387 results about "Pulmonary nodule" patented technology

Provided is an intelligent grading method and system for pulmonary nodules based on multi-modal feature fusion, including: obtaining ROI and VOI of pulmonary nodules based on chest CT examination images and examination reports by utilizing clinical multi-modal data from physical examination population, designing a multi-task feature extraction network based on attention mechanism, to obtain radiomics features and deep image features from the ROI and VOI; designing a cross-modal feature fusion method based on graph representation learning, designing a multi-modal information extraction method, obtaining specific feature representations and graph structures of modalities, and then fusing the feature representations and the graph structures; and proposing an optimization and clinical verification method of pulmonary nodule grading GCN model based on self-supervised learning, to realize fine grading of pulmonary nodule malignancy with slight differences, thereby providing a new approach to design of fine-grained classification algorithms.

The invention belongs to the technical field of medical treatment, and discloses a pulmonary nodule segmentation method and system based on a diffusion model, and the method employs a potential space fusion method based on anatomical constraint, integrates the anatomical priori knowledge of lung tissue contour and the like through a variational auto-encoder (VAE), and combines a KL divergence constraint strategy. And the sensitivity of the model to a tiny focus is effectively improved. Secondly, a multi-modal collaborative optimization FSUNet architecture is constructed, and a cross-scale feature pyramid fusion mechanism is utilized to cooperate with a channel attention gating and dynamic noise scheduling algorithm, so that the expression ability of multi-dimensional features is enhanced. And finally, designing a Dice-Focal joint loss function to suppress complex background noise interference, and meanwhile, based on a forward diffusion-based noise sample generation strategy, remarkably reducing the dependence of the model on annotated data. Experimental results on an LIDC-IDRI data set show that the diffusion segmentation method based on multi-scale feature fusion not only shows excellent performance, but also shows excellent performance on an FPS index at the same time.

The invention relates to the technical field of medical artificial intelligence, in particular to a pulmonary nodule malignant risk dynamic prediction method and system based on space-time attention and multi-modal data guide fusion. Clinical features are obtained based on clinical data, and a lung CT slice image is obtained based on a lung CT image; the method comprises the following steps: extracting general medical visual features in an image by using an open-source medical large model, extracting image features in a lung CT slice image through a visual Transform model, and performing feature guide optimization on the image features based on clinical features and the general medical visual features to obtain patient image specific features; performing joint mapping on the specific features of the patient image by using a space-time attention mechanism to obtain space-time enhancement features of the patient image; and performing weighted fusion on the clinical features, the general medical visual features and the space-time enhancement features of the patient by using a gating mechanism, and obtaining the risk prediction probability of the malignant pulmonary nodules of the patient by using a classifier. The method can predict the malignant probability of the pulmonary nodule, and facilitates the realization of auxiliary diagnosis such as pulmonary nodule screening.

The invention discloses a pulmonary nodule boundary detection method based on semantic segmentation. The method comprises the following steps: generating an edge prior image set; obtaining a multi-scale boundary alignment feature map set and a deformation offset parameter set; a post-gating deformation offset parameter set is obtained, the multi-scale boundary alignment feature image set is updated, cross-scale feature fusion is executed, and a fusion feature image set is generated; establishing a node set and an edge set by utilizing node features in the fusion feature graph set and a spatial adjacency relationship, and initializing a node feature vector set; forming a fusion feature map set enhanced by multi-hop reasoning; and outputting a pulmonary nodule region mask prediction image set and a pulmonary nodule boundary probability prediction image set. According to the method, fracture between pseudo boundaries and slices is effectively inhibited, so that a three-dimensional structure prediction result is more coherent, and high-readability and high-consistency boundary segmentation output is provided for a clinical CAD (Computer Aided Design) system.

