Brain trauma postoperative recovery evaluation system based on artificial intelligence

Abstract

The invention discloses a brain trauma postoperative recovery evaluation system based on artificial intelligence, which combines artificial intelligence and medical technology and provides an innovative scheme for postoperative recovery evaluation of a brain trauma patient. By collecting and analyzing medical images, physiological parameters and other multi-dimensional data, a prediction model is constructed by using LSTM and other advanced algorithms, and the recovery degree and speed of a patient are accurately evaluated; the system can automatically generate a personalized rehabilitation plan, dynamically adjust according to the rehabilitation progress, provide a visual evaluation report and a rehabilitation curve chart, and enhance doctor decision support and patient participation. Compared with a traditional evaluation method, the system significantly improves the evaluation accuracy and efficiency, reduces the workload of doctors, and improves the rehabilitation effect and life quality of patients. Meanwhile, innovation and development of medical technologies are promoted, new vitality is injected into the field of postoperative recovery evaluation of cerebral trauma, formulation of industrial standards and specifications is expected to be guided, and improvement of the overall level of the medical field is promoted.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a recovery assessment system for patients after brain trauma surgery, which uses artificial intelligence technology to accurately assess the recovery of patients after brain trauma surgery. Background Art

[0002] In the field of postoperative recovery assessment after brain trauma, the current state of technology mainly presents the following characteristics:

[0003] Multidimensional data collection and analysis:

[0004] With the continuous development of medical technology, multi-dimensional human body information collection technology has been widely used in the postoperative recovery assessment of brain trauma patients. This includes real-time monitoring and analysis of multiple physiological parameters such as EEG, EMG, joint movement, and gait.

[0005] This data provides doctors with comprehensive information on the patient's status, helping to more accurately assess the patient's recovery.

[0006] Intelligent algorithms and model applications:

[0007] Artificial intelligence algorithms, especially deep learning algorithms such as LSTM (long short-term memory network), have shown great potential in the assessment of postoperative recovery after brain trauma.

[0008] These algorithms can build predictive models by learning the recovery data and characteristics of historical patients, thereby accurately predicting the recovery of new patients.

[0009] Rehabilitation equipment and virtual reality technology:

[0010] Combined with advanced technologies such as virtual reality (VR) and brain-computer interface (BCI), intelligent rehabilitation equipment such as rehabilitation robots and dynamic balance training devices have been used clinically.

[0011] These devices not only provide personalized rehabilitation training programs, but also help patients better recover their functions through real-time feedback mechanisms.

[0012] Brain functional network and prediction of consciousness recovery:

[0013] Research on brain functional networks provides a new perspective for predicting the recovery of consciousness. By analyzing the activity patterns and connection strengths of brain functional networks, scientists and doctors can more accurately determine whether a brain-injured patient is likely to regain consciousness.

[0014] Although current technology has made significant progress in the assessment of recovery after brain injury surgery, there are still some drawbacks and challenges:

[0015] Data Quality and Integrity:

[0016] The accuracy and completeness of data are the foundation of the predictive accuracy of artificial intelligence algorithms. However, in actual applications, due to various factors in the data collection process (such as equipment failure, patient compliance, etc.), data may be biased or missing.

[0017] This may lead to inaccurate algorithm prediction results and even mislead doctors' judgment.

[0018] Interpretability and robustness of the algorithm:

[0019] Current AI algorithms, particularly deep learning algorithms, are often viewed as “black box” operations, meaning doctors may struggle to understand how the algorithms make their predictions, reducing their credibility.

[0020] In addition, the robustness of the algorithm is also an important issue. When faced with complex and changing clinical situations, the algorithm may not be able to maintain stable and accurate prediction performance.

[0021] Limitations of individualized rehabilitation programs:

[0022] Although intelligent rehabilitation devices provide personalized rehabilitation training programs, these programs are often based on preset algorithms and parameters. In actual applications, individual differences among patients may lead to incomplete applicability of the programs.

[0023] Therefore, how to flexibly adjust the rehabilitation program according to the patient's specific situation remains a challenge.

[0024] In summary, the AI-based postoperative recovery assessment system for brain trauma has made significant progress in terms of technological status, but there are still some drawbacks and challenges, including: data quality and integrity, algorithm interpretability and robustness, limitations of personalized rehabilitation programs, privacy and security issues, and the integration of technology and clinical practice. Summary of the Invention

[0025] The purpose of the present invention is to provide an artificial intelligence-based postoperative recovery assessment system for brain trauma. The system can analyze and predict the patient's postoperative medical imaging data, physiological parameters and clinical manifestations through deep learning algorithms, thereby providing accurate recovery assessment results and providing strong support for doctors to formulate personalized rehabilitation plans.

[0026] To achieve the above object, the present invention adopts the following technical solutions:

[0027] An artificial intelligence-based postoperative recovery assessment system for brain trauma, comprising:

[0028] a) Data acquisition module, used to collect the patient's medical imaging data, physiological parameters and clinical performance data;

[0029] b) a data preprocessing module, which performs denoising, cleaning, and normalization on the collected data to obtain preprocessed data;

[0030] c) a feature extraction module that uses a convolutional neural network to extract spatial features from the medical imaging data in the preprocessed data, uses a recurrent neural network to extract temporal features from the physiological parameter time series data in the preprocessed data, and combines the clinical manifestation data in the preprocessed data to form a comprehensive feature vector;

[0031] d) Deep learning model, using long short-term memory network as the core architecture, training the comprehensive feature vector and building a long short-term memory network model for recovery assessment;

[0032] e) an assessment result output module, which inputs the data of the patient to be assessed from step a) into the trained long short-term memory network model and outputs the recovery assessment results, including the degree of recovery and the speed of recovery;

[0033] f) User interface module, which provides a user-friendly interface for doctors to input patient data, view assessment results, and develop personalized rehabilitation plans based on the assessment results.

