83 results about "Disease Screening" patented technology

The invention provides a disease screening system based on a large model. The disease screening system is used for solving the technical problem that an existing disease screening system is intelligently used for single special disease screening. The system comprises a scheduling model and a plurality of AI auxiliary diagnosis models, the scheduling model is connected with the plurality of AI auxiliary diagnosis models, and the scheduling model is connected with a big data disease library. According to the method, a high-level scheduling model is utilized, multiple single AI auxiliary diagnosis models are managed in a centralized mode, automatic calling of a multi-disease AI system is achieved, automatic structured report generation is achieved through a large language model, historical medical history is combined, progress is predicted, treatment suggestions and reference cases are automatically given, an existing clinician film reading workflow is fitted, and the efficiency is improved. The whole process of actual diagnosis decision making of doctors is greatly fitted, and the working efficiency of the doctors can be improved to the maximum extent.

The invention discloses a medical image classification method and system based on multi-scale spatial state modeling, and the method comprises the steps: firstly dividing an input medical image into a plurality of non-overlapping image blocks, and mapping the non-overlapping image blocks to a feature space through a learnable linear projection layer to obtain an initial feature map; then, multiple layers of stacked MS-SMamba blocks are used for carrying out layer-by-layer feature extraction, each MS-SMamba block comprises a main branch, an auxiliary branch, a dynamic gating fusion network, a residual error connection unit and a feedforward network, and long-range dependency relation capture and multi-scale feature fusion are achieved; and finally, processing the last-layer output feature map through a global feature aggregation and classification module, generating a global feature vector, and outputting a classification result. According to the method, the capturing capability of complex pathological features in the medical image is improved, the calculation efficiency and clinical applicability are improved, and the method is suitable for scenes such as disease screening and auxiliary decision making in medical image diagnosis.

The invention relates to a big-small model collaborative diagnosis system based on federated learning and knowledge transfer. Comprising a data processing layer used for acquiring a to-be-diagnosed multi-modal medical examination data set and performing cross-modal feature processing to obtain cross-modal fusion features; the cross-modal fusion feature comprises a cross-modal alignment feature and text embedding; the large-small model collaborative architecture layer is used for performing disease preliminary screening according to the cross-modal fusion features, and calling a corresponding model for disease diagnosis according to a preset diagnosis strategy to obtain a diagnosis result; the encryption federal layer is used for encrypting bidirectional knowledge transmission between the edge end small model and the cloud end large model by using an encryption mechanism; and the bidirectional dynamic knowledge routing layer is used for bidirectionally optimizing the edge-end small model and the cloud-end large model based on a bidirectional dynamic routing mechanism in combination with the diagnosis result. By adopting the system, the diagnosis performance, the real-time response capability and the privacy protection level can be guaranteed, and meanwhile, the dynamic collaboration among model collaboration, knowledge evolution and data compliance is realized.

The invention discloses a disease risk assessment method and screening device based on a multi-group student physical collaborative digital network, and relates to the field of intelligent medical detection. In order to solve the defect that multi-omics-level system collaborative analysis and robust risk assessment are difficult to realize in the prior art, the technical scheme provided by the invention is as follows: acquiring a plasma sample, acquiring a spectral signal by adopting an attenuated total reflection Fourier transform infrared spectrum, and establishing a plasma spectrum digital information space; the method comprises the following steps: constructing a biological collaborative digital network containing four nodes of protein, lipid, saccharides and nucleic acid based on pathophysiology priori knowledge, and defining node strength, edge weight and network collaborative efficiency; a health baseline configuration file is established by using a health sample, a standardized deviation score of a to-be-tested sample is calculated, a comprehensive risk score is obtained, a disease screening result is output in combination with a machine learning model, and digital evaluation of multi-omics collaborative characteristics is realized. The method is suitable for non-invasive rapid screening and risk assessment work of neurodegenerative diseases and mental diseases.

The invention discloses a municipal road surface intelligent inspection method and system based on an unmanned aerial vehicle array, and the method comprises the steps: constructing a municipal digital twin base, fusing multi-source data, and carrying out the preliminary screening of diseases through a convolutional neural network; a quantum key and elliptic curve encryption mixed mode is adopted to perform data security enhanced transmission; obtaining the position of a relay node of the unmanned aerial vehicle based on an edge computing node, and constructing a microwave link through multi-module cooperation; measuring link quality, and dynamically planning an unmanned aerial vehicle inspection array; a three-level intelligent diagnosis system is constructed based on multi-algorithm coupling, and disease diagnosis is carried out; evaluating the inspection result, and updating the inspection scheme. According to the invention, rapid routing inspection is carried out through the unmanned aerial vehicle array, data transmission safety is guaranteed by adopting an encryption technology, transmission reliability is improved by dynamic link aggregation, a routing inspection scheme is dynamically adjusted, a routing inspection path and the unmanned aerial vehicle array are automatically optimized, a virtuous circle is formed, and intelligent development of municipal road routing inspection is promoted.

