CN111276218A - Accurate diagnosis and treatment system, equipment and method - Google Patents
Accurate diagnosis and treatment system, equipment and method Download PDFInfo
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- 238000003745 diagnosis Methods 0.000 title claims abstract description 131
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Abstract
The application discloses an accurate diagnosis and treatment system, equipment and a method, which relate to the technical field of intelligent diagnosis and treatment, wherein the system comprises an electrocardiogram intelligent diagnosis module, a cardiovascular medical image analysis module and a cardiovascular disease prediction and screening module; the electrocardiogram intelligent diagnosis module is provided with a data unit, an interconnection unit, a demoulding unit and a training unit; the cardiovascular medical image analysis module is provided with a deep learning unit and an acquisition unit for acquiring medical image data of a large number of normal persons or patients and supplying the medical image data to the deep learning unit; the cardiovascular disease prediction and screening module is provided with a data experiment unit, and the data experiment unit is used for storing electrocardiogram image data, medical image data and biochemical examination data. The diagnosis and treatment device has the advantages that the diagnosis and treatment precision is remarkably improved, and the purposes of accurate and timely diagnosis and treatment and effect improvement are achieved.
Description
Technical Field
The application relates to the technical field of intelligent diagnosis and treatment, in particular to an accurate diagnosis and treatment system, equipment and method.
Background
The disease diagnosis mainly has two key points of accuracy and timeliness.
The accuracy of the diagnosis will directly affect the subsequent medical treatment and efficacy. In reality, the inquiry communication of doctors often causes a serious negligence due to the lack of the careful level and the comprehensive level. And statistical data show that the misdiagnosis rate of domestic and foreign hospitals is 30% on average.
The timeliness of the diagnosis will directly affect whether malignant and dangerous diseases can be detected at an early stage. The diagnosis timeliness is guaranteed, and practical and effective help is brought to patients through accurate diagnosis and adaptive treatment means.
At present, the intelligent diagnosis of the electrocardiogram has been researched at home and abroad. The Meio center realizes the diagnosis of left ventricular dysfunction by adopting an artificial intelligence method based on electrocardiogram. The Wunda team, Stanford university, reached expert-level arrhythmia detection with a convolutional neural network. The early atrial fibrillation is monitored by the American Alivecor based on an artificial intelligence KardiaPro platform. The french healthcare company cardiologs Technologies uses AI for electrocardiographic monitoring. The apple watch adopts artificial intelligence and electrocardiogram to detect and evaluate heart abnormity and the like. However, most of these methods and systems are based on single-lead or dual-lead electrocardiographic signals, and therefore, a lot of useful information is lost in the actual diagnosis of 12-lead electrocardiography. On the other hand, the data basis of the existing electrocardiogram intelligent diagnosis method is based on a pure heart beat electric signal acquired by the equipment. Because the electrocardiograph devices have different data structures and format standards, the electrocardiograph intelligent diagnosis products lack universality and cannot be suitable for electrocardiograph signals acquired by other devices.
Chinese patent publication No. CN108937918A discloses an accurate diagnosis and treatment system, which comprises a diagnosis module and a treatment module, wherein the diagnosis module is provided with an electrocardiograph, an electroencephalograph, a whole body CT machine, a nuclear magnetic resonance apparatus and a color ultrasonic diagnostic apparatus; the treatment module is provided with a computer diagnosis and treatment terminal, and the computer diagnosis and treatment terminal is provided with an operation and storage chip, input equipment and output equipment.
However, the accurate diagnosis and treatment system only acquires human body data in a centralized manner through connection of a plurality of devices, diagnosis and treatment precision is low, accuracy and timeliness of patient disease diagnosis are affected, and improvement is needed.
Disclosure of Invention
In view of this, a first objective of the present application is to provide an accurate diagnosis and treatment system to achieve the purpose of accurate and timely diagnosis and treatment and improving the effect. The specific scheme is as follows:
an accurate diagnosis and treatment system comprises an electrocardiogram intelligent diagnosis module, a cardiovascular medical image analysis module and a cardiovascular disease prediction and screening module;
the electrocardiogram intelligent diagnosis module is provided with a data unit, an interconnection unit, a demoulding unit and a training unit;
the data unit is used for adopting a multi-lead electrocardiogram image as a data base;
the interconnection unit is used for an online electrocardiograph to manually label the relevant data basis in the data unit to obtain the intelligent diagnosis of the electrocardiogram image;
the demolding unit is used for carrying out statistical modeling based on a deep neural network;
the training unit is used for training the electrocardiogram images by adopting a convolutional neural network as a training model;
the cardiovascular medical image analysis module is provided with a deep learning unit and an acquisition unit for acquiring medical image data of a large number of normal persons or patients and supplying the medical image data to the deep learning unit;
the cardiovascular disease prediction and screening module is provided with a data experiment unit, and the data experiment unit is used for storing electrocardiogram image data, medical image data and biochemical examination data.