[0034] Furthermore, in the deep learning model, the loss function of the long short-term memory network model adopts the cross entropy loss function, and the formula is:

[0035]

[0036] Among them, N is the number of samples, yi is the true label of sample i, is the predicted label of sample i.

[0037] Furthermore, in the evaluation result output module, the predicted value of the recovery degree is calculated by the softmax function, and the formula is:

[0038]

[0039] Among them, z i is the original output value of the i-th node in the output layer of the long short-term memory network model, and C is the total number of nodes in the output layer, that is, the number of classifications of the degree of recovery;

[0040] Furthermore, the data acquisition module also includes a data synchronization mechanism, which specifically includes:

[0041] Timestamp synchronization: Accurately timestamp medical imaging data, physiological parameters, and clinical performance data to ensure that all data are collected at the same or similar time points;

[0042] Data format conversion: converting data from different sources into a unified format for subsequent processing and analysis;

[0043] Abnormal data detection: Real-time monitoring of abnormal or missing values during data collection, and prompt reminders to operators for processing.

[0044] Furthermore, the data preprocessing module also includes a data enhancement step, which specifically includes:

[0045] Medical image enhancement: rotate, scale, translate, flip, add noise, and blur medical image data;

[0046] Physiological parameter interpolation: For missing physiological parameter data, linear interpolation and spline interpolation methods are used to complete them;

[0047] Normalization processing: convert physiological parameter data into standard normal distribution.

[0048] Furthermore, the feature extraction module also includes an attention mechanism, which specifically includes:

[0049] Spatial attention: weighting key regions in medical imaging data to highlight features related to postoperative recovery after traumatic brain injury;

[0050] Temporal attention: weighting the key periods in the physiological parameter time series data to capture the dynamic changes closely related to the recovery process;

[0051] Multimodal fusion: Medical imaging data, physiological parameters, and clinical manifestation data are integrated through the attention mechanism to form a comprehensive feature vector that comprehensively reflects the patient's recovery status.

[0052] Furthermore, the deep learning model also includes a regularization term, which specifically includes:

[0053] L2 regularization: imposes a penalty term on the model weights to prevent overfitting caused by excessive weights;

[0054] Dropout: Randomly discard some neurons during model training to reduce the co-adaptability between neurons;

[0055] Early stopping method: Stop training when the performance of the validation set no longer improves to avoid overfitting.

[0056] Furthermore, the evaluation result output module also includes a visualization tool, which specifically includes:

[0057] Recovery curve chart: With time as the horizontal axis and recovery degree as the vertical axis, the patient's recovery curve is drawn to intuitively show the recovery process;

[0058] Heatmap: Visualize medical imaging data to highlight key areas related to recovery;

[0059] Interactive reports: Generate interactive reports containing basic patient information, assessment results, and visual charts for easy viewing and analysis by doctors.

[0060] The present invention proposes an artificial intelligence-based postoperative recovery assessment system for brain trauma, which has the following beneficial effects:

[0061] 1. Improving Assessment Accuracy and Efficiency

[0062] Multi-dimensional Data Analysis: The system integrates and analyzes multi-dimensional data from medical images, physiological parameters, and other sources to provide a more comprehensive and accurate assessment. This comprehensive assessment method can more accurately reflect the patient's recovery status than traditional single-metric assessments.

[0063] Intelligent Algorithm Prediction: By applying advanced AI algorithms such as LSTM, the system can learn from historical data and predict patients' recovery trends, providing doctors with forward-looking assessment results. This not only improves assessment accuracy but also significantly shortens the assessment cycle and improves overall efficiency.

[0064] 2. Development of a Personalized Rehabilitation Plan

[0065] Customized rehabilitation plan: Based on the patient's specific recovery status and assessment results, the system can automatically generate a personalized rehabilitation plan. This customized plan can better meet the patient's actual needs and improve rehabilitation results.

[0066] Dynamic adjustment and feedback: The system can adjust the rehabilitation plan in real time according to the patient's recovery progress and provide a feedback mechanism to help patients and doctors better track the rehabilitation process and ensure the effective implementation of the rehabilitation plan.

[0067] 3. Enhance physician decision support

[0068] Auxiliary diagnosis and decision-making: The assessment results and rehabilitation plan recommendations provided by the system can provide doctors with powerful auxiliary diagnostic information, helping doctors to more accurately judge the patient's recovery status and formulate more reasonable treatment plans.

[0069] Reduce doctors' workload: Through automated and intelligent evaluation processes, the system can reduce doctors' workload in data collection, analysis and evaluation, allowing doctors to focus more on patients' clinical treatment and rehabilitation guidance.

[0070] 4. Promote patient engagement and improve patient satisfaction

[0071] Improved patient engagement: The visual assessment results and rehabilitation plans provided by the system can enhance patients' understanding and confidence in the rehabilitation process, thereby improving their engagement and enthusiasm.

[0072] Visualization of rehabilitation effects: Through intuitive charts and reports, patients can clearly see their recovery progress, which helps improve patient satisfaction and rehabilitation motivation.

[0073] V. Promoting innovation and development of medical technology

[0074] Technological integration and innovation: The development and application of the system have promoted technological innovation and integration in multiple fields such as medical image processing, artificial intelligence algorithms, and rehabilitation equipment, injecting new vitality into the development of the medical field.

[0075] Development of industry standards and specifications: With the widespread application and recognition of the system, it is expected to promote the formulation and improvement of relevant industry standards and specifications in the field of postoperative recovery assessment of brain trauma, and further improve the level of the entire industry.

