, then update the historical group optimal fitness value = the current optimal fitness value, and update the historical group optimal position = the current position of the particle corresponding to the current optimal fitness value, otherwise do not update.

[0037] It is determined whether the optimal fitness value of the historical group reaches the first set fitness value or whether the number of iterations reaches the maximum number of iterations T. If so, the optimal hyperparameters are obtained based on the particles at the optimal position of the historical group, and the target venous machine learning model is constructed using the optimal hyperparameters. Otherwise, it is determined whether the current number of iterations t is less than the set threshold T1. If yes, the genetic algorithm is used to perform hybridization operations on the updated particles, and the speed and position of each particle in the particle population formed after hybridization are updated again. If no, the speed and position are directly updated again, T / 2≤T1≤T.

[0038] When is, a genetic algorithm is used to perform a hybridization operation on the updated particles, and the speed and position of each particle in the particle population formed after hybridization are updated again: the current historical individual optimal fitness values ​​of each particle are arranged in descending order, and the L1 particles with the lowest order are eliminated. The particle with the optimal position in the historical group among the non-eliminated particles is used as the parent generation, and L1 particles are randomly selected from the remaining non-eliminated particles as the parent generation. Each selected parent generation is hybridized with the parent generation to generate L1 particles. The current positions of the L1 particles generated by hybridization are used as the positions of the corresponding particles after t iterations. The current speed of each particle in the L1 particles generated by hybridization is the average speed of the particles on both sides of the particle, so as to obtain the particle population formed after hybridization. The particle population contains L particles. The speed and position of each particle in the particle population formed after hybridization are updated again. L1 is a positive integer and 1≤L1≤L / 2, t=t+1.

[0039] The control host 200 is also used to input the evaluator's blood oxygen value, pH value, lactate value, pressure value, blood vessel diameter, intima thickness, media thickness, adventitia thickness, composition and morphology of arterial plaque when arterial plaque exists, and degree of stenosis of the vessel diameter into the target arterial machine learning model for training and learning when the blood vessel is an arterial blood vessel, and output an arterial vascular disease risk identification result, and the arterial vascular disease risk identification result is no arterial vascular disease risk, atherosclerosis risk, aneurysm risk, thromboangiitis obliterans risk and arterial dissection risk.

[0040] Among them, constructing a machine learning model for the target artery includes: obtaining the blood oxygen value, pH value, lactate value and pressure value in the blood of C historical artery testers through a blood oxygen sensor, a pH sensor, a lactate sensor and a pressure sensor respectively, obtaining a high-resolution structural image of the inner wall of the blood vessel of the C historical artery testers through an ultrasonic sensor and intravascular ultrasound technology, performing image processing on the structural image of each historical artery tester, analyzing the blood vessel diameter, the thickness of the vascular intima, the thickness of the vascular middle membrane, the thickness of the vascular adventitia and whether there is an arterial plaque in the artery, and when it is analyzed that there is an arterial plaque in the artery, using an arterial plaque composition recognition model and an arterial plaque morphology recognition model to identify the composition and morphology of the arterial plaque and calculate the stenosis degree value of the vessel diameter, and marking the arterial vascular lesion markers of each historical arterial tester, and these arterial vascular lesion markers include a marker for no arterial vascular lesions, a marker for atherosclerosis, a marker for aneurysm, a marker for thromboangiitis obliterans and a marker for arterial dissection.

[0041] An arterial vascular feature set is constructed for each historical arterial test subject. The arterial vascular feature set consists of blood oxygen value, pH value, lactate value, pressure value, vascular diameter, vascular intima thickness, vascular media thickness, vascular adventitia thickness, composition and morphology of arterial plaques when arterial plaques exist, and stenosis degree of vessel diameter, as well as corresponding arterial vascular lesion markers. An arterial vascular sample set is constructed. The arterial vascular sample set includes the arterial vascular feature sets of C historical arterial test subjects, where C is a positive integer.