Preferably: the training unit divides the electrocardiogram image into small image patches according to the lead signals, and trains the divided small image patches as network input when a convolutional neural network is adopted as a training model.
Preferably: the electrocardiogram intelligent diagnosis module is also provided with a cloud platform unit for data analysis and an AI intelligent unit for combining the data analysis of the cloud platform unit and the intelligent diagnosis of the electrocardiogram image, and the AI intelligent unit is used for optimizing the algorithm and improving the intelligent diagnosis quality of the electrocardiogram image.
Preferably: the medical image data includes one or more of echocardiography, coronary CTA, coronary angiography, and intravascular ultrasound (IVUS) and Optical Coherence Tomography (OCT).
Preferably: the medical image data is acquired from medical image data of an elderly person aged 65 years or older.
The second objective of the present application is to provide an accurate diagnosis and treatment apparatus, which includes the above accurate diagnosis and treatment system.
The third purpose of the application is to provide an accurate diagnosis and treatment method, which comprises an electrocardiogram intelligent diagnosis method, a cardiovascular medical image analysis method and a cardiovascular disease prediction and screening method;
the intelligent electrocardiogram diagnosis method comprises the following steps:
step 1, a data unit acquires a multi-lead electrocardiogram image, and the multi-lead electrocardiogram image is used as a data base;
step 2, an online electrocardiogram doctor receives the data basis through the interconnection unit, and manually marks and analyzes the related data basis to form intelligent diagnosis of an electrocardiogram image;
step 3, the demolding unit carries out statistical modeling on data basis and manual labeling and analysis based on the deep neural network;
step 4, the training unit divides the electrocardiogram image in the data base into small image patches according to the lead signals, and trains the divided small image patches as network input when a convolutional neural network is used as a training model;
step 5, after the cloud platform unit performs data analysis, the AI intelligent unit combines the data analysis of the cloud platform unit and the intelligent diagnosis of the electrocardiogram image, optimizes the algorithm and improves the intelligent diagnosis quality of the electrocardiogram image, and finally obtains the intelligent diagnosis of the electrocardiogram;
the cardiovascular medical image analysis method comprises the following steps:
step 1, collecting a large amount of medical image data of normal persons or patients by a collecting unit;
step 2, the deep learning unit acquires medical image data and performs cardiovascular medical image analysis by adopting a deep learning method;
the cardiovascular disease prediction and screening method comprises the following steps:
step 1, acquiring electrocardiogram image data, medical image data and biochemical examination data of a collaborative clinic and a hospital;
step 2, establishing a data experiment unit, recording and storing electrocardiogram image data, medical image data and biochemical examination data;
and 3, predicting and screening the cardiovascular diseases by combining the electrocardiogram image data, the medical image data and the biochemical examination data in the data experiment unit.
Preferably: in the cardiovascular medical image analysis method, the medical image data material includes one or more of echocardiography, coronary CTA, coronary angiography, and intravascular ultrasound (IVUS) and Optical Coherence Tomography (OCT).
Preferably: in the cardiovascular medical image analysis method, the medical image data is acquired from medical image data of an elderly person aged 65 years or older.