[0076] In summary, the AI-based postoperative brain injury recovery assessment system has demonstrated significant benefits in improving assessment accuracy and efficiency, developing personalized rehabilitation plans, enhancing physician decision support, promoting patient engagement and satisfaction, and driving innovation and development in medical technology. These benefits not only contribute to improving the rehabilitation outcomes and quality of life for brain injury patients but also provide new directions and impetus for technological innovation and development in the medical field. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 This is a schematic diagram of the system structure of an artificial intelligence-based brain injury postoperative recovery assessment system of the present invention;

[0078] Figure 2 This is a system flow diagram of an artificial intelligence-based brain trauma postoperative recovery assessment system of the present invention. DETAILED DESCRIPTION

[0079] Technical solution content

[0080] This invention provides an artificial intelligence-based system for assessing postoperative brain injury recovery. The system includes a data acquisition module, a data preprocessing module, a feature extraction module, a deep learning model, an assessment result output module, and a user interface module. The specific implementation of each module is described in detail below.

[0081] 1. Data acquisition module

[0082] The data acquisition module is responsible for collecting the patient's medical imaging data, physiological parameters and clinical manifestation data. In order to ensure the synchronization and accuracy of the data, the data acquisition module adopts a timestamp synchronization mechanism to accurately timestamp all data and monitor abnormal values or missing values in real time.

[0083] The specific implementation process is:

[0084] 1.1 Data Collection Preparation

[0085] 1.1.1 Determine the data collection source:

[0086] Medical imaging data: from the hospital's radiology or neurosurgery department, obtained through CT or MRI scanning equipment;

[0087] Physiological parameters: monitoring equipment from the hospital, including electrocardiographs, blood pressure monitors, and blood oxygen saturation monitors;

[0088] Clinical performance data: clinical evaluation and records from doctors, including Glasgow Coma Scale (GCS) and Neurological impairment Scale (NIHSS).

[0089] 1.1.2 Configure data acquisition equipment:

[0090] Ensure that all data acquisition equipment is working properly and can be connected to the data acquisition system;

[0091] Calibrate and debug medical imaging equipment to ensure image quality meets standards;

[0092] Perform regular maintenance and calibration of physiological parameter monitoring equipment to ensure data accuracy.

[0093] 1.2 Data Collection Process

[0094] 1.2.1 Medical imaging data collection:

[0095] After the patient enters the examination room, a CT or MRI scan is performed according to the doctor's instructions;

[0096] After the scan is completed, the medical imaging data is exported from the device and stored in a designated database or file;

[0097] The data acquisition module automatically reads the newly generated medical imaging data and adds a time stamp to it.

[0098] 1.2.2 Physiological parameter data collection:

[0099] After the patient is admitted to the intensive care unit, he or she will be connected to a physiological parameter monitoring device;

[0100] The monitoring equipment collects the patient's heart rate, blood pressure, blood oxygen saturation and other physiological parameters in real time and transmits the data to the data acquisition system;

[0101] The data acquisition module receives physiological parameter data and adds an accurate time stamp to it.

[0102] 1.2.3 Clinical manifestation data collection:

[0103] Doctors conduct clinical assessments of patients and record clinical manifestation data such as GCS scores and NIHSS scores;

[0104] The doctor inputs the clinical performance data into the user interface or designated data input port of the data collection system;

[0105] The data acquisition module receives clinical performance data, adds a time stamp to it, and associates it with medical imaging data and physiological parameter data.

[0106] 1.3 Data synchronization and monitoring

[0107] 1.3.1 Timestamp synchronization mechanism:

[0108] The data acquisition module has a built-in timestamp synchronization mechanism to ensure that all collected data has an accurate timestamp;

[0109] The timestamp uses the Universal Time Coordinated (UTC) or local time, but must be unified within the system.

[0110] 1.3.2 Monitoring of outliers or missing values:

[0111] The data acquisition module monitors the collected data in real time and detects abnormal values (such as physiological parameter values outside the normal range) or missing values (such as data points that were not collected);

[0112] When abnormal values or missing values are detected, the data acquisition module automatically records and issues a warning, prompting the operator to handle it.

[0113] 1.3.3 Data quality control:

[0114] The data acquisition module is also responsible for data quality control, including data integrity, accuracy and consistency checks;

[0115] For data that does not meet quality requirements, the data collection module will mark or eliminate them to ensure the accuracy of subsequent analysis and evaluation.

[0116] 1.4 Data Storage and Backup

[0117] 1.4.1 Data Storage:

[0118] The collected data will be stored in a designated database or file for subsequent analysis and evaluation;

[0119] Databases or files should use encryption and security measures to ensure data security and privacy.

[0120] 1.4.2 Data backup:

[0121] The data acquisition module should regularly back up stored data to prevent data loss or damage;

[0122] Backup data should be stored in secure physical or cloud storage media, and recovery tests should be performed regularly to ensure the effectiveness of the backups.

[0123] Among them, the specific types of medical imaging data, physiological parameters and clinical manifestation data are:

[0124] Medical imaging data

[0125] Medical imaging data is image information about the internal structure and function of a patient's body obtained through various medical imaging devices. Common types of medical imaging data include:

[0126] CT (Computed Tomography) images: CT images are reconstructed by X-ray tomography of the human body. CT images have high resolution and can clearly show the internal structure and pathological conditions of the human body.

[0127] MRI (Magnetic Resonance Imaging) images: MRI images use magnetic fields and radio frequency waves to image the human body. MRI images can show the structure and function of internal soft tissues (such as the brain and spinal cord), and are of great significance for the diagnosis of diseases such as brain trauma.