The invention discloses a pulmonary nodule risk prediction method, system and device based on a large model, and a medium, belongs to the technical field of artificial intelligence, and aims to solve the technical problems of insufficient generalization ability of a network model and poor pulmonary nodule multi-task prediction effect in the prior art. The method comprises the following steps: acquiring sample data of multiple modalities including medical images, gene detection and demographic data; single-mode features are obtained through an exclusive feature extraction module, and then the single-mode features are input into a plurality of expert networks to obtain single-mode experts; obtaining a multi-modal feature after the single-modal feature passes through a cross-modal attention module, and inputting the multi-modal feature into a plurality of expert networks to obtain a plurality of multi-modal experts; the three task MoE modules are all provided with task gating networks in a matched mode, and the task MoE modules select the most appropriate expert set according to the calculated probability that each expert is activated; acquiring to-be-predicted data, inputting the to-be-predicted data into the selected expert set, and outputting malignant, growth and wettability risk prediction results of the pulmonary nodules by the task model.

The invention provides a pulmonary nodule display method and device, electronic equipment and a storage medium, and the method comprises the steps: segmenting a three-dimensional reconstruction image of the chest of a target patient to obtain a preoperative segmentation image, the three-dimensional reconstruction image being obtained based on preoperative CT data, and the preoperative segmentation image comprising the position information of a pulmonary nodule; segmenting the video image of the intraoperative lung tissue of the target patient collected by the thoracoscope in real time to obtain an intraoperative segmented image; feature matching is conducted on the preoperative segmented image and the intra-operative segmented image, the lens pose of the thoracoscope is determined, the preoperative segmented image is mapped into a two-dimensional reference image based on the lens pose, and the two-dimensional reference image comprises the position information of the pulmonary nodule; and carrying out image registration on the two-dimensional reference image and the intra-operative segmented image, and carrying out pulmonary nodule marking on the intra-operative segmented image to obtain a video image for displaying pulmonary nodules in real time. The position of the pulmonary nodule in the operation is accurately displayed in real time in a non-invasive mode, and the operation efficiency is improved.

Systems and methods for performing one or more medical imaging analysis tasks on PCCT (photon-counting computed tomography) images are provided. Image acquisition parameters of a PCCT image acquisition device are determined for acquiring PCCT images. One or more PCCT images of an anatomical object of a patient acquired using the PCCT image acquisition device configured with the image acquisition parameters are received. One or more medical imaging analysis tasks analyzing the anatomical object are performed based on the one or more PCCT images using one or more machine learning based models. Results of the one or more medical imaging analysis tasks are output.

The invention discloses a three-dimensional pulmonary nodule detection method and system based on morphological adaptation convolution, and the method comprises the steps: obtaining a chest CT image sequence, and carrying out the preprocessing of all chest CT images in the chest CT image sequence; according to the three-dimensional pulmonary nodule detection method and system based on form adaptive convolution, scale factors are introduced into a 3D dynamic convolution layer and cooperate with the offset, so that the position of a convolution kernel sampling point can be adaptively adjusted according to the form and size of the pulmonary nodule, and the problem that the traditional fixed convolution is difficult to adapt to the size difference and form heterogeneity of the pulmonary nodule is solved; stable detection of pulmonary nodules with different sizes, especially tiny pulmonary nodules, is realized; space coordinate information and multi-scale features are combined through a channel space attention module, extraction of key information such as pulmonary nodule edge texture and density gradient is enhanced, interference of blood vessel, trachea and CT artifacts is inhibited, and the problem that small nodules and background noise are difficult to distinguish is solved.

The invention discloses a pulmonary nodule image segmentation method and device based on optimization down-sampling and feature fusion, and relates to the technical field of image processing, the method comprises the steps: obtaining a data set containing a plurality of chest CT images, cutting the chest CT images in the data set into a preset-size region of interest containing pulmonary nodules, and obtaining a plurality of pulmonary nodules; constructing to obtain a training set, a verification set and a test set; the method comprises the following steps: constructing an SPDD module for realizing space-to-depth downsampling and a DPFM module for realizing dual-branch fusion, and creating a DFNet model based on the constructed SPDD module and DPFM module; and training, verifying and testing the DFNet model based on the training set, the verification set and the test set, and evaluating the performance of the DFNet model. The pulmonary nodule segmentation capability of the model can be effectively improved.