According to the scheme, the application provides the accurate diagnosis and treatment system, the accurate diagnosis and treatment equipment and the accurate diagnosis and treatment method, and the accurate diagnosis and treatment system, the accurate diagnosis and treatment equipment and the accurate diagnosis and treatment method have the following beneficial effects:
1. the universality and the practicability of the multi-lead electrocardiogram image are ensured by adopting the multi-lead electrocardiogram image as a data base;
2. through intelligent diagnosis around an electrocardiogram image, optimization algorithm and data analysis combined with a cloud platform, the quality of electrocardiogram diagnosis is continuously improved;
3. by establishing a data experiment unit for storing electrocardiogram image data, medical image data and biochemical examination data, solid data support is provided for clinical medical artificial intelligence research, and the treatment efficiency is improved;
4. through cooperating with clinic, hospital, the abundant data promotes the treatment effect, increases the benefited crowd.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a block diagram of an accurate diagnosis and treatment system disclosed in the present application;
FIG. 2 is a block diagram of the electrocardiogram intelligent diagnosis module disclosed in the present application;
fig. 3 is a block diagram of a cardiovascular medical image analysis module disclosed in the present application;
fig. 4 is a block diagram of a cardiovascular disease prediction and screening module disclosed in the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Example one
As shown in fig. 1, an accurate diagnosis and treatment system includes an electrocardiogram intelligent diagnosis module, a cardiovascular medical image analysis module, and a cardiovascular disease prediction and screening module; the electrocardiogram intelligent diagnosis module is used for processing the electrocardiogram image and obtaining electrocardiogram intelligent diagnosis; the cardiovascular medical image analysis module is used for carrying out cardiovascular medical image analysis on medical image data of a large number of normal persons or patients; the cardiovascular disease prediction and screening module is used for acquiring data and predicting and screening cardiovascular diseases through related data
As shown in fig. 2, the electrocardiogram intelligent diagnosis module is provided with a data unit, an interconnection unit, a demolding unit, a training unit, a cloud platform unit and an AI intelligent unit. The data unit is used for adopting a multi-lead electrocardiogram image as a data base; the interconnection unit is used for an online electrocardiograph to manually label the relevant data basis in the data unit to obtain the intelligent diagnosis of the electrocardiogram image; the demolding unit is used for performing statistical modeling based on the deep neural network; the training unit is used for dividing the electrocardiogram image into small image patches according to the lead signals, and training the small image patches which are divided by the training unit as network input when a convolutional neural network is used as a training model; the cloud platform unit is used for data analysis; the AI intelligent unit is used for combining the data analysis of the cloud platform unit and the intelligent diagnosis of the electrocardiogram image, so as to achieve the purposes of optimizing the algorithm and improving the intelligent diagnosis quality of the electrocardiogram image.
As shown in fig. 3, the cardiovascular medical image analysis module is provided with a deep learning unit and an acquisition unit for acquiring medical image data of a large number of normal persons or patients and providing the medical image data for the deep learning unit; medical image data was collected from medical image data of elderly people over 65 years of age and includes echocardiography, coronary CTA, coronary angiography, and intravascular ultrasound (IVUS) and Optical Coherence Tomography (OCT). The echocardiogram can display the structures of the heart and the great vessels in real time, reflect the blood flow speed and the type and evaluate the heart function state; the coronary CTA can three-dimensionally display coronary stenosis, position lesions such as plaque and the like, evaluate plaque components and stability, and has important significance for operation planning and postoperative evaluation such as PCI and the like; coronary angiography is one of the standards for examination of the heart and vascular system and is also the most important imaging means for guidance of interventional procedures; intravascular ultrasound (IVUS) and Optical Coherence Tomography (OCT) can better achieve images of the cross section of the vessel. At the same time, Optical Coherence Tomography (OCT) is more accurate for luminal stenosis assessment, while intravascular ultrasound (IVUS) is more advantageous for plaque component analysis.
As shown in fig. 4, the cardiovascular disease prediction and screening module is provided with a data experiment unit. The data experiment unit is used for storing electrocardiogram image data, medical image data and biochemical examination data. By combining electrocardiogram image data, medical image data and biochemical inspection data to predict and screen cardiovascular diseases, the method has the effect of remarkably improving the treatment efficiency.
The application also provides an accurate diagnosis and treatment device, which comprises the above accurate diagnosis and treatment system.
