Neurosurgical operation risk intelligent assessment system
By building an intelligent assessment system for neurosurgery risks and using multi-source data and advanced algorithms to accurately predict risks, the shortcomings of traditional evaluation methods are solved, comprehensive and accurate assessment of neurosurgery risks and personalized decision-making support are achieved, and medical quality and resource utilization efficiency are improved.
Patent Information
- Application Number
- CN202510382171.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-28
- Publication Date
- 2025-07-08
AI Technical Summary
In the prior art, neurosurgery risk assessment depends on the doctor's personal experience and routine examination indicators, and it is difficult to comprehensively and accurately judge the risk, resulting in underestimation or overestimation of risks, and the evaluation is inefficient and lack of standardization, resulting in unreasonable allocation of medical resources and unstable medical quality.
Using multi-source data acquisition module, image analysis and feature extraction module, risk prediction model module and decision support and visualization module, a neurosurgery risk intelligent assessment system is built through deep learning and machine learning algorithms, integrating patient multi-source data for accurate risk prediction, and providing intuitive and visual decision support.
It has achieved a comprehensive and accurate assessment of the risks of neurosurgery, reduced risk misjudgment, improved surgical safety and efficiency of medical resource utilization, provided personalized surgical plans, and improved the stability and consistency of medical quality.
Smart Images
Figure CN120280148A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of neurosurgery, and specifically to an intelligent risk assessment system for neurosurgical operations. Background Art
[0002] In the practice of neurosurgical operations, currently, the assessment of surgical risks mainly relies on the personal experience of doctors and routine preoperative examination indicators. However, neurosurgical operations face the challenges of the fine structure of the human brain and significant individual differences, and there are serious limitations in traditional assessment methods. It is difficult to comprehensively and accurately judge surgical risks only based on experience and routine indicators, which may lead to underestimation or overestimation of risks. Underestimating risks will expose patients to unexpected dangers during the operation, while overestimating risks may cause some operations that could be implemented through reasonable plans to be abandoned. At the same time, traditional assessment methods are inefficient. Doctors need to spend a lot of time and energy, and there is no standardized process. The assessment results of different doctors vary greatly, which is not conducive to steadily improving the quality of medical care and reasonably allocating medical resources. Therefore, an intelligent risk assessment system for neurosurgical operations is proposed. Summary of the Invention
[0003] In view of this, the present invention provides an intelligent risk assessment system for neurosurgical operations to solve or alleviate the technical problems existing in the prior art and at least provide a beneficial option.
[0004] The technical solution of the present invention is realized as follows: An intelligent risk assessment system for neurosurgical operations includes the following modules: a multi-source data acquisition module, an image analysis and feature extraction module, a risk prediction model module, and a decision support and visualization module.
[0005] Further preferably, the multi-source data acquisition module has the function of comprehensively collecting various types of information of patients, including detailed medical history, covering past neurological disease history and other major disease histories; recent imaging data, such as high-resolution brain MRI, CT angiography, etc., and various preoperative physiological indicators, such as heart rate, blood pressure, blood sugar, coagulation function, etc.; collecting gene detection data, and through a variety of data interfaces, realizing seamless docking with the hospital information management system, imaging equipment, inspection equipment, etc. Using standardized data formats and coding rules, the data is initially cleaned and integrated to remove duplicate, incorrect or incomplete data, laying a reliable foundation for subsequent analysis.
[0006] Further preferably, the image analysis and feature extraction module deeply analyzes imaging data, uses advanced deep learning algorithms to identify features such as the location, size, shape of brain lesions and their relationship with surrounding neurovascular tissues. At the same time, it extracts subtle features such as texture and gray scale in the image, trains the neural network with a large amount of labeled imaging data, enables the model to learn the image feature patterns related to different lesion types and surgical risks. In actual application, the patient's imaging data is input into the trained model, and the model outputs the analysis results of lesion features.
[0007] Further preferably, the risk prediction model module constructs a risk prediction model based on the data provided by the multi-source data acquisition module and the image analysis and feature extraction module, comprehensively considers the patient's individual situation, and gives a quantitative risk score. It uses a combination of multiple machine learning algorithms such as logistic regression, decision tree, and support vector machine to train and optimize a large amount of historical surgical case data, continuously adjusts the model parameters, enables the model to accurately capture the complex relationship between different factors and surgical risks, and realizes accurate risk prediction.