[0042] An arterial vascular machine learning model was constructed based on a convolutional neural network. The particle swarm algorithm and genetic algorithm were used to optimize the hyperparameters of the arterial vascular machine learning model. The arterial vascular sample set was used to train the arterial vascular machine learning model. The optimal hyperparameters were obtained through continuous iteration, and the target arterial vascular machine learning model was constructed using the optimal hyperparameters.

[0043] Specifically: construct an arterial vascular machine learning model based on a convolutional neural network, initialize the hyperparameters of the arterial vascular machine learning model, set the number of particles N in the particle population, the maximum number of iterations S, and the particle search space dimension K corresponding to the hyperparameters, each particle represents a set of hyperparameters, and initialize the speed and position of each particle.

[0044] Based on any particle as a hyperparameter, a corresponding arterial machine learning model is constructed, and the arterial sample set is used to train the arterial machine learning model corresponding to the particle, and the corresponding fitness value is calculated using the fitness function. Based on this, the fitness value of each particle is calculated, and the maximum fitness value of each particle is used as the optimal fitness, and the current position of each particle is used as the individual optimal position of each particle. The current position of the particle corresponding to the optimal fitness is used as the group optimal position of the particle population. The fitness function MSE(X) stands for mean square error.

[0045] Update the velocity and position of each particle:

[0046]

[0047] X nk (s+1)=X nk (s)+V nk (s+1)

[0048]

[0049] Among them, V nk (s) and X nk (s) represent the current particle speed and position, V nk (s+1) and X nk(s+1) represents the updated particle speed and position, n represents the nth particle, k represents the kth dimension of the search space, s represents the current iteration number, W(s) represents the adaptive inertia weight, W min2 Indicates the minimum inertia weight, W max2 Indicates the maximum inertia weight, C3 and C4 indicate the acceleration factor, Xpb nk Indicates the optimal position of the historical individual, and its corresponding fitness value is the optimal fitness value of the historical individual, Xgb k It represents the optimal position of the historical group, and its corresponding fitness value is the optimal fitness value of the historical group.

[0050] Update the historical individual optimal position and the historical group optimal position: use the fitness function to calculate the fitness value of each particle after the update, compare the updated fitness value of each particle with the corresponding historical individual optimal fitness value, if the updated fitness value of any particle is greater than the corresponding historical individual optimal fitness value, then update the historical individual optimal fitness value of the particle = the updated fitness value of the particle, and update the historical individual optimal position of the particle = the updated position of the particle, otherwise do not update; then take the maximum value of the historical individual optimal fitness values ​​of the current particles as the current optimal fitness value, if the current optimal fitness value is greater than the historical group optimal fitness value, then update the historical group optimal fitness value = the current optimal fitness value, and update the historical group optimal position = the current position of the particle corresponding to the current optimal fitness value, otherwise do not update.

[0051] It is determined whether the optimal fitness value of the historical group reaches the second set fitness value or whether the number of iterations reaches the maximum number of iterations S. If so, the optimal hyperparameters are obtained based on the particles at the optimal position of the historical group, and the target arterial vascular machine learning model is constructed using the optimal hyperparameters. Otherwise, it is determined whether the current number of iterations s is less than the set threshold S1. If yes, the genetic algorithm is used to perform hybridization operations on the updated particles, and the speed and position of each particle in the particle population formed after hybridization are updated again. If no, the speed and position are directly updated again, S / 2≤S1≤S.

[0052] When it is yes, a genetic algorithm is used to perform a hybridization operation on the updated particles, and the speed and position of each particle in the particle population formed after the hybridization are updated again. Specifically, the current historical individual optimal fitness values ​​of each particle are arranged in descending order, and the N1 particles with the lowest order are eliminated. The particles with the best position in the historical group among the non-eliminated particles are used as the parent generation, and N1 particles are randomly selected from the remaining non-eliminated particles as the parent generation. Each selected parent generation is hybridized with the parent generation to generate N1 particles. The current positions of the N1 particles generated by hybridization are used as the positions of the corresponding particles after s iterations. The current speed of each particle in the N1 particles generated by hybridization is the average speed of the particles on the front and back sides of the particle, so as to obtain the particle population formed after hybridization. The particle population contains N particles. The speed and position of each particle in the particle population formed after hybridization are updated again, N1 is a positive integer and 1≤N1≤N / 2, s=s+1.

