49 results about "Chest radiograph" patented technology

The invention discloses an X-ray chest radiography report automatic generation method, system and device based on factual description enhancement and a medium. The method comprises the following steps: firstly, constructing a medical entity extraction method based on a RadGraph model, and carrying out identification and structured extraction on clinical keywords to obtain factual description consisting of key medical entities; secondly, establishing a comparative learning method guided by factual description, enhancing semantic consistency between the image and the text from global and local levels, and extracting visual features with diagnostic value; establishing a historical similar case retrieval strategy independent of disease tags, and calculating visual semantic similarity to realize automatic retrieval of historical cases; and finally, proposing an evidence-driven chest radiography report generation method, constructing a cross-modal fusion network, and generating a chest radiography report with clinical accuracy and consistency. The system, the equipment and the medium automatically generate an X-ray chest radiography report based on factual description enhancement based on the method; according to the method, efficient and stable automatic retrieval is realized, the clinical accuracy of the generated chest radiograph report and the reliability of evaluation are improved, and the universality and robustness of the model are remarkably improved.

The invention relates to a chest pain classification method and system based on multi-modal data fusion and a deep learning model, and belongs to the technical field of intelligent medical auxiliary diagnosis. The method comprises the following steps: firstly, carrying out heart region segmentation and pathological feature enhancement preprocessing on a chest radiograph image, extracting anatomical features by adopting a multi-scale visual model which is optimized and improved aiming at the chest radiograph, meanwhile, carrying out multi-lead time sequence calibration and ST-segment waveform marking on an electrocardiogram signal, and capturing electrophysiological time sequence features through a multi-scale one-dimensional convolutional network; then, a dynamic gating hybrid expert system is constructed, an anatomy expert, an electrophysiology expert, a multi-modal association expert and a critical value expert process specific modal features respectively, and a gating network dynamically calculates expert weights based on real-time vital signs and pathological features; according to the method, by integrating multi-modal data and combining a deep learning technology, rapid and accurate chest pain classification support and report generation can be provided for clinicians, and the method has a wide application prospect.

The invention discloses a chest radiograph report generation and focus positioning method and system based on reinforcement learning, and belongs to the technical field of artificial intelligence and medical image analysis crossing. According to the method, firstly, a basic model is supervised and fine-tuned by using a multi-source chest radiograph image and a query text; and then optimizing the strategy model by adopting a GRPO reinforcement learning framework, introducing an indicator function guided by token entropy into a target function, and finally realizing parallel generation of an end-to-end chest radiograph report text and focus bounding box coordinates by the optimized strategy model. According to the method, visual positioning of the focus is realized while the generation quality of the chest radiograph report is improved, and the integrated output form of the method better fits the clinical actual working process.

The invention discloses a multi-label classification method based on key region selection and cross-layer feature fusion. Comprising the following steps: carrying out standard preprocessing operation on a chest radiograph image, and carrying out multi-label coding processing on an image label; inputting the preprocessed image into an OfficientNet-B0 network to extract features, performing dimensionality reduction to obtain an original feature sequence, and splicing the original feature sequence with learnable category embedding and position codes to form an embedding sequence; inputting the embedded sequence into a Transform encoder, and extracting deep features of the image through a multi-head attention mechanism and a multi-layer perceptron; constructing a key area selection module, selecting a discriminative area by using an attention mechanism of an encoder and filtering irrelevant information; constructing a cross-layer feature fusion module, fusing features of different levels in the encoder, and inputting the features into a classifier to obtain a multi-label classification result; and designing adaptive class balance loss, dynamically adjusting the attention on different classes of positive and negative samples, and optimizing model parameters through back propagation. The method is an optimization method for effectively improving image multi-label classification performance.

The invention discloses a chest radiograph report generation method based on thinking chain reasoning and active learning. The method comprises the following steps: collecting a multi-view chest radiograph image and a corresponding report; performing view angle identification and view angle classification on the multi-view-angle chest radiograph image; a shared visual encoder is adopted to extract sequence features of all visual angles, and a multi-visual-angle visual token is generated through cross-visual-angle feature fusion; constructing a systematic hierarchical diagnosis process, sequentially executing structured five-stage chain reasoning for each anatomical structure in the multi-view chest radiography image, and generating a structured reasoning prompt; inputting the multi-view visual token and the structured reasoning prompt into a large language model, and generating a target sequence through an autoregression mode; constructing a scoring function based on joint scoring of uncertainty and inconsistency; labeling the samples in the unlabeled sample pool according to the sample scores in each round of iteration; and inputting a to-be-tested multi-view chest radiograph image into the trained language model, and generating a thinking chain and a complete report text.