The application also provides an accurate diagnosis and treatment method, which comprises an electrocardiogram intelligent diagnosis method, a cardiovascular medical image analysis method and a cardiovascular disease prediction and screening method;
the intelligent electrocardiogram diagnosis method comprises the following steps:
step 1, a data unit acquires a multi-lead electrocardiogram image, and the multi-lead electrocardiogram image is used as a data base;
step 2, an online electrocardiogram doctor receives the data basis through the interconnection unit, and manually marks and analyzes the related data basis to form intelligent diagnosis of an electrocardiogram image;
step 3, the demolding unit carries out statistical modeling on data basis and manual labeling and analysis based on the deep neural network;
step 4, the training unit divides the electrocardiogram image in the data base into small image patches according to the lead signals, and trains the divided small image patches as network input when a convolutional neural network is used as a training model;
step 5, after the cloud platform unit performs data analysis, the AI intelligent unit combines the data analysis of the cloud platform unit and the intelligent diagnosis of the electrocardiogram image, optimizes the algorithm and improves the intelligent diagnosis quality of the electrocardiogram image, and finally obtains the intelligent diagnosis of the electrocardiogram;
the cardiovascular medical image analysis method comprises the following steps:
step 1, an acquisition unit acquires a large number of echocardiograms, coronary CTAs, coronary angiography, intravascular ultrasound (IVUS) and Optical Coherence Tomography (OCT) of normal or diseased elderly people over 65 years old;
step 2, the deep learning unit acquires an echocardiogram, coronary artery CTA, coronary angiography, intravascular ultrasound (IVUS) and Optical Coherence Tomography (OCT), and performs cardiovascular medical image analysis by adopting a deep learning method;
the cardiovascular disease prediction and screening method comprises the following steps:
step 1, acquiring electrocardiogram image data, medical image data and biochemical examination data of a collaborative clinic and a hospital;
step 2, establishing a data experiment unit, recording and storing electrocardiogram image data, medical image data and biochemical examination data;
and 3, predicting and screening the cardiovascular diseases by combining the electrocardiogram image data, the medical image data and the biochemical examination data in the data experiment unit.
Example two
As shown in fig. 1, an accurate diagnosis and treatment system includes an electrocardiogram intelligent diagnosis module, a cardiovascular medical image analysis module, and a cardiovascular disease prediction and screening module; the electrocardiogram intelligent diagnosis module is used for processing the electrocardiogram image and obtaining electrocardiogram intelligent diagnosis; the cardiovascular medical image analysis module is used for carrying out cardiovascular medical image analysis on medical image data of a large number of normal persons or patients; the cardiovascular disease prediction and screening module is used for acquiring data and predicting and screening cardiovascular diseases through related data
As shown in fig. 2, the electrocardiogram intelligent diagnosis module is provided with a data unit, an interconnection unit, a demolding unit, a training unit, a cloud platform unit and an AI intelligent unit. The data unit is used for adopting a multi-lead electrocardiogram image as a data base; the interconnection unit is used for an online electrocardiograph to manually label the relevant data basis in the data unit to obtain the intelligent diagnosis of the electrocardiogram image; the demolding unit is used for performing statistical modeling based on the deep neural network; the training unit is used for dividing the electrocardiogram image into small image patches according to the lead signals, and training the small image patches which are divided by the training unit as network input when a convolutional neural network is used as a training model; the cloud platform unit is used for data analysis; the AI intelligent unit is used for combining the data analysis of the cloud platform unit and the intelligent diagnosis of the electrocardiogram image, so as to achieve the purposes of optimizing the algorithm and improving the intelligent diagnosis quality of the electrocardiogram image.
As shown in fig. 3, the cardiovascular medical image analysis module is provided with a deep learning unit and an acquisition unit for acquiring medical image data of a large number of normal persons or patients and providing the medical image data for the deep learning unit; medical image data was acquired from medical image data of elderly people over 65 years of age and includes echocardiography, coronary CTA, coronary angiography, and intravascular ultrasound (IVUS). The echocardiogram can display the structures of the heart and the great vessels in real time, reflect the blood flow speed and the type and evaluate the heart function state; the coronary CTA can three-dimensionally display coronary stenosis, position lesions such as plaque and the like, evaluate plaque components and stability, and has important significance for operation planning and postoperative evaluation such as PCI and the like; coronary angiography is one of the standards for examination of the heart and vascular system and is also the most important imaging means for guidance of interventional procedures; intravascular ultrasound (IVUS) can better image the cross section of the vessel. Meanwhile, intravascular ultrasound (IVUS) has excellent plaque component analysis capability.
As shown in fig. 4, the cardiovascular disease prediction and screening module is provided with a data experiment unit. The data experiment unit is used for storing electrocardiogram image data, medical image data and biochemical examination data. By combining electrocardiogram image data, medical image data and biochemical inspection data to predict and screen cardiovascular diseases, the method has the effect of remarkably improving the treatment efficiency.
The application also provides an accurate diagnosis and treatment device, which comprises the above accurate diagnosis and treatment system.