The invention provides a space disease physiotherapy system fusing multi-source heterogeneous biological information, and relates to the technical field of intelligent spaceflight physiotherapy. The system comprises a broadcast module, a first acquisition module, a second acquisition module, a category determination module and a physiotherapy scheme module. The broadcasting module is used for generating and broadcasting space disease diagnosis problems according to a preset symptom description list, the first acquisition module and the second acquisition module are used for acquiring answer voice information and physiological index data, the category determination module is used for determining category information of space diseases suffered by astronauts, and the physiotherapy scheme module is used for determining physiotherapy schemes. According to the method and the device, accurate space disease screening prediction can be carried out on the astronaut according to the collected answer voice and the physiological indexes without guidance of professional physicians, dependence on the professional physicians is reduced, the accuracy and the efficiency of screening prediction of the space disease suffered by the astronaut are improved, effective support is provided for further determination of a physical therapy scheme, and the method and the device are suitable for popularization and application. And the physical therapy effect of space diseases is improved.

A rapid diagnostic device for detecting multiple biomarkers in bodily fluid samples, particularly useful for disease screening including breast cancer. The device comprises a housing containing at least one test strip with a sample receiving zone, conjugate pad, and multiple test zones. The test strip utilizes specific materials including Cytiva GF / DVA and Ahlstrom 1667 sample pads, an Ahlstrom 6613 conjugate pad with carboxylated 150 nm gold nanoshell conjugates, and a Sartorius CN140 membrane with precisely positioned test and control zones. The device simultaneously detects multiple biomarkers, such as hCG and CA-15-3, using specific antibodies. Features include a built-in safety lancet, integrated buffer system, and compatibility with various sample types. Results are available within ten minutes, with options for quantitative analysis via a dedicated reader or smartphone application. This multi-biomarker approach potentially improves screening accuracy compared to single-marker tests, offering versatility for detecting various medical conditions.

The invention discloses a health state monitoring management system for a traditional Chinese medicine AI digital health manager, and belongs to the technical field of health management. Comprising an information receiving module; a health index calculation module; a personal case library construction module; a primary disease screening module; a secondary disease screening module; and a disease diagnosis module. According to the invention, by constructing a personal case library, adopting primary and secondary grading screening mechanisms and combining with an analytic hierarchy process to dynamically set feature weights and standardize data, accurate personalized health state monitoring, efficient disease screening and diagnosis and scientific quantitative evaluation effects conforming to traditional Chinese medicine syndrome differentiation logic are achieved; the problems of insufficient utilization of personalized health data, low disease screening efficiency and unscientific health index quantitative evaluation in the prior art are solved.

The application discloses a kind of children's rare lung disease multi-modal intelligent screening method, system and equipment, belong to medical artificial intelligence technical field.The method includes: obtaining the medical image data, clinical text data and corresponding visit time information generated by the children at multiple time points, and the multi-modal data is standardized;Based on the multi-modal time series data after processing, construct cross-modal time series logic diagram, the cross-modal time series logic diagram is used to represent the association between different modal data and its evolution characteristics with time change;The cross-modal time series logic diagram is input into the children's rare lung disease screening model for analysis, and the risk prediction result of suffering from rare lung disease is output.The screening model includes multi-modal feature extraction module, cross-modal feature fusion module and global comprehensive prediction module, by the joint modeling of image feature, text feature and time series information, realize the intelligent screening of children's rare lung disease.The application can overcome the problem of multi-modal information fragmentation and insufficient use of time series association in the prior art, and is suitable for the auxiliary screening and risk assessment scene of children's rare lung disease.