[0053] In this embodiment, hybridization operations are used in the early and middle stages of iteration to prevent the particle swarm from falling into the local optimal solution, and in the late stages of iteration, the stability and convergence of the solution are ensured by reducing the hybridization operations. This embodiment combines the advantages of both the particle swarm algorithm and the genetic algorithm, and can construct optimized venous machine learning models and arterial machine learning models.

[0054] In this embodiment, the ultrasonic sensor 50 and intravascular ultrasound technology can monitor the structure of the inner wall of the blood vessel and obtain blood vessel parameters based on this. The blood oxygen sensor 10, pH sensor 20, lactate sensor 30 and pressure sensor 40 can simultaneously monitor the changes in blood oxygen and local chemical environment, providing more parameters for the diagnosis of vascular lesions. Then, the built-in software of the external control host can automatically identify the risk of vascular lesions in real-time data through artificial intelligence machine learning algorithms. Intelligent data analysis can greatly improve the efficiency and accuracy of diagnosis and reduce manual errors.

[0055] Although the specific embodiments of the present invention are described above, those skilled in the art should understand that these are only examples, and the protection scope of the present invention is defined by the appended claims. Those skilled in the art may make various changes or modifications to these embodiments without departing from the principles and essence of the present invention, but these changes and modifications all fall within the protection scope of the present invention.

Claims

1. A vascular lesion risk identification system based on a machine learning model, comprising an intravascular ultrasound catheter and a control host, characterized in that: The outer surface of the intravascular ultrasonic catheter is equipped with a blood oxygen sensor, a pH sensor, a lactate sensor and a pressure sensor, and the front end outer surface of the intravascular ultrasonic catheter is equipped with an ultrasonic sensor; The control host is used to obtain the blood oxygen value, pH value, lactate value and pressure value in the blood vessels of the assessee through the blood oxygen sensor, pH sensor, lactate sensor and pressure sensor respectively, and obtain a high-resolution structural image of the inner wall of the assessee's blood vessels through the ultrasonic sensor and intravascular ultrasound technology; The control host is also used to analyze whether the blood vessel is a vein or an artery based on the blood oxygen value and the lactate value, and when the blood vessel is analyzed to be a vein, analyze the blood vessel diameter, the thickness of the blood vessel intima, the thickness of the blood vessel middle membrane and the thickness of the blood vessel adventitia after performing image processing on the structural image; when the blood vessel is analyzed to be an artery, analyze the blood vessel diameter, the thickness of the blood vessel intima, the thickness of the blood vessel middle membrane, the thickness of the blood vessel adventitia and whether there is an arterial plaque in the artery after performing image processing on the structural image, and when the arterial plaque is analyzed to be present in the arterial vessel, identify the composition and morphology of the arterial plaque and calculate the stenosis degree value of the vessel diameter; The control host is also used for inputting the evaluator's blood oxygen value, pH value, lactate value, pressure value, blood vessel diameter, intima thickness, media thickness and adventitia thickness into the target venous blood vessel machine learning model for training and learning, and outputting a venous vascular disease risk identification result, the venous vascular disease risk identification result being no venous vascular disease risk, deep vein valve insufficiency risk and deep vein thrombosis risk; The control host is also used to input the evaluator's blood oxygen value, pH value, lactate value, pressure value, blood vessel diameter, intima thickness, media thickness, adventitia thickness, composition and morphology of arterial plaques when arterial plaques are present, and degree of stenosis of the vessel diameter into the target arterial machine learning model for training and learning, and output an arterial vascular disease risk identification result, and the arterial vascular disease risk identification result is no arterial vascular disease risk, atherosclerosis risk, aneurysm risk, thromboangiitis obliterans risk and arterial dissection risk.