The application also provides an accurate diagnosis and treatment method, which comprises an electrocardiogram intelligent diagnosis method, a cardiovascular medical image analysis method and a cardiovascular disease prediction and screening method;
the intelligent electrocardiogram diagnosis method comprises the following steps:
step 1, a data unit acquires a multi-lead electrocardiogram image, and the multi-lead electrocardiogram image is used as a data base;
step 2, an online electrocardiogram doctor receives the data basis through the interconnection unit, and manually marks and analyzes the related data basis to form intelligent diagnosis of an electrocardiogram image;
step 3, the demolding unit carries out statistical modeling on data basis and manual labeling and analysis based on the deep neural network;
step 4, the training unit divides the electrocardiogram image in the data base into small image patches according to the lead signals, and trains the divided small image patches as network input when a convolutional neural network is used as a training model;
step 5, after the cloud platform unit performs data analysis, the AI intelligent unit combines the data analysis of the cloud platform unit and the intelligent diagnosis of the electrocardiogram image, optimizes the algorithm and improves the intelligent diagnosis quality of the electrocardiogram image, and finally obtains the intelligent diagnosis of the electrocardiogram;
the cardiovascular medical image analysis method comprises the following steps:
step 1, an acquisition unit acquires a large number of echocardiograms, coronary CTAs, coronary angiography and intravascular ultrasound (IVUS) of normal or diseased elderly people over 65 years old;
step 2, the deep learning unit acquires an echocardiogram, coronary CTA, coronary angiography and intravascular ultrasound (IVUS), and performs cardiovascular medical image analysis by adopting a deep learning method;
the cardiovascular disease prediction and screening method comprises the following steps:
step 1, acquiring electrocardiogram image data, medical image data and biochemical examination data of a collaborative clinic and a hospital;
step 2, establishing a data experiment unit, recording and storing electrocardiogram image data, medical image data and biochemical examination data;
and 3, predicting and screening the cardiovascular diseases by combining the electrocardiogram image data, the medical image data and the biochemical examination data in the data experiment unit.
EXAMPLE III
As shown in fig. 1, an accurate diagnosis and treatment system includes an electrocardiogram intelligent diagnosis module, a cardiovascular medical image analysis module, and a cardiovascular disease prediction and screening module; the electrocardiogram intelligent diagnosis module is used for processing the electrocardiogram image and obtaining electrocardiogram intelligent diagnosis; the cardiovascular medical image analysis module is used for carrying out cardiovascular medical image analysis on medical image data of a large number of normal persons or patients; the cardiovascular disease prediction and screening module is used for acquiring data and predicting and screening cardiovascular diseases through related data
As shown in fig. 2, the electrocardiogram intelligent diagnosis module is provided with a data unit, an interconnection unit, a demolding unit, a training unit, a cloud platform unit and an AI intelligent unit. The data unit is used for adopting a multi-lead electrocardiogram image as a data base; the interconnection unit is used for an online electrocardiograph to manually label the relevant data basis in the data unit to obtain the intelligent diagnosis of the electrocardiogram image; the demolding unit is used for performing statistical modeling based on the deep neural network; the training unit is used for dividing the electrocardiogram image into small image patches according to the lead signals, and training the small image patches which are divided by the training unit as network input when a convolutional neural network is used as a training model; the cloud platform unit is used for data analysis; the AI intelligent unit is used for combining the data analysis of the cloud platform unit and the intelligent diagnosis of the electrocardiogram image, so as to achieve the purposes of optimizing the algorithm and improving the intelligent diagnosis quality of the electrocardiogram image.
As shown in fig. 3, the cardiovascular medical image analysis module is provided with a deep learning unit and an acquisition unit for acquiring medical image data of a large number of normal persons or patients and providing the medical image data for the deep learning unit; medical image data was collected from medical image data of elderly people over 65 years of age and included echocardiography, coronary CTA, coronary angiography, and Optical Coherence Tomography (OCT). The echocardiogram can display the structures of the heart and the great vessels in real time, reflect the blood flow speed and the type and evaluate the heart function state; the coronary CTA can three-dimensionally display coronary stenosis, position lesions such as plaque and the like, evaluate plaque components and stability, and has important significance for operation planning and postoperative evaluation such as PCI and the like; coronary angiography is one of the standards for examination of the heart and vascular system and is also the most important imaging means for guidance of interventional procedures; and the Optical Coherence Tomography (OCT) can better realize the image of the cross section of the blood vessel. At the same time, Optical Coherence Tomography (OCT) has the effect of accurately assessing luminal stenosis.