[0128] Physiological parameters

[0129] Physiological parameters are various indicators that reflect the physiological state of the human body. They are usually collected in real time through monitoring equipment. Common types of physiological parameters include:

[0130] Heart rate: Heart rate refers to the number of times the heart beats per minute and is an important indicator of heart function;

[0131] Blood pressure: Blood pressure refers to the pressure exerted on the blood vessel walls by blood as it flows through them. It is divided into systolic pressure and diastolic pressure and is a key indicator for assessing the health of the cardiovascular system.

[0132] Blood oxygen saturation: Blood oxygen saturation refers to the amount of oxygen in the blood, usually expressed as a percentage. Blood oxygen saturation is an important indicator for assessing the health of the respiratory and circulatory systems.

[0133] Clinical performance data

[0134] Clinical manifestation data refers to information such as patient symptoms and signs obtained by doctors through clinical observation and evaluation. Common types of clinical manifestation data include:

[0135] Glasgow Coma Scale (GCS): GCS is a scoring system for assessing the patient's state of consciousness, including eye opening response, language response and motor response. The maximum total score is 15 points and the minimum score is 3 points. The lower the score, the more severe the consciousness disorder.

[0136] Neurological impairment score (NIHSS): NIHSS is a scoring system for assessing the degree of neurological impairment in patients, including level of consciousness, gaze, visual field, facial paralysis, upper limb movement, lower limb movement, ataxia, sensation, language, dysarthria and neglect. The higher the total score, the more severe the neurological impairment.

[0137] These data types together constitute the main content that needs to be collected by the data acquisition module. The timestamp synchronization mechanism ensures the synchronization and accuracy of the data, and monitors abnormal values or missing values in real time, providing reliable data support for subsequent medical analysis and evaluation.

[0138] 2. Data preprocessing module

[0139] The data preprocessing module performs preprocessing operations such as denoising, cleaning, and normalization on the collected data. For medical imaging data, image enhancement techniques (such as rotation, scaling, and translation) are used to increase data diversity; for physiological parameter data, linear interpolation methods are used to fill missing values and perform standardization; for clinical manifestation data, normalization is performed directly; the preprocessed data will be used for subsequent feature extraction and model training.

[0140] The specific implementation process is:

[0141] 2.1 Medical Image Data Preprocessing

[0142] 2.1.1 Image Denoising

[0143] Medical imaging data will be affected by noise during the acquisition process, such as equipment noise, environmental noise, etc.

[0144] Filtering technology (including Gaussian filtering and mean filtering) is used to remove noise from the image and improve image quality.

[0145] 2.1.2 Image Enhancement

[0146] To increase the diversity of medical imaging data, image enhancement techniques are used, including rotation, scaling, and translation;

[0147] These operations can simulate different perspectives and scales, which helps improve the generalization ability of subsequent models.

[0148] 2.1.3 Image Normalization

[0149] Medical imaging data has different pixel value ranges, so normalization is performed to scale the pixel values to a uniform range (i.e., 0-1);

[0150] Normalization helps reduce the brightness differences between different images and improves the training effect of the model.

[0151] 2.2 Physiological parameter data preprocessing

[0152] 2.2.1 Missing Value Handling

[0153] Physiological parameter data may have missing values during the collection process, such as measurement interruptions caused by equipment failure, patient movement, etc.

[0154] Linear interpolation method is used to fill in missing values, and the size of the missing value is calculated based on the adjacent valid values.

[0155] 2.2.2 Standardization

[0156] The dimensions and distributions of physiological parameter data were different, so they were normalized and converted to a standard normal distribution with a mean of 0 and a standard deviation of 1;

[0157] Standardization helps to eliminate the dimensional differences between different parameters and improve the stability and accuracy of the model.

[0158] 2.3 Clinical manifestation data preprocessing

[0159] 2.3.1 Data Cleaning

[0160] Clinical performance data contains abnormal or duplicate values and needs to be cleaned;

[0161] Outliers are caused by data entry errors, measurement errors, etc. and should be deleted or replaced with reasonable values;

[0162] Duplicate values are caused by repeated data entry or multiple evaluations. Duplicate records are deleted.

[0163] 2.3.2 Normalization

[0164] The clinical manifestation data have different value ranges, so normalization is performed to scale the data to a uniform range (i.e., 0–1);

[0165] Normalization helps reduce the numerical differences between different indicators and improve the training effect of the model.

[0166] 2.4 Storage and transmission of preprocessed data

[0167] 2.4.1 Data Storage

[0168] The preprocessed data is stored in a specified database or file for subsequent feature extraction and model training;

[0169] The database or file uses encryption and security measures to ensure the security and privacy of the data.

[0170] 2.4.2 Data Transmission

[0171] The preprocessed data is transferred to the feature extraction module or model training module for further processing;

[0172] Encryption and verification mechanisms are used during data transmission to ensure data integrity and reliability.

[0173] Through the above steps, the data preprocessing module can perform effective preprocessing operations on the collected medical imaging data, physiological parameter data, and clinical manifestation data, providing high-quality data support for subsequent feature extraction and model training.

[0174] 3. Feature Extraction Module

[0175] The feature extraction module uses deep learning algorithms to extract key features from preprocessed data. For medical imaging data, a convolutional neural network (CNN) is used to extract spatial features; for physiological parameter data, a recurrent neural network (RNN), especially a long short-term memory network (LSTM), is used to extract temporal features. At the same time, an attention mechanism is introduced to weight key information to improve the accuracy of feature extraction. The output of the feature extraction module is a comprehensive feature vector that contains key information related to postoperative recovery after brain trauma.

[0176] The specific implementation process is:

[0177] 3.1 Feature Extraction of Medical Imaging Data

[0178] 3.1.1 Convolutional Neural Network (CNN) Model Selection

[0179] Select CNN models suitable for medical imaging data processing, including ResNet, VGG, etc.;

[0180] Adjust model parameters such as the number of layers and convolution kernel size according to the characteristics of the specific task and dataset.