2. The vascular lesion risk identification system based on machine learning model according to claim 1, characterized in that: The control host is also used to construct an arterial plaque composition recognition model: collect high-resolution structural images of the inner wall of the arterial blood vessels of A historical patients by using an ultrasonic sensor and intravascular ultrasound technology, perform image processing on the structural image of each historical patient and then perform plaque image segmentation to obtain a plaque segmentation image of each historical patient, perform feature extraction on each plaque segmentation image to obtain a corresponding plurality of plaque composition features, and mark specific composition markers of the arterial plaques of each historical patient, wherein the specific composition markers of the arterial plaques include a lipid core composition marker, a necrotic substance composition marker, a fibrous cap composition marker, an inflammatory cell composition marker, a calcium deposition composition marker, and a smooth muscle cell composition marker; Construct a plaque composition feature set for each historical patient. The plaque composition feature set consists of multiple plaque composition features and corresponding arterial plaque specific composition labels. Construct a plaque composition training sample set and a plaque composition verification sample set. The plaque composition training sample set includes the plaque composition feature sets of A1 historical patients, and the plaque composition verification sample set includes the plaque composition feature sets of A2 historical patients. A=A1+A2, A, A1 and A2 are all positive integers and A1 is much larger than A2. A random forest regression model was used to construct an arterial plaque composition recognition model, the plaque composition training sample set was input into the arterial plaque composition recognition model for model training, and the plaque composition verification sample set was input into the trained arterial plaque composition recognition model for model verification, so as to construct a target arterial plaque composition recognition model; The control host is also used to perform plaque image segmentation on the image processed by the evaluator to obtain the evaluator's plaque segmentation image when analyzing the existence of arterial plaque in the arterial blood vessel, perform feature extraction on the plaque segmentation image to obtain the evaluator's multiple plaque composition features, input the evaluator's multiple plaque composition features into the target arterial plaque composition recognition model, and identify the composition of the arterial plaque.

3. The vascular lesion risk identification system based on machine learning model as claimed in claim 2, characterized in that: The control host is also used to construct an arterial plaque morphology recognition model: feature extraction is performed on the plaque segmentation image of each historical patient to obtain a plurality of corresponding plaque morphology features, and specific morphological marks of the arterial plaque of each historical patient are marked, and the specific morphological marks of the arterial plaque include stable plaque marks, unstable plaque marks, vulnerable plaque marks, calcified plaque marks, fibrous plaque marks and thrombotic plaque marks; Constructing a plaque morphology feature set for each historical patient, the plaque morphology feature set is composed of multiple plaque morphology features and corresponding arterial plaque specific morphology marks, constructing a plaque morphology training sample set and a plaque morphology verification sample set, the plaque morphology training sample set includes the plaque morphology feature set of A1 historical patients, and the plaque morphology verification sample set includes the plaque morphology feature set of A2 historical patients; A random forest regression model was used to construct an arterial plaque morphology recognition model, the plaque morphology training sample set was input into the arterial plaque morphology recognition model for model training, the plaque morphology verification sample set was input into the trained arterial plaque morphology recognition model for model verification, and the target arterial plaque morphology recognition model was constructed; The control host is also used to perform plaque image segmentation on the image processed by the evaluator to obtain the evaluator's plaque segmentation image when analyzing the existence of arterial plaque in the arterial blood vessel, perform feature extraction on the plaque segmentation image to obtain the evaluator's multiple plaque morphological features, input the evaluator's multiple plaque morphological features into the target arterial plaque morphology recognition model, and identify the morphology of the arterial plaque.