1. Name of the product in this design: Display screen panel with a graphical user interface for disease screening. 2. Purpose of this design: To display a graphical user interface. 3. The key design features of this product are its graphical user interface. 4. The image or photograph that best illustrates the design's key features: the front view. 5. The display screen panel uses a standard design, omitting other views. 6. Purpose of the graphical user interface: Used for screening diseases after a physical examination. Human-computer interaction and state changes in the graphical user interface: The upper part of the main view displays a 3D human body model, and the "Respiratory System" button is lit. The lower part displays a floating window introducing the symptoms corresponding to the respiratory system. Swiping left or right on the 3D human body model in the main view allows you to rotate and view the model. For example, swiping left on the 3D human body model leads to state change diagram 1. Clicking the "Circulatory System" button in the main view leads to state change diagram 2. The lower part of state change diagram 2 displays a floating window introducing the symptoms corresponding to the circulatory system. Swiping up on the floating window in state change diagram 2 leads to state change diagram 3, where the floating window becomes longer. Swiping down on the longer floating window in state change diagram 3 returns you to state change diagram 2. In the interface change state diagram 3, the gray blocks represent replaceable content screens. 7. Other situations requiring explanation: This display panel can be used with desktop computers, laptops, tablets, televisions, and mobile phones.

The invention provides a multi-modal fusion-based congenital disease early screening method and device, and the method comprises the steps: collecting a target case ultrasonic image and a case report, building a cue word template, and guiding a large language model to output a structured text; extracting visual semantic description based on a Q-former architecture, obtaining image features and enhanced text features, fusing the image features and the enhanced text features to obtain joint features, inputting the joint features to a pre-trained classifier to obtain a disease prediction result, and establishing a total loss function optimization classifier; and for a plurality of ultrasonic images of a single case, outputting a final disease prediction result by adopting a majority voting strategy so as to realize early screening of congenital diseases. According to the method, image features are subjected to semantic supplementation by introducing migration knowledge and text information of a large language model, a deep interaction mechanism of an image modal and a text modal is constructed, the problem of alignment of different-source modal data is solved, effective fusion of cross-modal deep features is realized, and thus the accuracy and reliability of disease screening are remarkably improved.

The application relates to the technical field of medical treatment and discloses a liver chronic disease screening method, a device and a storage medium.The method comprises the following steps: a liver detection system collects liver stiffness data and liver fat attenuation parameters of a target object; a mobile terminal system receives the liver stiffness data and the liver fat attenuation parameters, combines the liver stiffness data and the liver fat attenuation parameters with basic information of a preset target object, generates acquisition characteristic data, carries out label marking matrix processing on pretreatment integrated data of the cloud service system, generates a label marking matrix, carries out fitting residual processing on the label marking matrix, obtains extended decision tree data, carries out discrete sampling processing on the extended decision tree data, obtains a sparse vector, carries out classification calculation processing on the sparse vector, obtains a classification result, and generates a disease screening report based on the classification result.In the embodiment of the application, through system architecture and cloud service system data processing, the early warning timeliness and accuracy of liver chronic disease screening are improved.

The application provides a FFA image generation system, method, medium and device based on a dual-domain constraint Mamba diffusion model, which integrates dual spatial and frequency domain constraints for cross-modal generation of FFA images from CFP images. Through a learnable wavelet feature extractor, the key frequency components of blood vessels are effectively extracted from the fundus CFP image, enhancing the structured guidance of the vascular edge representation in the generated FFA image. In the spatial domain, through a dual-pyramid feature extractor, the local details and global semantics of the fundus lesion area are effectively fused, improving the consistency of the lesion representation in the generated FFA image. The dual-domain conditional Mamba module serves as the denoising core, effectively coordinating spatial and frequency information to improve the clarity and accuracy of the image. The application is verified on the Hajeb, MPOS and clinical data sets, with PSNR reaching 30.11 and 29.40, effectively generating high-quality FFA images that retain vascular details, providing a new method for retinal disease screening.

The application discloses a kind of X-ray film lung disease screening systems of fusing multi-level features, including lung parenchyma segmentation module, lung disease preliminary screening module, multi-level feature construction module, lung disease screening module;Lung parenchyma segmentation module is used to obtain lung parenchyma part from complete chest X-ray film segmentation;Lung disease preliminary screening module is used to construct based on feature consistency variation auto-encoding generation adversarial network and train it, and obtains preliminary screening abnormal score using trained generation adversarial network;Multi-level feature construction module is used to extract features of different semantic levels, including the extraction of bottom visual features, middle layer depth features and high layer experience features;Lung disease screening module is used to construct lung disease screening model using multi-level features and the abnormal score obtained by lung disease preliminary screening module, and obtains the final lung disease classification result.The present application can effectively solve the problem that current computer-aided lung disease screening field research is difficult to be applied to practical clinical scene.