[0181] 3.1.2 Spatial Feature Extraction

[0182] Input the preprocessed medical imaging data into the CNN model;

[0183] The CNN model gradually extracts spatial features in the image, including edges, textures, shapes, etc., through structures such as convolutional layers and pooling layers; each convolutional layer can extract more abstract features from the output of the previous layer, and then pass them to the next layer layer by layer.

[0184] 3.1.3 Feature Vector Generation

[0185] In the last few layers of the CNN model, fully connected layers are usually used to combine the learned features;

[0186] The output of the fully connected layer is used as the feature vector of the medical imaging data, which contains spatial feature information related to postoperative recovery of brain trauma.

[0187] 3.2 Physiological parameter data feature extraction

[0188] 3.2.1 Recurrent Neural Network (RNN) Model Selection

[0189] Choose RNN models suitable for time series data processing, especially long short-term memory networks (LSTMs);

[0190] Adjust the model's parameters such as the number of hidden layers and the number of neurons according to the characteristics of the physiological parameter data and task requirements.

[0191] 3.2.2 Temporal Feature Extraction

[0192] The preprocessed physiological parameter data is input into the LSTM model in time series;

[0193] The LSTM model captures long-term dependencies in time series data through its unique gating mechanism (input gate, forget gate, and output gate); the LSTM model can learn the patterns of changes in physiological parameter data over time and extract time series features related to postoperative recovery from brain trauma.

[0194] 3.2.3 Feature Vector Generation The output of the LSTM model is used as the feature vector of the physiological parameter data, which contains the temporal feature information related to postoperative recovery after brain trauma.

[0195] 3.3 Introducing the Attention Mechanism

[0196] 3.3.1 Attention Mechanism Selection

[0197] According to the characteristics of the specific task and dataset, choose the appropriate type of attention mechanism, including channel attention, spatial attention, etc.

[0198] 3.3.2 Weighted Processing of Key Information

[0199] Introducing the attention mechanism into CNN and LSTM models to perform weighted processing on the extracted features;

[0200] The attention mechanism can selectively focus on the most relevant parts of the input data, improving the accuracy of feature extraction;

[0201] Through weighted processing, features that are more relevant to recovery after brain trauma are given greater weights in the feature vector.

[0202] 3.4 Comprehensive feature vector generation

[0203] 3.4.1 Feature Fusion

[0204] Fusing the eigenvectors of medical imaging data and physiological parameter data;

[0205] The fusion can be performed by splicing, weighted summation, etc. to obtain a comprehensive feature vector.

[0206] 3.4.2 Feature Vector Optimization

[0207] Further optimization processing is performed on the comprehensive feature vector, including dimensionality reduction and denoising;

[0208] Ensure that the comprehensive feature vector contains key information related to postoperative recovery after brain trauma and has high accuracy and robustness.

[0209] Through these steps, the feature extraction module extracts key features from the preprocessed medical imaging data and physiological parameter data, generating a comprehensive feature vector. This feature vector will be used for subsequent model training and prediction tasks, providing strong data support for research related to postoperative recovery after brain trauma.

[0210] 4. Deep Learning Model

[0211] The deep learning model uses an LSTM network as its core architecture, trains the comprehensive feature vector, and establishes a long short-term memory network (LSTM) model for recovery assessment. The input of the LSTM network is the comprehensive feature vector output by the feature extraction module, and the output is the predicted value of the degree of recovery. To optimize the model performance, the cross entropy loss function is used as the objective function, the formula is:

[0212]

[0213] Among them, N is the number of samples, yi is the true label of sample i (classification label of recovery degree), is the predicted label of sample i (calculated by the softmax function).

[0214] In order to prevent overfitting, L2 regularization and Dropout technology are introduced during the model training process.

[0215] The specific implementation process is:

[0216] 4.1 Data Preparation

[0217] Deep learning models require large data sets for training. During the data preparation stage, comprehensive feature vectors related to the degree of recovery and their corresponding recovery degree classification labels should be collected; the data should be preprocessed, including missing value processing, standardization or normalization, to ensure the stability and efficiency of model training.

[0218] 4.2 Model Construction

[0219] 4.2.1 Select a deep learning framework: Use a deep learning framework such as PyTorch or TensorFlow for modeling.

[0220] 4.2.2Build LSTM network:

[0221] Input layer: receives the comprehensive feature vector output by the feature extraction module;

[0222] LSTM layer: As the core architecture of the model, it captures long-term dependencies in sequential data;

[0223] Fully connected layer: converts the output of the LSTM layer into a predicted value of the degree of recovery;

[0224] Output layer: Use the softmax function to convert the predicted value into a probability distribution for comparison with the true label.

[0225] 4.2.3 Introducing Regularization and Dropout:

[0226] L2 regularization: Add the sum of squares of weight parameters to the loss function to prevent overfitting of the model;

[0227] Dropout technology: Randomly discard some neurons during training to improve the generalization ability of the model.

[0228] 4.3 Model Compilation

[0229] Specify the loss function, optimizer, and evaluation metric. In this example:

[0230] Loss function: The cross entropy loss function is used to measure the difference between the model's predicted probability distribution and the true distribution;

[0231] Optimizer: Select an optimizer such as Adam to update the model weights according to the gradient of the loss function;

[0232] Evaluation metrics: Select metrics such as accuracy, precision, and recall to evaluate the performance of the model on the test dataset.

[0233] 4.4 Model Training

[0234] The preprocessed dataset is divided into a training set and a validation set (or test set). The model is trained using the training set, and the model performance is monitored using the validation set. The training process involves batch processing and multiple rounds of iterations. During the training process, changes in indicators such as the loss function value and accuracy should be recorded to facilitate model tuning.