The embodiment of the invention provides a pulmonary nodule automatic labeling method based on multi-modal semantic affinity iterative optimization, and belongs to the technical field of data processing, and the method specifically comprises the steps: obtaining a lung CT image and a text report; performing structured semantic analysis on the text report; performing visual feature extraction on the lung CT image; based on the key semantic information, performing semantic guidance processing on the depth visual feature representation to generate semantic enhanced visual guidance features; based on the semantic enhanced visual guidance features, calling a segmentation basic model, and generating a plurality of candidate segmentation masks by introducing an active variation mechanism; for each candidate segmentation mask, calculating a semantic matching degree score of the candidate segmentation mask; and comparing the semantic matching degree scores of all the candidate segmentation masks, and selecting the candidate segmentation mask with the highest score as a final pulmonary nodule labeling result to be output. Through the scheme of the invention, the labeling precision, adaptability and interpretability are improved.

The present invention discloses a dandelion flavonoid derivative, its preparation method, and application. The dandelion flavonoid derivative has the following molecular structure: The dandelion flavonoid derivative provided by the present invention has anti-inflammatory and lung nodule-removing effects, is simple to prepare, and has good biocompatibility, making it a promising new drug for eliminating lung nodules.

The invention discloses an intraoperative pulmonary nodule positioning method and system based on fusion of a CT image and an endoscope video. The method comprises the following steps: firstly, constructing a three-dimensional lung model containing a pulmonary nodule position based on preoperative high-resolution CT data; generating a real-time lung surface model by using an endoscope video stream through a three-dimensional reconstruction technology during the operation; a feature point extraction and matching algorithm is adopted, and a non-rigid registration technology is combined to realize accurate registration of the preoperative model and the intraoperative dynamic model; projection display of pulmonary nodules in a real-time endoscope visual field is established through space coordinate mapping, and a dynamic compensation algorithm is developed to solve the problem of tissue deformation caused by respiratory movement and instrument operation. Meanwhile, the virtual nodule position is overlaid to an endoscope video picture through the augmented reality technology, and visual intraoperative navigation is provided for surgeons. The method effectively solves the technical bottleneck that a traditional method depends on a metal marker and cannot adapt to intraoperative tissue deformation and the like, and has clinical practical value.

The invention provides a context feature aggregation pulmonary nodule segmentation method combining edge perception and multi-scale semantic guidance, and the method comprises the steps: obtaining a to-be-segmented pulmonary nodule CT image, inputting the to-be-segmented pulmonary nodule CT image into a trained EGP-Net network model, and obtaining a segmentation result; wherein in the EGP-Net network model, multilayer features of an encoder are sent to a global pyramid guide module to reconstruct and selectively introduce deep global semantics back to a shallow layer, and low-layer features are merged into an edge guide network to extract and reinforce fine-grained boundary information; the attention feature fusion module performs weighted attention fusion on global semantics and local edge features, and the multi-scale context decoder fuses multi-scale information through lightweight reconstruction such as sub-pixel convolution to generate a fine segmentation map. Experimental results show that the EGP-Net network model is superior to an existing advanced method in segmentation precision and boundary consistency, and has good clinical auxiliary diagnosis and precision medical application potential.

The invention discloses a pulmonary nodule malignant classification method based on double-view scanning and hypergraph fusion driving, and the method comprises the steps: firstly collecting and preprocessing a pulmonary nodule CT data set which comprises a public data set and a private data set; the method comprises the following steps: building a DPSHG-VMama model for pulmonary nodule classification, wherein the DPSHG-VMama model comprises a stacked convolution Stem block, a text embedding layer, a collaborative fusion Mama block, a stacked DP-VMama block, a patch merging layer for down-sampling and a feature classifier; the preprocessed image data set serves as input, a DPSHG-VMama model is trained, and network parameters are optimized through iteration; and inputting a to-be-classified region-of-interest image into the trained DPSHG-VMama model, and outputting an accurate pulmonary nodule vicious classification result. According to the method, the pulmonary nodule malignancy degree is predicted by effectively establishing the space-time dynamic model and improving the multi-modal fusion efficiency, so that the classification accuracy is remarkably improved.