4. The vascular lesion risk identification system based on machine learning model according to claim 1, characterized in that: The control host is also used to build a target venous blood vessel machine learning model: The blood oxygen value, pH value, lactate value and pressure value in the blood vessels of B historical vein testers are obtained through a blood oxygen sensor, a pH sensor, a lactate sensor and a pressure sensor respectively, and a high-resolution structural image of the inner wall of the blood vessels of the B historical vein testers is obtained through an ultrasonic sensor and intravascular ultrasound technology, and the structural image of each historical vein tester is processed and analyzed to obtain the blood vessel diameter, the thickness of the vascular intima, the thickness of the vascular middle membrane and the thickness of the vascular adventitia, and the venous vascular lesion markers of each historical vein tester are marked, and the venous vascular lesion markers include a venous vascular lesion-free marker, a deep vein valve insufficiency marker and a deep vein thrombosis marker; Construct a venous blood vessel feature set for each historical vein tester, the venous blood vessel feature set consists of blood oxygen value, pH value, lactate value, pressure value, blood vessel diameter, intima thickness, media thickness and adventitia thickness and corresponding venous vascular lesion markers, and construct a venous blood vessel sample set, the venous blood vessel sample set includes the venous blood vessel feature sets of B historical vein testers, B is a positive integer; A venous vascular machine learning model was constructed based on a convolutional neural network. The particle swarm algorithm and genetic algorithm were used to optimize the hyperparameters of the venous vascular machine learning model. The venous vascular sample set was used to train the venous vascular machine learning model. The optimal hyperparameters were obtained through continuous iteration, and the target venous vascular machine learning model was constructed using the optimal hyperparameters.

5. The vascular lesion risk identification system based on machine learning model according to claim 4, characterized in that: The control host is also used to construct a venous blood vessel machine learning model based on a convolutional neural network, use a particle swarm algorithm and a genetic algorithm to optimize the hyperparameters of the venous blood vessel machine learning model, and use a venous blood vessel sample set to train the venous blood vessel machine learning model, obtain the optimal hyperparameters through continuous iteration, and use the optimal hyperparameters to construct a target venous blood vessel machine learning model: A venous machine learning model is constructed based on a convolutional neural network. The hyperparameters of the venous machine learning model are initialized. The number of particles L, the maximum number of iterations T, and the particle search space dimension D corresponding to the hyperparameters are set. Each particle represents a set of hyperparameters. The speed and position of each particle are initialized. Based on any particle used as a hyperparameter, a corresponding venous machine learning model is constructed, and the venous machine learning model corresponding to the particle is trained using a venous sample set, and a corresponding fitness value is calculated using a fitness function. The fitness function is a function that is inversely proportional to the mean square error of the model deviation. Based on this, the fitness value of each particle is calculated, the maximum fitness value among each particle is used as the optimal fitness, the current position of each particle is used as the individual optimal position of each particle, and the current position of the particle corresponding to the optimal fitness is used as the group optimal position of the particle population; Update the velocity and position of each particle: X ld (t+1)=X ld (t)+V ld (t+1) Among them, V ld (t) and X ld (t) represent the current particle speed and position, V ld (t+1) and X ld (t+1) represents the updated particle speed and position, l represents the lth particle, d represents the dth dimension of the search space, t represents the current iteration number, W(t) represents the adaptive inertia weight, W min1 Indicates the minimum inertia weight, W max1 Indicates the maximum inertia weight, C1 and C2 indicate the acceleration factor, Xpb ld Indicates the optimal position of the historical individual, and its corresponding fitness value is the optimal fitness value of the historical individual, Xgb d Indicates the optimal position of the historical group, and its corresponding fitness value is the optimal fitness value of the historical group; Update the historical individual optimal position and the historical group optimal position: use the fitness function to calculate the fitness value of each particle after the update, compare the updated fitness value of each particle with the corresponding historical individual optimal fitness value, if the updated fitness of any particle is greater than the corresponding historical individual optimal fitness value, then update the historical individual optimal fitness value of the particle = the updated fitness of the particle, and update the historical individual optimal position of the particle = the updated position of the particle, otherwise do not update; then take the maximum value of the historical individual optimal fitness values ​​of the current particles as the current optimal fitness value, if the current optimal fitness value is greater than the historical group optimal fitness value, then update the historical group optimal fitness value = the current optimal fitness value, and update the historical group optimal position = the current position of the particle corresponding to the current optimal fitness value, otherwise do not update; It is determined whether the optimal fitness value of the historical group reaches the first set fitness value or whether the number of iterations reaches the maximum number of iterations T. If so, the optimal hyperparameters are obtained based on the particles at the optimal position of the historical group, and the target venous machine learning model is constructed using the optimal hyperparameters. Otherwise, it is determined whether the current number of iterations t is less than the set threshold T1. If yes, the genetic algorithm is used to perform hybridization operations on the updated particles, and the speed and position of each particle in the particle population formed after hybridization are updated again. If no, the speed and position are directly updated again, T / 2≤T1≤T.