[0008] Further preferably, the decision support and visualization module presents the risk prediction results to doctors in an intuitive visual way, and displays various risk probabilities, risk levels and comparison with patients of the same type in the form of charts. At the same time, it provides doctors with surgical decision-making suggestions based on the risk assessment results, uses data visualization technology to convert the risk data output by the model into intuitive graphics and text information, and the decision-making suggestions are generated based on the analysis and summary of a large number of successful surgical cases and risk response strategies, combined with the specific risk situation of the current patient.
[0009] Due to the above technical solutions adopted in the embodiments of the present invention, it has the following advantages:
[0010] First, the present invention combines multi-source data with advanced analysis algorithms. Compared with traditional methods, it can more comprehensively and accurately evaluate the risks of neurosurgical operations, reduce the misjudgment of risks caused by subjective judgment and insufficient information, improve the safety of operations, quickly generate risk assessment reports and decision-making suggestions, greatly save doctors' time and energy, enable doctors to make more scientific and reasonable surgical decisions in a short time, optimize the utilization of medical resources, provide a unified and standardized risk assessment process, reduce the differences in assessment results among different doctors, and contribute to improving the stability and consistency of medical quality.
[0011] Second, the present invention fully considers the individual differences of each patient, formulates personalized surgical plans and risk response strategies based on the unique physiological, pathological, and genetic information of the patient, improves the treatment effect and patient satisfaction, accumulates a large amount of surgical case data and risk assessment results, provides rich teaching materials for medical education, and also provides strong data support for scientific research related to the risks of neurosurgical operations, promoting the continuous development of medical research in this field.
[0012] The above summary is for the purpose of the specification only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the present invention will be readily apparent by reference to the drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS
[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.
[0014] Figure 1 It is a system flow chart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0015] In the following, only some exemplary embodiments are briefly described. As those skilled in the art can recognize, the described embodiments can be modified in various different ways without departing from the spirit or scope of the present invention. Therefore, the drawings and the description are considered to be exemplary in nature and not restrictive.
[0016] The embodiments of the present invention will be described in detail below with reference to the drawings.
[0017] As Figure 1 shown, the embodiments of the present invention provide an intelligent risk assessment system for neurosurgical operations, including the following modules: a multi-source data acquisition module, an image analysis and feature extraction module, a risk prediction model module, and a decision support and visualization module.
[0018] In one embodiment, the multi-source data acquisition module has the function of comprehensively collecting various types of information of patients, including detailed medical history, covering past neurological disease history and other major disease histories; recent imaging data, such as high-resolution brain MRI, CT angiography, etc., and various physiological indicators before surgery, such as heart rate, blood pressure, blood sugar, coagulation function, etc.; collecting gene detection data, and through a variety of data interfaces, realizing seamless docking with the hospital information management system, imaging equipment, inspection equipment, etc. Using standardized data formats and coding rules, the data is initially cleaned and integrated to remove duplicate, incorrect or incomplete data, laying a reliable foundation for subsequent analysis; for the docking with the hospital information system, through a data interface specification that conforms to medical industry standards, such as the HL7 (HealthLevel 7) protocol, the automatic acquisition of patient medical history data is realized. For imaging equipment, the DICOM (Digital Imaging and Communications in Medicine) standard interface is used to ensure the accurate transmission of CT, MRI and other imaging data to the system. Inspection equipment transmits physiological index data to the system in accordance with the standardized data format through its own data output interface. In the data cleaning stage, data cleaning algorithms are used to set data ranges, logical rules, etc. to automatically identify and process data that does not meet the requirements.
[0019] In one embodiment, the image analysis and feature extraction module deeply analyzes the imaging data, uses advanced deep learning algorithms to identify features such as the location, size, shape of brain lesions and their relationship with surrounding neurovascular tissues, and at the same time extracts subtle features such as texture and gray scale in the image. The neural network is trained with a large number of labeled imaging data to let the model learn the image feature patterns related to different lesion types and surgical risks. In actual application, the patient's imaging data is input into the trained model, and the model outputs the analysis results of lesion features; the convolutional neural network (CNN) is selected as the basic architecture of the deep learning model and trained with a large number of labeled neurosurgical imaging data. During the training process, the parameters of the network, such as the size of the convolutional kernel, the number of layers, the learning rate, etc., are continuously adjusted to improve the model's ability to identify lesion features. For the newly input patient imaging data, image preprocessing, including normalization, noise reduction and other operations, is first performed, and then it is input into the trained model for feature extraction and lesion analysis.