The invention relates to the field of medical image processing and artificial intelligence, in particular to a pulmonary nodule treatment effect AI evaluation system which comprises an image acquisition module, an image registration module, a feature representation module, a multi-scale analysis module, a trajectory analysis module, a response prediction module and a decision support module. According to the system, accurate alignment of CT images before and after treatment is realized through a 4D registration technology, manifold representation of a pulmonary nodule state is constructed based on a differential geometry theory, and nodule features are mapped into points on a high-dimensional manifold; extracting features of different time and space scales by adopting multi-scale space-time analysis, and constructing a manifold trajectory representing a treatment response process; geodesic prediction is realized by using a Riemann geometric framework, and long-term curative effect is predicted from early treatment response; the system not only evaluates the current treatment effect, but also can provide personalized treatment suggestions and optimal follow-up visit plans.

The invention discloses a pulmonary nodule detection method based on multi-kernel representation learning, and belongs to the field of medical image analysis. The method comprises the following steps: S1, obtaining suspected region image block data of which the form is similar to that of a nodule, and carrying out standardization processing; s2, performing feature extraction through principal component analysis according to the normalized image blocks; s3, according to the extracted feature representation, calculating corresponding kernel matrixes by using multiple kernel functions; s4, according to the obtained multiple kernel matrixes, based on spectral entropy weighted fusion, obtaining a mixed kernel matrix; s5, training a single-class support vector machine model according to the mixed kernel matrix, and establishing a discrimination boundary of a normal sample; and S6, in a test stage, repeatedly executing the steps S1 to S4 on suspected nodule region image blocks acquired from the CT image of the patient, extracting features, constructing a mixed kernel matrix, inputting the trained single-class support vector machine model for judgment, and if the suspected nodule region image blocks are abnormal, outputting a nodule region until judgment of all candidate regions is completed. The problems that an existing single-core support vector machine model is poor in adaptability and sensitive to data are solved, the deep learning method depends on a large number of labeled samples, and robust pulmonary nodule detection is achieved under the condition that pulmonary nodule samples are scarce by means of the unsupervised characteristic of anomaly detection.

The invention discloses a pulmonary nodule malignancy probability prediction method based on radiomics and clinical features, and the method comprises the following steps: collecting and processing original lung image data, and extracting pulmonary nodule image blocks; collecting and processing clinical data of a target patient, and constructing a structured clinical feature vector; inputting the pulmonary nodule image block and the structured feature vector into a dual-channel adaptive fusion network, and extracting an image feature vector and a structured embedded feature vector; a modal sensing module judges that a modal is missing, a modal compensation module is started when the modal is missing, a pseudo-dual-channel output feature pair is generated, and otherwise, a cross attention fusion feature vector is generated; a dynamic fusion module carries out dynamic fusion on the cross attention fusion feature vector or the pseudo dual-channel feature pair, and outputs a dynamic fusion feature vector; and inputting the dynamic fusion feature vector into a classifier, and outputting a malignant probability value of the pulmonary nodule. According to the invention, a dual-channel adaptive fusion network is adopted to realize the intelligent prediction of the malignant probability of pulmonary nodules.

The invention discloses a lung abnormal nodule identification processing method and system, and the method comprises the steps: obtaining lung CT image data, and carrying out the preprocessing of the lung CT image data, and obtaining a to-be-detected lung image; performing pixel point analysis on the lung image to be detected by using a discrete fractional differential operator to obtain a fractional order enhanced image; carrying out multi-scale and multi-direction acquisition on pixel points of the fractional order enhanced image to calculate local energy and local entropy, and constructing a high-dimensional fusion feature matrix; carrying out dimensionality reduction on the high-dimensional fusion feature matrix, and extracting to obtain a pulmonary nodule region; the nodule region in the lung CT image can be effectively extracted; through pixel point analysis, calculation of local energy and entropy, and construction and dimension reduction of a high-dimensional feature matrix, the lung abnormal nodule region is finally identified, and the detection precision and efficiency of the pulmonary nodule are improved.