The invention discloses a pneumoconiosis staged diagnosis system oriented to a class imbalance X-ray chest radiograph data set. The pneumoconiosis staged diagnosis system comprises a preprocessing module, a user-defined deformable separable convolution module, a feature fusion module, a residual block and a convolution block attention module. The size and the shape of a receptive field of a pneumoconiosis chest radiograph image are adaptively adjusted by using self-defined deformable separable convolution, target features can be more accurately captured for images with variable target shapes and complex backgrounds, and a focus area is displayed by using the preprocessed pneumoconiosis image, so that the accuracy of the pneumoconiosis chest radiograph image is improved. The method comprises the following steps: firstly extracting an image feature, then performing feature fusion on the extracted image feature and a manifested focus area, then inputting the fused feature representation into a residual block, introducing shortcut connection to relieve a gradient disappearance problem, and finally helping a model to process information more intelligently and focus on key content through a convolution block attention module, thereby performing staged diagnosis on pneumoconiosis. According to the method, efficient and rapid detection can be achieved within a short processing time, high accuracy is kept, and the method can be used for stage diagnosis of pneumoconiosis.

The invention discloses an intelligent assessment method and system for coronary stenosis based on chest radiography, and relates to the technical field of health risk assessment, and the method comprises the steps: obtaining chest radiography image data of a target patient; based on the time sequence fluctuation of the calcification focus area in the chest radiograph image data, calculating to obtain a calcification activity index, determining a cardiac pulsation key point in the chest radiograph image data, and based on the displacement change of the cardiac pulsation key point between each image frame, calculating to obtain a collateral circulation compensation index of the calcification focus area; based on the collateral circulation compensation index and the calcification activity index, calculating a dynamic risk factor corresponding to the coronary artery stenosis; and inputting the dynamic risk factor into a preset neural network model for iterative training, and performing prediction processing on the chest radiograph image data based on the trained preset neural network model to obtain a coronary artery stenosis risk assessment result. The technical effect of reducing the clinical misjudgment risk of coronary artery stenosis is achieved.

The invention provides a chest radiograph image recognition method and device, and relates to the technical field of image processing. The method comprises the following steps: acquiring a chest radiograph image to be recognized; inputting the chest radiograph image into a pre-trained identification model to obtain an identification probability of each preset disease tag corresponding to the chest radiograph image; the recognition model is used for performing recursive multi-scale feature division and spatial reconstruction on the chest radiograph image, and performing interactive updating between a preset disease tag and features in the feature division process of each scale level to obtain tag semantics with global perception; and determining the recognition probability of each preset disease tag corresponding to the chest radiograph image based on the tag semantics with global perception. According to the invention, the recognition precision of complex focuses with variable scales and different shapes in the chest radiograph can be improved.

The present invention discloses a method and device for detecting DR thoracic rib images, comprising the following steps: data preprocessing, lung field region segmentation, obtaining rib gradient images, detecting rib lower boundary lines, and detecting rib upper boundary curves. The original DR image is downsampled to a reasonable size and preprocessing is completed; next, the left and right lung field regions in the image are segmented; then, the upper and lower edges of the ribs are detected respectively within the lung field regions, thereby achieving rib contour detection. The method adopted by the present invention can effectively identify the ribs in the chest cavity. This invention is an important basis for realizing rib extraction and rib suppression in chest radiographs.

The present invention discloses a method for suppressing rib images in X-ray chest films based on an attention-based generative adversarial network, which specifically includes the following steps: Step 1, preprocess the training set and test set images in the dataset; Step 2, construct an X-ray chest film rib image suppression network model based on an attention-based generative adversarial network; Step 3, use the preprocessed training set data in Step 1 to train the model constructed in Step 2 to obtain a trained X-ray chest film rib image suppression network model based on an attention-based generative adversarial network; Step 4, put the preprocessed test set images in Step 1 into the model trained in Step 3 to finally obtain soft tissue images after bone suppression. The present invention overcomes the problems of texture detail loss, blurred generated images, or incomplete rib suppression caused by directly generating bone suppression images in existing methods, and further obtains bone suppression images that are clear and do not change the detail information in non-rib regions.