6. The vascular lesion risk identification system based on machine learning model according to claim 5, characterized in that: The control host is further used to perform a hybridization operation on the updated particles using a genetic algorithm when the answer is yes, and update the speed and position of each particle in the particle population formed after the hybridization again: arrange the current historical individual optimal fitness values ​​of each particle in descending order, eliminate L1 particles with the lowest order, use the particle with the optimal position in the historical group among the non-eliminated particles as the parent generation, and randomly select L1 particles from the remaining non-eliminated particles as the parent generation, each selected parent generation is hybridized with the parent generation to generate L1 particles, and the current positions of the L1 particles generated by hybridization are respectively used as the positions of the corresponding particles after t iterations, and the current speed of each particle in the L1 particles generated by hybridization is the average speed of the particles on both sides of the particle, so as to obtain the particle population formed after the hybridization, and update the speed and position of each particle in the particle population formed after the hybridization again, L1 is a positive integer and 1≤L1≤L / 2, t=t+1.

7. The vascular disease risk identification system based on machine learning model according to claim 1, characterized in that: The control host is also used to construct a machine learning model for the target artery: the blood oxygen value, pH value, lactate value and pressure value in the blood of C historical artery testers are obtained through a blood oxygen sensor, a pH sensor, a lactate sensor and a pressure sensor respectively; high-resolution structural images of the inner wall of the blood vessels of the C historical artery testers are obtained through an ultrasonic sensor and intravascular ultrasound technology; the structural image of each historical artery tester is processed and then the blood vessel diameter, the thickness of the vascular intima, the thickness of the vascular middle membrane, the thickness of the vascular adventitia and whether there is an arterial plaque in the artery are analyzed; when the presence of an arterial plaque in the artery is analyzed, the composition and morphology of the arterial plaque are identified and the degree of stenosis of the vessel diameter is calculated; and arterial vascular lesion markers of each historical arterial tester are marked, and the arterial vascular lesion markers include a marker for no arterial vascular lesion, a marker for atherosclerosis, a marker for aneurysm, a marker for thromboangiitis obliterans and a marker for arterial dissection; Construct an arterial blood vessel feature set for each historical arterial tester. The arterial blood vessel feature set consists of blood oxygen value, pH value, lactate value, pressure value, blood vessel diameter, blood vessel intima thickness, blood vessel media thickness, blood vessel adventitia thickness, composition and morphology of arterial plaques when arterial plaques exist, and the degree of stenosis of the vessel diameter, as well as corresponding arterial blood vessel lesion markers. Construct an arterial blood vessel sample set. The arterial blood vessel sample set includes the arterial blood vessel feature sets of C historical arterial testers, where C is a positive integer. An arterial vascular machine learning model was constructed based on a convolutional neural network. The particle swarm algorithm and genetic algorithm were used to optimize the hyperparameters of the arterial vascular machine learning model. The arterial vascular sample set was used to train the arterial vascular machine learning model. The optimal hyperparameters were obtained through continuous iteration, and the target arterial vascular machine learning model was constructed using the optimal hyperparameters.