[0235] 4.5 Model Evaluation and Optimization

[0236] Use the test dataset to evaluate the trained model and calculate indicators such as accuracy, precision, and recall. Optimize the model based on the evaluation results, including adjusting the number of units in the LSTM layer, the learning rate of the optimizer, the strength of the regularization term, etc. The optimized model should be evaluated again to ensure performance improvement.

[0237] 4.6 Model Prediction and Application

[0238] The optimized model is applied to the prediction of actual data, and new data is input into the model to obtain the predicted value of the recovery degree. The prediction results can be used to guide actual decision-making or further analysis.

[0239] In summary, the implementation process of the deep learning model based on the LSTM network includes steps such as data preparation, model construction, model compilation, model training, model evaluation and optimization, and model prediction and application. Through a reasonable implementation process, an effective recovery assessment model can be established to provide strong support for practical applications.

[0240] 5. Evaluation result output module

[0241] The evaluation result output module inputs the data of the patient to be evaluated into a trained long short-term memory network model (LSTM model) and outputs the recovery evaluation results; the recovery evaluation results include the degree of recovery (probability distribution calculated by the softmax function) and the recovery speed (calculated based on the changing trend of the recovery degree). In order to intuitively display the evaluation results, the evaluation result output module also provides visualization tools, including recovery curve graphs, heat maps, etc.

[0242] The specific implementation process is:

[0243] 5.1 Data Preparation and Input

[0244] 5.1.1 Data Collection: Collect relevant data of the patients to be evaluated, including medical imaging data, physiological parameter data, and clinical manifestation data;

[0245] 5.1.2 Data preprocessing: The collected data is input into the data preprocessing module for denoising, cleaning, and normalization, and a comprehensive feature vector is generated;

[0246] Data input: The preprocessed comprehensive feature vector is input into the trained LSTM model to prepare for recovery evaluation.

[0247] 5.2 Recovery Assessment

[0248] 5.2.1 Model prediction: Use the LSTM model to predict the input comprehensive feature vector and obtain the probability distribution of the recovery degree.

[0249] 5.2.2 Probability distribution analysis: The probability distribution calculated by the softmax function is used to determine the specific category of the recovery degree, and the category with the highest probability is selected as the prediction result.

[0250] 5.2.3 Result output: The predicted results of the degree of recovery shall be output in text form, including the specific degree of recovery category and the corresponding probability value.

[0251] 5.3 Recovery Speed Assessment

[0252] 5.3.1 Historical data acquisition: For patients with multiple assessment records, obtain their historical recovery data.

[0253] 5.3.2 Calculation of change trend: Based on historical recovery degree data, calculate the change trend of recovery degree to evaluate the recovery speed.

[0254] 5.3.3 Speed evaluation: Based on the change trend, the recovery speed is divided into different levels (including fast, medium, and slow), and the corresponding evaluation results are output.

[0255] 5.4 Application of visualization tools

[0256] 5.4.1 Recovery curve generation: For patients with multiple assessment records, a curve chart showing the recovery degree over time is generated to intuitively display the recovery process.

[0257] 5.4.2 Heat map generation: Based on the probability distribution of recovery degree, a heat map is generated to show the probability of different recovery degree categories.

[0258] 5.4.3 Visualization result output: The generated visualization results such as recovery curves and heat maps are output in the form of images for reference by doctors and patients.

[0259] 5.5 Results Integration and Report Generation

[0260] 5.5.1 Result integration: Integrate the recovery degree assessment results, recovery speed assessment results, and the results generated by the visualization tool.

[0261] 5.5.2 Report Generation: Generate a detailed recovery assessment report based on the integrated results. The report should include the patient's basic information, assessment time, recovery level, recovery speed, and visualization results.

[0262] 5.5.3 Report output: The generated recovery assessment report shall be output in the form of electronic or paper documents for doctors and patients to review.

[0263] Through the above steps, the evaluation result output module can effectively process the data of the patient to be evaluated, and output accurate recovery evaluation results and results generated by the visualization tool, providing strong decision support for doctors and patients.

[0264] 6. User Interface Module

[0265] The user interface module provides a user-friendly interface to facilitate doctors to input patient data, view assessment results, and develop personalized rehabilitation plans based on the assessment results; the user interface module supports data import and export functions, making it convenient for doctors to exchange and share data with other medical systems.

[0266] The specific implementation process is:

[0267] 6.1 Requirements Analysis and Design

[0268] 6.1.1 User Needs Research

[0269] Conduct in-depth discussions with target users (doctors) to understand their specific needs in daily work, such as data input, reviewing assessment results, and developing rehabilitation plans;

[0270] Analyze the user interfaces of competing or similar software, summarize their strengths and weaknesses, and provide reference for design.

[0271] 6.1.2 Functional Design

[0272] Based on the results of the user needs survey, determine the main functions of the user interface module, including data input, assessment result viewing, rehabilitation plan formulation, data import and export, etc.;

[0273] Design the overall layout and operation flow of the user interface to ensure that the interface is simple, intuitive and easy to use.

[0274] 6.1.3 Prototype production and review

[0275] Use design tools (including Sketch, Adobe XD, etc.) to create user interface prototypes, including the layout of each page, buttons, icons and other elements;

[0276] Organize a review meeting and invite user representatives, development team and other relevant personnel to review the prototype, collect feedback and make modifications.

[0277] 6.2 Development and Implementation

[0278] 6.2.1 Front-end Technology Selection

[0279] According to the requirements and design of the user interface, choose appropriate front-end technology frameworks (React, Vue, etc.) and libraries (AntDesign, Element UI, etc.) for development.