As shown in fig. 4, the cardiovascular disease prediction and screening module is provided with a data experiment unit. The data experiment unit is used for storing electrocardiogram image data, medical image data and biochemical examination data. By combining electrocardiogram image data, medical image data and biochemical inspection data to predict and screen cardiovascular diseases, the method has the effect of remarkably improving the treatment efficiency.
The application also provides an accurate diagnosis and treatment device, which comprises the above accurate diagnosis and treatment system.
The application also provides an accurate diagnosis and treatment method, which comprises an electrocardiogram intelligent diagnosis method, a cardiovascular medical image analysis method and a cardiovascular disease prediction and screening method;
the intelligent electrocardiogram diagnosis method comprises the following steps:
step 1, a data unit acquires a multi-lead electrocardiogram image, and the multi-lead electrocardiogram image is used as a data base;
step 2, an online electrocardiogram doctor receives the data basis through the interconnection unit, and manually marks and analyzes the related data basis to form intelligent diagnosis of an electrocardiogram image;
step 3, the demolding unit carries out statistical modeling on data basis and manual labeling and analysis based on the deep neural network;
step 4, the training unit divides the electrocardiogram image in the data base into small image patches according to the lead signals, and trains the divided small image patches as network input when a convolutional neural network is used as a training model;
step 5, after the cloud platform unit performs data analysis, the AI intelligent unit combines the data analysis of the cloud platform unit and the intelligent diagnosis of the electrocardiogram image, optimizes the algorithm and improves the intelligent diagnosis quality of the electrocardiogram image, and finally obtains the intelligent diagnosis of the electrocardiogram;
the cardiovascular medical image analysis method comprises the following steps:
step 1, collecting a large number of echocardiograms, coronary CTAs, coronary angiography and Optical Coherence Tomography (OCT) of normal or diseased old people with the age of more than 65 years by a collecting unit;
step 2, the deep learning unit acquires an echocardiogram, coronary artery CTA, coronary angiography and Optical Coherence Tomography (OCT), and performs cardiovascular medical image analysis by adopting a deep learning method;
the cardiovascular disease prediction and screening method comprises the following steps:
step 1, acquiring electrocardiogram image data, medical image data and biochemical examination data of a collaborative clinic and a hospital;
step 2, establishing a data experiment unit, recording and storing electrocardiogram image data, medical image data and biochemical examination data;
and 3, predicting and screening the cardiovascular diseases by combining the electrocardiogram image data, the medical image data and the biochemical examination data in the data experiment unit.
Example four
The fourth embodiment is different from the third embodiment in that the medical image data in the fourth embodiment includes echocardiography, coronary CTA, and Optical Coherence Tomography (OCT).
EXAMPLE five
The fifth embodiment is different from the third embodiment in medical image data in the fifth embodiment, and includes echocardiography, coronary angiography, and Optical Coherence Tomography (OCT).
EXAMPLE six
The sixth embodiment differs from the third embodiment in the medical image data in the sixth embodiment, and includes coronary CTA, coronary angiography, and Optical Coherence Tomography (OCT).
In conclusion, the application ensures the universality and the practicability by adopting the multi-lead electrocardiogram image as a data base; further, the electrocardio diagnosis quality is continuously improved through intelligent diagnosis around the electrocardiogram image, optimization algorithm and data analysis combined with a cloud platform; the data experiment unit for storing electrocardiogram image data, medical image data and biochemical examination data is established, so that solid data support is provided for clinical medical artificial intelligence research, and the treatment efficiency is improved; meanwhile, through cooperation with clinics and hospitals, data information is enriched, diagnosis and treatment effects are improved, and beneficial groups are increased. The health examination is accomplished more rapidly to the help patient, when finding the focus place, also helps the image doctor to promote and read the piece efficiency, reduces the misdiagnosis probability, and then more convenient provides service for the user.
References in this application to "first," "second," "third," "fourth," etc., if any, are intended to distinguish between similar elements and not necessarily to describe a particular order or sequence. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein. Furthermore, the terms "comprises" and "comprising," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, or apparatus.
It should be noted that the descriptions in this application referring to "first", "second", etc. are for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In addition, technical solutions between various embodiments may be combined with each other, but must be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present application.