The invention discloses a pulmonary nodule benign and malignant identification and prediction system based on multi-modal feature fusion, and belongs to the technical field of data processing, and the system comprises a data collection module which is used for collecting pulmonary nodule multi-modal data; the feature fusion module is used for carrying out preprocessing and feature fusion on the pulmonary nodule multi-modal data, and generating pulmonary nodule multi-modal fusion data by integrating features of different modals; and the identification and prediction module is used for constructing a pulmonary nodule benign and malignant identification and prediction model, analyzing and identifying the pulmonary nodule multi-modal fusion data according to the pulmonary nodule benign and malignant identification and prediction model, and determining a pulmonary nodule benign and malignant identification result. According to the method, the problems that effective pulmonary nodule benign and malignant identification and prediction cannot be carried out based on multi-modal feature fusion in the prior art, and the accuracy and efficiency of pulmonary nodule benign and malignant identification are reduced are solved. Effective pulmonary nodule benign and malignant identification and prediction can be carried out based on multi-modal feature fusion, and the accuracy and efficiency of pulmonary nodule benign and malignant identification can be improved.

The invention discloses a benign and malignant identification and growth prediction system based on pulmonary nodule radiomics, and relates to the technical field of medical image processing. Comprising an acquisition module used for acquiring a pulmonary nodule segmentation mask, a CT value and surrounding tissue segmentation data; the calculation module is used for synchronously calculating a first score representing internal CT value distribution heterogeneity, a second score representing surface complexity and a third score representing blood vessel interaction according to the data; the judgment module is used for triggering a high-risk alarm when the scores of the three items all exceed corresponding threshold values; the prediction module is used for correcting the basic growth model according to the three scores and generating growth prediction data; and the output module outputs an alarm and a prediction result. The system also comprises optimization modules of weight fusion, time sequence processing, growth partitioning and the like. According to the scheme, through multi-dimensional feature fusion and dynamic modeling, more accurate identification of benign and malignant pulmonary nodules and more reliable growth trend prediction are realized, and key support is provided for clinical decision making.

A traditional Chinese medicine composition for treating pulmonary nodules, wherein the composition is prepared from the following traditional Chinese medicines in parts by weight: a principle drug selected from one or more of 10-45 parts of raw Astragali radix, 10-25 parts of Sargassum, and 10-25 parts of Arisaematis rhizoma preparatum; a minister drug selected from one or more of 10-25 parts of Fritillariae thunbergii bulbus, 10-25 parts of Forsythiae fructus, 10-25 parts of stir-fried Atractylodis macrocephalae rhizoma, 1T0 part of Hirudo, 10-25 parts of Dioscoreae nipponicae rhizoma, 10-35 parts of Adenophorae radix, 10-35 parts of honey-processed Mori cortex, 10-25 parts of Perillae fructus, 10-25 parts of Peucedani radix, 10-35 parts of Phragmitis rhizome, 10-45 parts of raw Coicis semen, 5-25 parts of vinegar-processed Cyperi rhizoma, 10-25 parts of Aurantii fructus, 10-25 parts of Paeoniae radix alba, 10-35 parts of Scutellariae barbatae herba, 10-35 parts of Herba duchesneae indicae, 10-35 parts of Salviae chinensis herba, 5-20 parts of Menispermi rhizoma, 5-20 parts of Momordicae semen, 10-25 parts of Curcumae rhizoma, and 5-20 parts of Gleditsiae spina; an assistant drug selected from one or more of 5-25 parts of vinegar-processed Bupleuri radix, 5-25 parts of Citri reticulatae pericarpium, 10-25 parts of Perillae fructus, 2-12 parts of processed Ephedrae herba, 10-25 parts of Cynanchi stauntonii rhizoma et radix, 5-20 parts of Typhonii rhizoma, 2-12 parts of Zingiberis rhizoma, 10-35 parts of Morindae officinalis radix, 10-35 parts of Scutellariae barbatae herba, 10-45 parts of Radix aconiti lateralis praeparata nigra, and 10-25 parts of Typhonii rhizoma praeparata; and an envoy drug selected from one or more of 5-20 parts of Fagopyri dibotryis rhizoma, 10-25 parts of Platycodonis radix, 10-25 parts of Paeoniae radix alba, and 5-25 parts of Glycyrrhizae radix et rhizoma praeparata cum melle.