[0280] 6.2.2 Interface Development

[0281] According to the design prototype, use the front-end technology framework and library to develop the interface to realize functions such as data input, evaluation result viewing, and rehabilitation plan formulation;

[0282] During the development process, we focus on user experience to ensure that the interface responds quickly and operates smoothly.

[0283] 6.2.3 Data import and export function development

[0284] Implement data import function, support common data file formats (Excel, CSV, etc.), and facilitate doctors to import data from other medical systems;

[0285] It realizes the data export function and supports exporting assessment results, rehabilitation plans and other data into data files or report formats, making it convenient for doctors to exchange and share data with other medical systems.

[0286] 6.2.4 Connecting with the back-end interface

[0287] Develop interface docking functions with back-end services to implement operations such as storage, reading, and updating of user data;

[0288] Ensure the consistency of data format and interaction mode between front-end and back-end interfaces to improve system stability and reliability.

[0289] 6.3 Testing and Optimization

[0290] 6.3.1 Functional Testing

[0291] Test various functions of the user interface module, including data input, assessment result viewing, rehabilitation plan formulation, data import and export, etc., to ensure that the functions are normal and available.

[0292] 6.3.2 User Experience Testing

[0293] Invite user representatives to conduct user experience testing, collect feedback and evaluate the usability and satisfaction of the user interface;

[0294] Based on the test results, the user interface is optimized and improved to enhance the user experience.

[0295] 6.3.3 Performance Optimization

[0296] Test and optimize the performance of user interface modules, including optimization of loading speed, response time, etc.;

[0297] Ensure that the user interface module can run normally on different devices and browsers to improve the compatibility and stability of the system.

[0298] 6.4 Deployment and Launch

[0299] 6.4.1 Deployment Preparation

[0300] Prepare user interface modules for packaging and deployment, including code optimization and resource file organization;

[0301] Ensure that the deployment environment meets system requirements and prepare relevant deployment documents and scripts.

[0302] 6.4.2 System Deployment

[0303] Deploy the user interface module to the server or cloud platform and perform related configuration and testing;

[0304] Ensure that the user interface module can connect and interact with the backend service normally and can correctly process user requests and data.

[0305] 6.4.3 Online Operation

[0306] Conduct final inspection and testing of the user interface module before going online to ensure the system is stable and reliable;

[0307] Develop operational plans and promotion strategies to attract users and collect user feedback for continuous improvement and optimization.

[0308] By implementing the above steps, a user-friendly user interface module can be developed to facilitate doctors to enter patient data, view assessment results, and develop personalized rehabilitation plans based on the assessment results. The module also supports data import and export, making it easier for doctors to exchange and share data with other medical systems.

[0309] Application examples:

[0310] Artificial intelligence-based postoperative recovery assessment system for brain trauma

[0311] 1. System Background and Objectives

[0312] Background: Traumatic brain injury is a common neurological injury. Patients' recovery after surgery varies from person to person, and accurate assessment is needed to guide rehabilitation plans.

[0313] Objective: To develop an artificial intelligence-based postoperative recovery assessment system for brain trauma, which can provide doctors with objective assessment results and personalized rehabilitation plan recommendations by collecting and analyzing patient data.

[0314] 2. System Architecture and Modules

[0315] System architecture: The system includes data input module, feature extraction module, LSTM model evaluation module, evaluation result output module and user interface module.

[0316] Module functions:

[0317] Data input module: collects patients’ basic information, medical imaging data and physiological parameter data;

[0318] Feature extraction module: extracts key features from medical imaging data, such as the connection strength of brain functional networks and the degree of brain tissue damage, and extracts physiological features from physiological parameter data;

[0319] LSTM model evaluation module: uses the trained LSTM model to evaluate the recovery degree and recovery speed of the extracted features;

[0320] Assessment result output module: generates a recovery assessment report, including the degree of recovery, recovery speed and a recovery curve displayed by a visualization tool; user interface module: provides a friendly interface for doctors to input data, view assessment results and rehabilitation plan recommendations.

[0321] 3. Data Collection and Processing

[0322] Data Collection:

[0323] Patient basic information: age, gender, type of brain injury, operation time, postoperative recovery time, etc.;

[0324] Medical imaging data: CT, MRI, etc.

[0325] Physiological parameter data: heart rate, blood pressure, blood oxygen saturation, etc.;

[0326] Data preprocessing:

[0327] Perform denoising and enhancement on medical imaging data to improve the accuracy of feature extraction;

[0328] The physiological parameter data were normalized to eliminate the dimensional differences between different parameters.

[0329] IV. Feature Extraction and Model Training

[0330] Feature extraction:

[0331] Use advanced image processing technology to extract key features from medical imaging data;

[0332] Extracting recovery-related physiological features from physiological parameter data;

[0333] Model training:

[0334] Collect historical patient recovery data and corresponding characteristic data;

[0335] Use this data to train the LSTM model and adjust the model parameters to improve prediction accuracy;

[0336] 5. Evaluation Process and Result Output

[0337] Evaluation Process:

[0338] The doctor inputs the data of the patient to be evaluated through the user interface module;

[0339] The system preprocesses and extracts features from the data;

[0340] The extracted features are input into the trained LSTM model for evaluation;

[0341] The model outputs the probability distribution of recovery degree and recovery speed;

[0342] Result output:

[0343] Recovery level: Displays the probability of a patient recovering to different health states in the form of a probability distribution;

[0344] Recovery speed: Based on the changing trend of the recovery degree, the recovery speed is divided into three levels: fast, medium, and slow;

[0345] Visualization tools: Provide recovery curve charts, heat maps, etc. to intuitively display the patient's recovery process and the possibility of different recovery states.