The invention belongs to the technical field of image processing, and provides a chest radiography lesion detection method based on enhanced feature extraction and fusion, comprising: inputting a chest radiography into a target detection model to obtain a lesion detection result; marking a lesion detection result in the chest radiograph; the target detection model comprises a backbone network which is used for extracting N-1 feature maps of different scales from a chest radiograph, wherein the N-1 feature maps comprise a reference feature map and a non-reference feature map; the neck network is used for carrying out size transformation on the non-reference feature map; obtaining a spliced feature map, fusing the spliced feature map to obtain a fused feature map, and distributing the fused feature map to obtain a hierarchical distribution feature map; fusing the fusion features of the preset distribution hierarchy and the feature map of the hierarchy N to obtain a plurality of detection features; the head output layer is used for obtaining a lesion detection result based on a plurality of detection features; the method reduces information loss, can obtain more detail features, captures the overall structure information of the chest radiograph, can adapt to targets of different sizes, and improves the detection precision of lesion targets.

The invention relates to an X-ray chest radiography spine offset detection method and system based on a prior probability graph, and the method comprises the steps: obtaining a to-be-detected X-ray chest radiography image, and carrying out the normalization of the transverse pixels of the image; obtaining a corresponding prior probability value through linear interpolation in pre-constructed one-dimensional prior probability distribution based on the normalized position of each column of transverse pixels of the image, constructing a one-dimensional array, copying the one-dimensional array in the vertical direction, and constructing a two-dimensional prior probability graph; inputting an X-ray chest radiograph image to be detected into the GPU accelerated segmentation network to obtain a spine binary mask; and based on the two-dimensional prior probability graph, calculating an average prior probability and an offset probability in the mask area of the spine binary mask, comparing the average prior probability and the offset probability with a preset offset threshold value, if the offset probability is greater than the offset threshold value, determining that the spine is offset, otherwise, determining that the spine is basically centered. Compared with the prior art, the method has the advantages that the offset detection result has better interpretability, stability and quantificaiton.

A Wasserstein distance and difference metric-combined chest radiograph anomaly identification domain adaptation method and a corresponding system are provided. The method includes the following steps: step 1, data preparation and data pre-processing for chest radiographs; step 2, multi-scale feature extraction based on a swin transformer network; step 3, loss minimization based on a Wasserstein distance and a contrastive domain discrepancy; and step 4, using the model to perform chest radiograph prediction after verifying the model. The method selects source domain samples closest to target domain samples, narrows a distance of the same class between the target domain samples and the source domain samples in feature space, and expands a distance between different classes. Meanwhile, a classification prediction task for the chest radiographs is performed by using the multi-scale features, improving a receptive field and capturing more information conducive to the classification prediction task.

The present invention belongs to the field of image processing technology and provides a chest X-ray lesion detection method based on enhanced feature extraction and fusion, comprising: inputting a chest X-ray into a target detection model to obtain a lesion detection result; marking the lesion detection result in the chest X-ray; the target detection model comprises: a backbone network: used to extract N-1 feature maps of different scales from the chest X-ray, including a reference feature map and a non-reference feature map; a neck network: performing size transformation on the non-reference feature map; obtaining a spliced ​​feature map, fusing the spliced ​​feature map to obtain a fused feature map and distributing it to obtain a hierarchical distribution feature map; fusing the fused features of a preset distribution level and the feature map of level N to obtain multiple detection features; a head output layer: obtaining a lesion detection result based on the multiple detection features; the present invention reduces information loss, can obtain more detailed features, and at the same time captures the overall structural information of the chest X-ray, can adapt to targets of different sizes, and improves the accuracy of lesion target detection.

Methods are provided to predict, based on chest radiographs, whether a patient suffers from any comorbidities in one or more high-level comorbidity classes, e.g., high-level hierarchical condition categories. The chest radiograph is a low-cost, minimally-invasive source of visual information that is able to accurately predict whether a subject suffers from diabetes with chronic complications, morbid obesity, congestive heart failure, specified heart arrhythmias, vascular disease, or chronic obstructive pulmonary disease, among other high-level comorbidity classes. The predictive methods provided herein are also able to accurately predict overall measures of health-related complications, including the risk adjustment factor. These predictive methods can be used to focus review of medical records, improving the detection of disease. Outputs generated by these methods can also be used to predict the severity of disease and / or the extent of care provided to subjects with COVID-19 or related diseases.