[0020] In one embodiment, the risk prediction model module constructs a risk prediction model based on the data provided by the multi-source data acquisition module and the image analysis and feature extraction module. It comprehensively considers the individual situation of the patient and gives a quantitative risk score. By using a combination of multiple machine learning algorithms such as logistic regression, decision tree, and support vector machine, it trains and optimizes a large amount of historical surgical case data, continuously adjusts the model parameters, enables the model to accurately capture the complex relationship between different factors and surgical risks, and realizes accurate risk prediction. A large amount of historical case data of neurosurgical operations is collected, including the patient's preoperative information, the actual risk situation during the operation, and the postoperative recovery situation, etc. These data are divided into a training set, a validation set, and a test set according to a certain proportion. By using a machine learning algorithm library, such as the Sci k it-l earn library in Python, the integration and training of multiple algorithms are realized. During the training process, the model parameters are continuously optimized through methods such as cross-validation to improve the prediction accuracy of the model. Finally, a model that can accurately predict surgical risks is obtained.
[0021] In one embodiment, the decision support and visualization module presents the risk prediction results to the doctor in an intuitive visual way and displays various risk probabilities, risk levels, and comparison situations with patients of the same type in the form of charts. At the same time, it provides surgical decision-making suggestions for the doctor based on the risk assessment results. By using data visualization technology, the risk data output by the model is converted into intuitive graphic and text information. The decision-making suggestions are generated based on the analysis and summary of a large number of successful surgical cases and risk response strategies, combined with the specific risk situation of the current patient. By using data visualization tools, such as Echarts, etc., the risk prediction results are presented in an intuitive chart form, including bar charts, line charts, pie charts, etc., to facilitate the doctor to intuitively understand various risk situations. For the decision-making suggestion part, a decision knowledge base is established, and information such as surgical plans, instrument selections, and perioperative treatment measures in a large number of successful surgical cases is sorted out and summarized. By matching the risk assessment results of the current patient, the corresponding suggestions are extracted from the decision knowledge base and provided to the doctor.
[0022] As described above, it is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of various changes or substitutions, and these should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claimed rights.
Claims
1. An intelligent risk assessment system for neurosurgery, characterized in that: It includes the following modules: multi-source data acquisition module, image analysis and feature extraction module, risk prediction model module, and decision support and visualization module.
2. The intelligent risk assessment system for neurosurgery according to claim 1, wherein: The multi-source data acquisition module has the function of comprehensively collecting various types of information of patients, including detailed medical history, covering past neurological disease history and other major disease histories; recent imaging data, such as high-resolution brain MRI, CT angiography, etc., preoperative physiological indicators, such as heart rate, blood pressure, blood sugar, coagulation function, etc.; collecting gene detection data, through a variety of data interfaces, realizing seamless docking with the hospital information management system, imaging equipment, inspection equipment, etc., using standardized data formats and coding rules, initially cleaning and integrating the data, removing duplicate, incorrect or incomplete data, and laying a reliable foundation for subsequent analysis.
3. The intelligent risk assessment system for neurosurgical operations according to claim 1, characterized in that: The image analysis and feature extraction module deeply analyzes the imaging data, uses advanced deep learning algorithms to identify features such as the location, size, shape of brain lesions and their relationship with surrounding neurovascular tissues, and at the same time, extracts subtle features such as texture and gray level in the image, trains the neural network with a large number of labeled imaging data, enables the model to learn the image feature patterns related to different lesion types and surgical risks, and in actual application, inputs the patient's imaging data into the trained model, and the model outputs the analysis results of lesion features.
4. The intelligent risk assessment system for neurosurgical operations according to claim 1, wherein: The risk prediction model module constructs a risk prediction model based on the data provided by the multi-source data acquisition module and the image analysis and feature extraction module, comprehensively considers the individual situation of the patient, and gives a quantitative risk score. It uses a combination of multiple machine learning algorithms such as logistic regression, decision tree, and support vector machine to train and optimize a large amount of historical surgical case data, continuously adjusts the model parameters, enables the model to accurately capture the complex relationship between different factors and surgical risks, and realizes accurate risk prediction.
5. The intelligent risk assessment system for neurosurgery according to claim 1, wherein: The decision support and visualization module presents the risk prediction results to the doctor in an intuitive visual way, and displays various risk probabilities, risk levels and comparison with patients of the same type in the form of charts. At the same time, it provides the doctor with surgical decision-making suggestions based on the risk assessment results, uses data visualization technology to convert the risk data output by the model into intuitive graphics and text information, and the decision-making suggestions are generated based on the analysis and summary of a large number of successful surgical cases and risk response strategies, combined with the specific risk situation of the current patient.