The utility model discloses a sealing structure of a disease screening test tube, which comprises a test tube body and a cover covering the test tube body, a first sealing structure is arranged on the cover and the test tube body, and a second sealing structure is arranged on the test tube body. The first sealing structure comprises a first sealing ring which is formed on the outer side of the middle of the cover and abuts against the inner wall of the test tube body after the cover is closed, and a second sealing ring which is formed on the outer side of the lower end of the cover and abuts against the inner wall of the test tube body after the cover is closed. The first sealing ring and the second sealing ring are both in interference fit with the inner wall of the test tube body. According to the sealing structure of the disease screening test tube, reagent filling and sealing are facilitated, the sealing performance is good after the cover is closed, peculiar smell of a sample and the reagent cannot be leaked, deodorization is effectively achieved, and use by people is facilitated.

The application discloses an application of a prostate hyperplasia related marker in preparation of a product for detecting prostate hyperplasia, and the prostate hyperplasia related marker is at least one selected from MINP1, ACTBL and APOC4 proteins. In the technical scheme, MINP1, ACTBL and APOC4 are combined as a detection marker for prostate hyperplasia (BPH), so that the complementarity of the three in different stages of diseases can be fully exerted, and the comprehensive performance of the detection product is obviously improved. MINP1 and ACTBL participate in disease progression by regulating prostate cell proliferation and apoptosis, and APOC4 can present significant expression changes in blood cells of early BPH patients, so that BPH disease screening can be accurately performed. When the three are applied in cooperation, early screening of BPH can be realized, and the activity and severity of the lesion can be evaluated, so that a detection system covering the whole process of disease occurrence and development is formed, the limitation of a single marker in the diagnosis dimension is effectively made up, and the comprehensiveness and accuracy of BPH detection are greatly improved.

The invention discloses a urinary incontinence prediction device and method based on machine learning, and relates to the technical field of urinary incontinence prediction. The method comprises the steps that a database is built on the basis of the device, and the database comprises electronic data of urinary incontinence disease screening; preprocessing the data in the database; selecting features related to urinary incontinence from the preprocessed data; dividing data into a training set and a test set, and introducing a cost function to optimize the initial XGBoost prediction model to obtain an optimized function; and performing prediction by using the optimized XGBoost function so as to solve prediction data of related urinary incontinence prediction. The device comprises a bladder pressure monitor body, a display screen is fixedly arranged on the front face of the bladder pressure monitor body, a pressure measuring connector is arranged at the top of the bladder pressure monitor body, a catheter is arranged on one side of the bladder pressure monitor body, and a cleaning device is arranged on the front face of the bladder pressure monitor body. And a first motor is arranged in the cleaning device. By arranging the first motor, the lead screw, the nut seat and the cleaning rod, the first motor drives the lead screw to rotate, the rotating lead screw drives the nut seat and the cleaning rod to vertically move, the vertically moving cleaning rod can clean the surface of the display screen, attached dust is cleaned down, and the display screen is kept clean; and dust is prevented from hindering a worker to read bladder pressure monitoring data displayed on the display screen.

The application discloses a magnetic capsule gastroscopy vehicle and a magnetic body thereof. The magnetic capsule gastroscopy vehicle comprises a vehicle body, the vehicle body comprises a vehicle compartment, the inside of the vehicle compartment is divided into a front cavity and a rear cavity arranged in front and back through an isolation door, the front cavity is a medical staff control area for medical staff activities, and the rear cavity is a patient disinfection and detection area for patient activities; the isolation door is a sliding glass isolation door; a front door for entering the front cavity is arranged at the front end of the vehicle compartment, a rear door for entering the rear cavity is arranged at the rear end of the vehicle compartment, and a general rear door for sealing the rear end of the rear cavity is further arranged at the rear side of the vehicle compartment; a check bed is detachably arranged in the rear cavity, and a magnetic body movement mechanism is arranged beside the check bed; a console is arranged in the front cavity, and the console is connected with the magnetic body movement mechanism in control. The application is convenient for flexible and mobile gastrointestinal disease screening, realizes contactless examination, expands the working range and the beneficiary population, and can provide medical aid in special environments such as infectious diseases and wars.

The invention discloses a preparation method and application of an internal standard type core-shell magnetic nanotube. The preparation method comprises the following steps: S1, preparing an alpha-Fe2O3 nanotube; s2, preparation of a Fe3O4 nano tube is carried out; s3, preparation of Fe3O4 (at) Au-(4-MPBA) is carried out; s4, synthesis of Fe < 3 > O < 4 > (at) Au-(4-MPBA) (at) Ag (IS-DMMNTs); and S5, modifying the IS-DMMNTs by using the aptamer. According to the invention, the internal standard SERS method integrates enrichment, separation, identification and detection under the condition of keeping the original rapid and sensitive detection advantages of the method, a quantitative signal source is stabilized, the accuracy of the method is improved, and the mode of'multiple detection values of a sample 'saves the detection cost and also improves the disease field screening or detection efficiency.