The invention provides a chest radiography multi-label classification method and system based on a cross-modal memory network, and the method comprises the steps: obtaining chest radiography data, and carrying out the preprocessing of the chest radiography data; the chest radiograph data comprises an original chest radiograph image and corresponding label information; escaping the label information to map the label information to a uniquely corresponding text identifier; training a chest radiograph classification model established based on a cross-modal memory network (CMN) by using the preprocessed chest radiograph image and the transferred label information; and realizing a multi-label classification task of the chest radiograph by utilizing the chest radiograph classification model. According to the chest radiograph multi-label classification method and system, key features in chest radiograph images can be effectively captured and fused, effective alignment and fusion between medical terms and labels are achieved at the same time, and therefore the accuracy and interpretability of a classification model are improved.

The application discloses a kind of based on chest radiograph coronary artery stenosis intelligent evaluation method and system, it is related to health risk assessment technical field, the method includes: obtaining the chest radiograph image data of target patient;Based on the time sequence fluctuation of calcification focus area in chest radiograph image data, the calcification activity index is calculated, determine the heart beat key point in chest radiograph image data, based on the displacement change between each image frame of heart beat key point, the collateral circulation compensation index of calcification focus area is calculated;Based on collateral circulation compensation index and calcification activity index, the dynamic risk factor corresponding to coronary stenosis is calculated;Dynamic risk factor is input to the preset neural network model and is iteratively trained, and based on the preset neural network model after training is completed to chest radiograph image data is predicted to process, obtains coronary stenosis risk assessment result.The application reaches the technical effect of reducing the clinical misjudgment risk of coronary artery stenosis.

The invention provides a chest radiograph abnormal region detection method and a program product, and the method comprises the steps: adding an FAHG module on the basis of YOLOv11, and carrying out the frequency domain enhancement and noise suppression of a feature map outputted to a detection head; in the FAHG module, Fourier transform is firstly carried out on the feature map; then low-pass, band-pass and high-pass filtering is executed respectively, and low-frequency, intermediate-frequency and high-frequency sub-band components are obtained through decomposition; enhancement processing is carried out on the three sub-band components, cross-channel information interaction and feature expression are enhanced for the low-frequency sub-band components and the intermediate-frequency sub-band components, and noise is suppressed for the high-frequency sub-band components; and after the enhanced low-frequency sub-band component, the enhanced intermediate-frequency sub-band component and the enhanced high-frequency sub-band component are respectively subjected to inverse Fourier transform, weighted fusion is carried out. Compared with other existing YOLO series models, the method for detecting the abnormal area of the chest radiograph image has high detection precision and generalization ability, the detection precision is remarkably improved under the condition that the calculated amount is not obviously increased, and good balance is achieved between efficiency and precision.

The invention discloses a tuberculosis auxiliary diagnosis system fusing chest radiography and breath sound characteristics. The tuberculosis auxiliary diagnosis system comprises a breath sound acquisition module, a breath sound dynamic analysis module, a chest radiography acquisition module, a chest radiography detection module, an activity discrimination fusion module and a result output module. Breathing sound signals are collected at a plurality of standard point positions on the body surface of a patient, dynamic abnormal indexes are extracted, meanwhile, chest radiography images are obtained, focus attribute information is recognized, an activity judgment fusion module conducts consistency analysis on the chest radiography images and the dynamic abnormal indexes, standardized risk scores are generated, and high-risk, low-risk or uncertain grading results are output. According to the method, the misdiagnosis and missed diagnosis risks can be effectively reduced under the complex conditions of old focus combined activities, early hidden cases, combined chronic obstructive pulmonary disease and the like, and a visual diagnosis report containing a chest radiograph thermodynamic diagram and a breath sound frequency spectrum is provided. Compared with single chest radiography or single breath sound analysis, the sensitivity and specificity of active tuberculosis diagnosis are improved, and the method has high clinical application value.