The invention discloses a pulmonary nodule MRI segmentation method based on attention-guided cross-modal fusion, belongs to the field of medical image processing, and aims to solve the problems of high missing detection rate of small nodules, insufficient multi-modal fusion and low boundary segmentation precision in existing pulmonary nodule MRI segmentation. The method comprises the following steps: preprocessing a lung multi-modal MRI image and generating a pulmonary nodule multi-modal attention feature map; constructing a segmentation network with attention jump connection and a cross-modal fusion network based on a conditional diffusion model, and forming a dual-branch joint optimization framework of segmentation and fusion decoupling; and end-to-end training is completed through a joint loss function including segmentation loss, fusion loss and consistency loss. According to the method, the pulmonary nodule segmentation precision and the model robustness are remarkably improved, and the method is suitable for accurate diagnosis scenes of clinical pulmonary nodules.

Provided is an intelligent grading method and system for pulmonary nodules based on multi-modal feature fusion, including: obtaining ROI and VOI of pulmonary nodules based on chest CT examination images and examination reports by utilizing clinical multi-modal data from physical examination population, designing a multi-task feature extraction network based on attention mechanism, to obtain radiomics features and deep image features from the ROI and VOI; designing a cross-modal feature fusion method based on graph representation learning, designing a multi-modal information extraction method, obtaining specific feature representations and graph structures of modalities, and then fusing the feature representations and the graph structures; and proposing an optimization and clinical verification method of pulmonary nodule grading GCN model based on self-supervised learning, to realize fine grading of pulmonary nodule malignancy with slight differences, thereby providing a new approach to design of fine-grained classification algorithms.

The invention discloses a pulmonary nodule detection system and method based on federated learning and a target detection algorithm, and relates to the technical field of medical image detection and auxiliary diagnosis, the system comprises a plurality of medical institution clients and a cloud central server, the central server initializes to generate a global model, and deploys the global model to each client; each client uses a local CT data set to train a global model, model parameters are updated through training until loss is minimized so as to complete training, a local model is generated, and the updated model parameters are uploaded to the central server; wherein the total loss of the local model is the dynamic weighted sum of various losses; the central server adopts a dynamic weighted average method to aggregate the updated model parameters of all the clients, generates an updated global model, then deploys the updated global model, and gradually optimizes the global model through multiple times of global iteration updating; and each client identifies the to-be-detected CT image by using the locally deployed optimized global model, and outputs a more accurate pulmonary nodule identification result.

The invention discloses a pulmonary nodule growth prediction method based on the combination of LSTM and GAN, and relates to the technical field of image analysis and processing, and the method comprises the following steps: collecting multi-time-sequence chest CT data, and carrying out the marking and auditing to form a data set; preprocessing the marked CT data to obtain a standardized pulmonary nodule ROI (Region of Interest); segmenting a pulmonary nodule region by using the three-dimensional image segmentation network, and generating a three-dimensional segmentation mask; inputting the segmentation masks of the multiple time nodes into an LSTM network, and extracting a time sequence feature vector representing nodule dynamic evolution; based on the time sequence feature vector and the random noise, the generator synthesizes the predicted pulmonary nodule image at the future moment, and the discriminator jointly optimizes model parameters through multiple loss functions; and inputting the three-dimensional segmentation masks at the current moment and the historical moment, and generating a predicted pulmonary nodule image at the future moment. The method can effectively improve the situation that the prior art is insufficient in utilization of time sequence information and lacks high-quality generation and inference ability.

The invention belongs to the technical field of medicine, and discloses a pulmonary nodule intraoperative positioning system based on a flexible array sensor, which comprises the flexible array sensor arranged at a to-be-detected part of a subject and used for collecting a compression signal and obtaining stress distribution; the nodule area detection unit is used for determining a nodule area through clustering analysis according to the stress distribution; the morphological optimization unit is used for performing morphological optimization on the determined nodule region; the feature extraction unit is used for extracting related geometric features based on the optimized nodule region; and the visualization unit is used for visually displaying the stress distribution, the optimized nodule region and related geometric features. The method can assist doctors in accurately positioning the pulmonary nodules, and meanwhile, quantitative features of the nodules are provided.