The principle and the implementation of the present application are explained herein by applying specific examples, and the above description of the embodiments is only used to help understand the method and the core idea of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (9)
1. The utility model provides an accurate system of diagnosing which characterized in that: the cardiovascular disease prediction and screening system comprises an electrocardiogram intelligent diagnosis module, a cardiovascular medical image analysis module and a cardiovascular disease prediction and screening module;
the electrocardiogram intelligent diagnosis module is provided with a data unit, an interconnection unit, a demoulding unit and a training unit;
the data unit is used for adopting a multi-lead electrocardiogram image as a data base;
the interconnection unit is used for an online electrocardiograph to manually label the relevant data basis in the data unit to obtain the intelligent diagnosis of the electrocardiogram image;
the demolding unit is used for carrying out statistical modeling based on a deep neural network;
the training unit is used for training the electrocardiogram images by adopting a convolutional neural network as a training model;
the cardiovascular medical image analysis module is provided with a deep learning unit and an acquisition unit for acquiring medical image data of a large number of normal persons or patients and supplying the medical image data to the deep learning unit;
the cardiovascular disease prediction and screening module is provided with a data experiment unit, and the data experiment unit is used for storing electrocardiogram image data, medical image data and biochemical examination data.
2. The precision diagnosis and treatment system according to claim 1, wherein: the training unit divides the electrocardiogram image into small image patches according to the lead signals, and trains the divided small image patches as network input when a convolutional neural network is adopted as a training model.
3. The precision diagnosis and treatment system according to claim 1, wherein: the electrocardiogram intelligent diagnosis module is also provided with a cloud platform unit for data analysis and an AI intelligent unit for combining the data analysis of the cloud platform unit and the intelligent diagnosis of the electrocardiogram image, and the AI intelligent unit is used for optimizing the algorithm and improving the intelligent diagnosis quality of the electrocardiogram image.
4. The precision diagnosis and treatment system according to claim 1, wherein: the medical image data includes one or more of echocardiography, coronary CTA, coronary angiography, and intravascular ultrasound (IVUS) and Optical Coherence Tomography (OCT).
5. The precision diagnosis and treatment system according to claim 1, wherein: the medical image data is acquired from medical image data of an elderly person aged 65 years or older.
6. The utility model provides an accurate diagnosis and treatment equipment which characterized in that: comprising an accurate medical treatment system according to any one of claims 1-5.
7. An accurate diagnosis and treatment method is characterized in that: comprises an electrocardiogram intelligent diagnosis method, a cardiovascular medical image analysis method and a cardiovascular disease prediction and screening method;
the intelligent electrocardiogram diagnosis method comprises the following steps:
step 1, a data unit acquires a multi-lead electrocardiogram image, and the multi-lead electrocardiogram image is used as a data base;
step 2, an online electrocardiogram doctor receives the data basis through the interconnection unit, and manually marks and analyzes the related data basis to form intelligent diagnosis of an electrocardiogram image;
step 3, the demolding unit carries out statistical modeling on data basis and manual labeling and analysis based on the deep neural network;
step 4, the training unit divides the electrocardiogram image in the data base into small image patches according to the lead signals, and trains the divided small image patches as network input when a convolutional neural network is used as a training model;
step 5, after the cloud platform unit performs data analysis, the AI intelligent unit combines the data analysis of the cloud platform unit and the intelligent diagnosis of the electrocardiogram image, optimizes the algorithm and improves the intelligent diagnosis quality of the electrocardiogram image, and finally obtains the intelligent diagnosis of the electrocardiogram;
the cardiovascular medical image analysis method comprises the following steps:
step 1, collecting a large amount of medical image data of normal persons or patients by a collecting unit;
step 2, the deep learning unit acquires medical image data and performs cardiovascular medical image analysis by adopting a deep learning method;
the cardiovascular disease prediction and screening method comprises the following steps:
step 1, acquiring electrocardiogram image data, medical image data and biochemical examination data of a collaborative clinic and a hospital;
step 2, establishing a data experiment unit, recording and storing electrocardiogram image data, medical image data and biochemical examination data;
and 3, predicting and screening the cardiovascular diseases by combining the electrocardiogram image data, the medical image data and the biochemical examination data in the data experiment unit.
8. The precise diagnosis and treatment method according to claim 7, wherein: in the cardiovascular medical image analysis method, the medical image data material includes one or more of echocardiography, coronary CTA, coronary angiography, and intravascular ultrasound (IVUS) and Optical Coherence Tomography (OCT).
9. The precise diagnosis and treatment method according to claim 7, wherein: in the cardiovascular medical image analysis method, the medical image data is acquired from medical image data of an elderly person aged 65 years or older.
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