The application discloses a chest radiograph rib extraction method based on an adversarial generation network, and comprises the following steps: obtaining a chest radiograph image pair through dual-energy subtraction to construct a data set, wherein the chest radiograph image pair comprises one frame of standard chest radiograph I C , one frame of soft tissue image I S and one frame of bone image I B ; performing gray histogram equalization processing on the standard chest radiograph I C , the soft tissue image I S and the bone image I B respectively to obtain I CB and I CS ; using pairs of I C , I CB and I CS from the same individual to construct a rib generation data set, constructing rib boundary supervision data sets according to I C , I B and I CS of each individual in the data set according to a construction rule; inputting the rib boundary supervision data set into a rib supervision network, calculating the similarity loss between three feature maps of the image pair, inputting the constructed gray image into the rib supervision network for training, and completing the training of the rib supervision network; constructing a rib image generation network and training the rib image generation network; and inputting the collected chest radiograph image into the trained rib image generation network to obtain the rib image in the chest radiograph image.

This invention proposes an automated diagnostic report generation method based on pixel-level localization evidence from chest X-rays, belonging to the fields of medical image processing and artificial intelligence. By constructing a neural network comprising multimodal feature extraction, a large language model, image segmentation, and collaborative output modules, this invention achieves pixel-level localization evidence for lesion text, enabling reliable annotation of lesion descriptions in the diagnostic report within the image. During the training phase, visual and text encoders extract image and text features respectively, the large language model generates a structured report, the image segmentation module generates pixel-level masks using language embedding as cues, and the collaborative output module establishes a correspondence between the report and the mask through special markers, achieving localizable joint prediction. This invention requires no manual prompts, supports fully automated analysis, and possesses good interpretability and clinical deployment adaptability, making it suitable for intelligent assisted diagnostic scenarios involving medical images such as chest X-rays.

The application discloses a chest radiograph report generation and lesion positioning method and system based on reinforcement learning, and belongs to the technical field of cross between artificial intelligence and medical image analysis. The method first uses a plurality of source chest radiograph images and query texts to supervise and fine-tune a basic model; then a GRPO reinforcement learning framework is used to optimize a strategy model, and an indicator function guided by token entropy is introduced into a target function, and finally, the optimized strategy model is used to realize end-to-end parallel generation of chest radiograph report texts and lesion boundary box coordinates. The method improves the quality of chest radiograph report generation while realizing the visualization and positioning of lesions, and the integrated output form is more in line with the actual clinical workflow.

The invention discloses a child chest radiograph evaluation method, device and equipment based on deep learning and a medium, and the method comprises the steps: dividing a pre-collected child chest X-ray image into a training set, an internal verification set and an external verification set, and constructing a U-Net segmentation model, the method comprises the steps that a training set is established, region segmentation is conducted on the training set through a U-Net segmentation model, a DenseNetECA model is established, a core architecture of the DenseNetECA model is composed of four ECABlock modules, dense connection and an efficient channel attention mechanism are integrated in the ECABlock modules, feature dimensionality reduction and space down-sampling are conducted between the two ECABlock modules through a Transformation module, and the two ECABlock modules are connected through a Decision module; and inputting the processed training set, the internal verification set and the external verification set into a DenseNetECA model to complete model training, inputting a to-be-detected X-ray image into a U-Net segmentation model to perform region segmentation, and inputting the segmented to-be-detected X-ray image into the DenseNetECA model to perform evaluation to obtain an image evaluation result. The accuracy of the chest image of the child can be improved.

The present invention relates to the field of medical image processing technology, and specifically to a cardiothoracic ratio calculation method, a method and device for determining the probability of cardiac atrioventricular enlargement, aiming to avoid missed diagnosis. The cardiothoracic ratio calculation method proposed in the present invention comprises: inputting an anteroposterior chest radiograph image into a trained cardiopulmonary segmentation model and a spinal segmentation model respectively to obtain cardiopulmonary segmentation results and spinal segmentation results; and removing noise from the cardiopulmonary segmentation results; obtaining the spinal centerline according to the spinal segmentation results; obtaining the maximum length of the line connecting the outermost segmentation points of the left and right lung fields on the same horizontal plane as the maximum transverse diameter of the thorax based on the cardiopulmonary segmentation results; calculating the farthest distance between the left and right lateral edges of the heart and the spinal centerline on the same horizontal plane based on the cardiopulmonary segmentation results, and calculating the sum of the farthest distance of the left edge and the farthest distance of the right edge as the maximum transverse diameter of the heart; and calculating the cardiothoracic ratio based on the maximum transverse diameter of the heart and the maximum transverse diameter of the thorax. The present invention reduces dependence on doctor's experience and improves accuracy.