[0346] Specific application examples

[0347] Patient Information:

[0348] Name: Zhang San;

[0349] Age: 35 years old

[0350] Gender: Male;

[0351] Type of brain injury: traffic accident;

[0352] Surgery date: May 1, 2023;

[0353] Postoperative recovery time: 1 month;

[0354] Data input:

[0355] Medical imaging data: CT scans showed slight damage to brain tissue;

[0356] Physiological parameter data: normal heart rate, slightly high blood pressure, normal blood oxygen saturation.

[0357] Feature extraction:

[0358] Extract the connectivity strength of brain functional networks and the degree of brain tissue damage from CT scans;

[0359] The characteristic values of heart rate, blood pressure and blood oxygen saturation are extracted from the physiological parameter data.

[0360] Evaluation results:

[0361] Recovery level: 70% probability of full health, 25% probability of mild disability, 5% probability of severe disability;

[0362] Recovery speed: Medium.

[0363] Visual display:

[0364] The recovery curve shows that Zhang San's recovery level is gradually increasing;

[0365] The heat map shows that Zhang San has the highest probability of returning to full health.

[0366] Rehabilitation program recommendations:

[0367] Based on the evaluation results, the doctor recommended that Zhang San continue with rehabilitation training, focusing on strengthening the recovery of brain function;

[0368] It is recommended that Zhang San undergo regular checkups to monitor his recovery.

[0369] Summarize

[0370] This system collects and analyzes data from patients after brain trauma surgery, providing doctors with objective assessment results and personalized rehabilitation plan recommendations.

[0371] The present invention and its embodiments are described above. This description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if a person skilled in the art is inspired by this and, without departing from the purpose of the present invention, designs structures and embodiments similar to this technical solution without inventiveness, they shall fall within the scope of protection of the present invention.

Claims

1. An artificial intelligence-based postoperative brain injury recovery assessment system, characterized in that: include: a) Data acquisition module, used to collect the patient's medical imaging data, physiological parameters and clinical performance data; b) a data preprocessing module, which performs denoising, cleaning, and normalization on the collected data to obtain preprocessed data; c) a feature extraction module that uses a convolutional neural network to extract spatial features from the medical imaging data in the preprocessed data, uses a recurrent neural network to extract temporal features from the physiological parameter time series data in the preprocessed data, and combines the clinical manifestation data in the preprocessed data to form a comprehensive feature vector; d) Deep learning model, using long short-term memory network as the core architecture, training the comprehensive feature vector and building a long short-term memory network model for recovery assessment; e) an assessment result output module, which inputs the data of the patient to be assessed from step a) into the trained long short-term memory network model and outputs the recovery assessment results, including the degree of recovery and the speed of recovery; f) User interface module, which provides a user-friendly interface for doctors to input patient data, view assessment results, and develop personalized rehabilitation plans based on the assessment results.

2. The artificial intelligence-based postoperative brain injury recovery assessment system according to claim 1, characterized in that: In the deep learning model, the loss function of the long short-term memory network model adopts the cross entropy loss function, and the formula is: Among them, N is the number of samples, yi is the true label of sample i, is the predicted label of sample i.

3. The artificial intelligence-based postoperative brain injury recovery assessment system according to claim 1, characterized in that: In the evaluation result output module, the predicted value of the recovery degree is calculated by the softmax function, and the formula is: Among them, z i is the original output value of the i-th node in the output layer of the long short-term memory network model, and C is the total number of nodes in the output layer, that is, the number of classifications of the recovery degree.

4. The artificial intelligence-based postoperative brain trauma recovery assessment system according to claim 1, characterized in that: The data acquisition module also includes a data synchronization mechanism, which specifically includes: Timestamp synchronization: Accurately timestamp medical imaging data, physiological parameters, and clinical performance data to ensure that all data are collected at the same or similar time points; Data format conversion: converting data from different sources into a unified format for subsequent processing and analysis; Abnormal data detection: Real-time monitoring of abnormal or missing values during data collection, and prompt reminders to operators for processing.

5. The artificial intelligence-based postoperative brain trauma recovery assessment system according to claim 1, characterized in that: The data preprocessing module further includes a data enhancement step, which specifically includes: Medical image enhancement: rotate, scale, translate, flip, add noise, and blur medical image data; Physiological parameter interpolation: For missing physiological parameter data, linear interpolation and spline interpolation methods are used to complete them; Normalization processing: convert physiological parameter data into standard normal distribution.

6. The artificial intelligence-based postoperative brain trauma recovery assessment system according to claim 1, characterized in that: The feature extraction module also includes an attention mechanism, which specifically includes: Spatial attention: weighting key regions in medical imaging data to highlight features related to postoperative recovery after traumatic brain injury; Temporal attention: weighting the key periods in the physiological parameter time series data to capture the dynamic changes closely related to the recovery process; Multimodal fusion: Medical imaging data, physiological parameters, and clinical manifestation data are integrated through the attention mechanism to form a comprehensive feature vector that comprehensively reflects the patient's recovery status.

7. The artificial intelligence-based postoperative brain trauma recovery assessment system according to claim 1, characterized in that: The deep learning model also includes a regularization term, which specifically includes: L2 regularization: imposes a penalty term on the model weights to prevent overfitting caused by excessive weights; Dropout: Randomly discard some neurons during model training to reduce the co-adaptability between neurons; Early stopping method: Stop training when the performance of the validation set no longer improves to avoid overfitting.

8. The artificial intelligence-based postoperative brain injury recovery assessment system according to claim 1, characterized in that: The evaluation result output module also includes a visualization tool, which specifically includes: Recovery curve chart: With time as the horizontal axis and recovery degree as the vertical axis, the patient's recovery curve is drawn to intuitively show the recovery process; Heatmap: Visualize medical imaging data to highlight key areas related to recovery; Interactive reports: Generate interactive reports containing basic patient information, assessment results, and visual charts for easy viewing and analysis by doctors.

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