The invention provides an uncertainty perception pulmonary nodule segmentation method based on a multi-branch collaborative network, and the method introduces an expert conflict perception mechanism, and enables a model to learn consensus information and divergence features between scorers at the same time through constructing a probability graph soft label and a conflict graph based on multi-scorer labeling. Therefore, the uncertainty existing in clinical practice can be reflected more comprehensively; a multi-branch collaborative segmentation network structure is provided, an encoder-decoder trunk is combined with three functional branches of consensus, conflict and boundary, and adaptive weighted integration is realized through a conflict regulation and control fusion module. The method not only improves the overall precision of the segmentation result, but also can generate probability distribution prediction which better meets the actual diagnosis demand under the condition that the opinions of multiple scorers are inconsistent, and provides a more reliable basis for clinical auxiliary decision making.

The invention relates to the field of medical image processing, in particular to a pulmonary nodule natural orifice optimal path intelligent planning system which comprises a data preprocessing module, a bronchus three-dimensional segmentation module, a pulmonary nodule segmentation module, a differential geometric path search module, a path evaluation module and a doctor interaction module. The differential geometric path search module comprises a Riemannian curvature flow modeling unit, a multi-scale Gaussian curvature constraint unit and a geodesic curvature self-adaptive correction unit, can model bronchus into a Riemannian manifold, applies curvature flow optimization, and combines a multi-scale decomposition strategy and a geodesic theory to obtain a geodesic curvature self-adaptive correction model; precise description and optimal path planning of bronchial geometrical characteristics are realized, the path evaluation module provides multi-dimensional evaluation of blood vessel avoidance, diameter adaptation and curvature safety, and the doctor interaction module supports three-dimensional visualization and real-time path editing. The problem that a traditional Euclidean space algorithm is insufficient in description of complex forms of bronchus is solved by introducing a differential geometry theory.

The invention relates to the technical field of audio signal processing. The invention provides a method, system and device for predicting pulmonary nodules through sound and a storage medium. The method comprises the following steps: in a quiet test environment, directionally collecting a sound signal of a patient through an audio sensor; wherein the sound signals comprise breath sounds, cough sounds, vowels and sentences; performing noise reduction processing on the collected sound signals to obtain a noise reduction data set; extracting multi-dimensional voiceprint features from the noise reduction data set, and constructing a sound feature matrix; and performing multi-modal fusion analysis on the sound feature matrix and clinical data of the patient, inputting the sound feature matrix and the clinical data into a sound prediction model optimized by a focus loss function, and outputting a pulmonary nodule risk prediction result and a visual diagnosis report. The problems that the misdiagnosis and missed diagnosis rate is high, the radiation risk is large and the equipment dependence is strong when the pulmonary nodules are examined by the existing iconography, and the characteristic analysis is single, the noise immunity is poor and the screening accuracy is insufficient in the sound detection technology are solved.

The invention provides a pulmonary nodule patient treatment prediction method based on artificial intelligence, and the method comprises the steps: obtaining a historical tongue picture feature and a historical pulmonary nodule CT image, extracting a tongue coating color value RGB component, a spatial frequency of a tongue surface texture feature, a gradient value of pulmonary nodule edge sharpness, and a CT value standard deviation of pulmonary nodule density distribution, and obtaining an initial feature set; extracting the change trend of the tongue coating thickness and the pulmonary nodule edge sharpness in the treatment cycle from the weighted feature set, analyzing the dynamic evolution rule of the correlation between the change rate of the tongue coating thickness and the gradient value of the pulmonary nodule edge sharpness, and obtaining a time sequence feature vector in combination with the time rhythm characteristics of the traditional Chinese medicine tongue diagnosis; and carrying out standardization and normalization processing on the fusion feature set, predicting by combining a clinical curative effect evaluation standard and a pulmonary nodule iconography improvement index to obtain the correlation strength of the change rate of the thickness of the tongue coating and the improvement trend of the edge sharpness of the pulmonary nodule in a treatment cycle, and obtaining a predicted curative effect result.