The present application belongs to the technical field of deep learning and medical image processing, and specifically relates to a chest radiograph abnormality recognition domain adaptive method and system combining Wasserstein distance and difference measurement. The method comprises the following steps: S1, chest radiograph data preparation and preprocessing; S2, multi-scale feature extraction based on Swin Transformer; S3, loss minimization based on Wasserstein distance and contrast domain difference; S4, model verification for chest radiograph prediction. The present application has the characteristics that not only the source domain sample closest to the target domain sample can be selected, but also the distance between the same categories of samples in the target domain and the source domain can be shortened in the feature space, and the distance between different categories can be widened, and at the same time, the extracted multi-scale features are used for chest radiograph classification task, which can effectively improve the receptive field and capture more information beneficial to the chest radiograph classification task.

The invention discloses an image processing method and system, computer equipment and a storage medium, and relates to the technical field of image processing, and the method comprises the steps: obtaining a chest radiograph CXR image and a real CT image of a chest and a lung, carrying out the structure registration of the real CT image, and obtaining the real CT image with an aligned structure; the method comprises the following steps: performing preliminary 2D feature extraction on a chest radiograph CXR image, performing down-sampling, secondary feature extraction and feature refining processing on preliminary 2D features in multiple stages in sequence, performing dimension expansion on the refined features, performing 3D decoding, generating a 3D simulation CT image, training a multi-task interactive learning model through a real CT image and the 3D simulation CT image which are structurally aligned, and obtaining a multi-task interactive learning model through the real CT image and the 3D simulation CT image which are structurally aligned. And a multi-task interactive learning model capable of carrying out image segmentation and classification is obtained. According to the invention, the 3D simulation CT image is generated, the dimensions of the simulation CT image and the real CT image are unified, and the dependence on the real CT image data is reduced.

The application discloses a longitudinal chest radiograph progress monitoring method and system based on anatomical anchor semantic difference, relates to the field of medical image processing, and generates a shared anatomical support mask by performing spatial union on candidate boxes of front and rear chest radiographs in a unified coordinate system; the mask and the original image are soft fused through a learnable gate to obtain an image enhanced in anatomical anchoring; longitudinal difference processing is performed on the front and rear enhanced images in an image-report semantic embedding space to obtain report-aware semantic difference, a visual encoder is used to extract multi-scale visual features to calculate difference features, the difference features are refined in nonlinearity, the refined difference information is asymmetrically injected into current visual features, and finally, multi-scale fusion, semantic recalibration and progress classification are completed. The application can stably, accurately and explainably monitor the disease performance changes in the front and rear chest images of the same patient without relying on the input of radiology reports in the reasoning stage.

The application discloses an AI model-based perioperative medical detection image accurate analysis and management system and method, and relates to the technical field of medical image processing. The AI model-based perioperative medical detection image accurate analysis and management system and method comprises the following steps: S1, preprocessing chest radiograph structure feature data and historical statistical data of a surgical patient in a perioperative period; S2, discriminating and segmenting a structure overlapping area by gradient field response and geometric density correction for the overlapping structure area; S3, constructing a time sequence registration criterion by adopting a structure-guided rigid and affine joint registration algorithm; S4, fusing time sequence labels by fusing response and space fluctuation suppression; and S5, constructing, training and reasoning a structure function evaluation model by adopting multi-modal feature enhancement. The technical problem that data labels are confused due to structure overlapping and time sequence changes in the prior art AI analysis of chest radiographs in a perioperative period, and the stability and accuracy of AI model training and reasoning are affected is solved.

A Wasserstein distance and difference metric-combined chest radiograph anomaly identification domain adaptation method and a corresponding system, a Wasserstein distance and difference metric-combined chest radiograph anomaly identification domain adaptation device, and a non-transitory computer-readable storage medium are provided. The method includes the following steps: step 1, data preparation and data pre-processing for chest radiographs; step 2, multi-scale feature extraction based on a swin transformer network; step 3, loss minimization based on a Wasserstein distance and a contrastive domain discrepancy; and step 4, using the model to perform chest radiograph prediction after verifying the model.

1. The name of the design product: electric double column support. 2. The use of the design product: the design product is used to assist patients to move position, and assist in examination. 3. The design points of the design product: the overall shape of the product. 4. The picture or photo that best indicates the design points: use state diagram. 5. Other circumstances that need to be explained: set 1 is a mobile photography bed, set 2 is a chest radiograph frame.