8. The vascular lesion risk identification system based on machine learning model according to claim 7, characterized in that: The control host is also used to build an arterial machine learning model based on a convolutional neural network, use a particle swarm algorithm and a genetic algorithm to optimize the hyperparameters of the arterial machine learning model, and use an arterial sample set to train the arterial machine learning model, obtain the optimal hyperparameters through continuous iteration, and use the optimal hyperparameters to build a target arterial machine learning model: An arterial vascular machine learning model is constructed based on a convolutional neural network, and the hyperparameters of the arterial vascular machine learning model are initialized. The number of particles N in the particle population, the maximum number of iterations S, and the particle search space dimension K corresponding to the hyperparameters are set. Each particle represents a set of hyperparameters, and the speed and position of each particle are initialized. Based on any particle used as a hyperparameter, a corresponding arterial blood vessel machine learning model is constructed, and the arterial blood vessel sample set is used to train the arterial blood vessel machine learning model corresponding to the particle, and a corresponding fitness value is calculated using a fitness function, where the fitness function is a function that is inversely proportional to the mean square error of the model deviation, and the fitness value of each particle is calculated based on this, the maximum fitness value among each particle is used as the optimal fitness, the current position of each particle is used as the individual optimal position of each particle, and the current position of the particle corresponding to the optimal fitness is used as the group optimal position of the particle population; Update the velocity and position of each particle: X nk (s+1)=X nk (s)+V nk (s+1) Among them, V nk (s) and X nk (s) represent the current particle speed and position, V nk (s+1) and X nk (s+1) represents the updated particle speed and position, n represents the nth particle, k represents the kth dimension of the search space, s represents the current iteration number, W(s) represents the adaptive inertia weight, W min2 Indicates the minimum inertia weight, W max2 Indicates the maximum inertia weight, C3 and C4 indicate the acceleration factor, Xpb nk Indicates the optimal position of the historical individual, and its corresponding fitness value is the optimal fitness value of the historical individual, Xgb k Indicates the optimal position of the historical group, and its corresponding fitness value is the optimal fitness value of the historical group; Update the historical individual optimal position and the historical group optimal position: use the fitness function to calculate the fitness value of each particle after the update, compare the updated fitness value of each particle with the corresponding historical individual optimal fitness value, if the updated fitness of any particle is greater than the corresponding historical individual optimal fitness value, then update the historical individual optimal fitness value of the particle = the updated fitness of the particle, and update the historical individual optimal position of the particle = the updated position of the particle, otherwise do not update; then take the maximum value of the historical individual optimal fitness values ​​of the current particles as the current optimal fitness value, if the current optimal fitness value is greater than the historical group optimal fitness value, then update the historical group optimal fitness value = the current optimal fitness value, and update the historical group optimal position = the current position of the particle corresponding to the current optimal fitness value, otherwise do not update; It is determined whether the optimal fitness value of the historical group reaches the second set fitness value or whether the number of iterations reaches the maximum number of iterations S. If so, the optimal hyperparameters are obtained based on the particles at the optimal position of the historical group, and the target arterial vascular machine learning model is constructed using the optimal hyperparameters. Otherwise, it is determined whether the current number of iterations s is less than the set threshold S1. If yes, the genetic algorithm is used to perform hybridization operations on the updated particles, and the speed and position of each particle in the particle population formed after hybridization are updated again. If no, the speed and position are directly updated again, S / 2≤S1≤S.

9. The vascular lesion risk identification system based on machine learning model according to claim 8, characterized in that: The control host is further used to perform a hybridization operation on the updated particles using a genetic algorithm when the answer is yes, and update the speed and position of each particle in the particle population formed after the hybridization again: arrange the current historical individual optimal fitness values ​​of each particle in descending order, eliminate N1 particles with the lowest order, use the particle with the optimal position in the historical group among the non-eliminated particles as the parent generation, and randomly select N1 particles from the remaining non-eliminated particles as the parent generation, each selected parent generation is hybridized with the parent generation to generate N1 particles, the current positions of the N1 particles generated by the hybridization are respectively used as the positions of the corresponding particles after s iterations, the current speed of each particle in the N1 particles generated by the hybridization is the average speed of the particles on both sides of the particle, so as to obtain the particle population formed after the hybridization, and update the speed and position of each particle in the particle population formed after the hybridization again, N1 is a positive integer and 1≤N1≤N / 2, s=s+1.

10. The vascular disease risk identification system based on machine learning model according to claim 1, characterized in that: The control host is also used to calculate the value of vessel diameter stenosis degree = (vessel diameter - arterial plaque cross-sectional area) / vessel diameter.

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