The application discloses a prediction model for pulmonary arterial hypertension diagnosis and severity judgment based on pulmonary arteriography, a construction method and application. By collecting and analyzing pulmonary arteriography and test information of pulmonary arterial hypertension patients and controls, the application constructs a clinical prediction model for pulmonary arterial hypertension diagnosis and severity judgment. The diagnosis model and the severity judgment model both contain five independent prediction variables. After the model is constructed, the model is visualized through a nomogram, and the performance is evaluated through an ROC curve, a DCA curve and a calibration curve. The model is tested through a machine learning method, and the results show that the model has good performance. In addition, through correlation analysis of continuous variables, the application further constructs a linear regression equation with mean pulmonary arterial pressure as the dependent variable. The application provides a new clue for non-invasive screening of pulmonary arterial hypertension and has important significance for expanding the disease screening population and early diagnosis.

The invention relates to the technical field of feature selection and mode recognition, in particular to a special child disease screening method based on multi-granularity particle ball hypergraph feature selection. According to the method, single-granularity information is converted into multi-granularity knowledge expression, and a particle-ball hypergraph is constructed by using a complex association relationship between features so as to select features most favorable for screening diseases of special children, so that the generalization ability and the discrimination ability of a model are enhanced; meanwhile, the SVM classifier based on the optimal feature subspace is constructed to achieve screening of the special child diseases, and the screening accuracy of the special child diseases is improved; the method can more effectively adapt to the feature difference of different data sets, optimize the feature subspace, and improve the accuracy and calculation efficiency of special child disease screening.

The application discloses a primer, a probe composition for nontuberculous mycobacteria typing and application thereof, and provides a PCR primer and a probe for rapid detection and typing of 13 common NTM in clinic based on genome big data for carrying out pan-genomics analysis, and the specific genes of the common NTM in clinic are screened by using a pan-genomics method, and then typing primers and probes are designed based on the genes, so that the problems of insufficient accuracy and specificity in NTM typing are solved; the various NTM specific sequences screened guarantee the accuracy and specificity; the high sensitivity of the PCR method and the specificity of the sequences guarantee that various interference substances in the clinical samples can be avoided, and the clinical samples can be directly analyzed; the PCR detection is low in cost and low in equipment requirement, and can be carried out in primary hospitals, thereby providing an efficient and economical solution for the precise diagnosis of nontuberculous mycobacteria, the control of disease prevalence and the large-scale disease screening.

The invention discloses a health state monitoring management system for a traditional Chinese medicine AI digital health manager, and belongs to the technical field of health management. Comprising an information receiving module; a health index calculation module; a personal case library construction module; a primary disease screening module; a secondary disease screening module; and a disease diagnosis module. According to the invention, by constructing a personal case library, adopting primary and secondary grading screening mechanisms and combining with an analytic hierarchy process to dynamically set feature weights and standardize data, accurate personalized health state monitoring, efficient disease screening and diagnosis and scientific quantitative evaluation effects conforming to traditional Chinese medicine syndrome differentiation logic are achieved; the problems of insufficient utilization of personalized health data, low disease screening efficiency and unscientific health index quantitative evaluation in the prior art are solved.

The application provides a non-target detection device and system based on a nanopore and a detection method thereof, the non-target detection device and system based on a nanopore are used for non-target detection of a complex matrix through a nanopore, feature extraction is performed on a nanopore detection graph, and sample classification is realized through model construction, the detection method does not make a prior definition on a to-be-detected target, and various molecular information existing in serum is acquired in a net catching manner, non-target capture and overall state analysis of various molecules in serum are realized, and a new way of rapid and portable disease screening is provided.The application breaks through the limitation of traditional single marker detection by combining the nanopore technology and machine learning, and is expected to promote the development of precision medicine in a more efficient and comprehensive direction.

The application discloses a kind of based on visual guidance's mechanical arm mouthwash nursing, breath detection and holographic diagnosis and treatment auxiliary system and method, it is related to stomatology field, including: main control module, auxiliary mechanical arm module, gas detection module, visual sensing module, diagnosis and treatment intelligent body interaction module, audio and video acquisition communication module with main control module electricity connection, and multi-modal mobile nursing terminal.The application realizes the pose control of mechanical arm and the imaging visual angle adjustment of diagnosis and treatment intelligent body by visual sensing system, integrates supine position automatic mouthwash nursing, exhalation component disease screening, doctor-patient / holographic visualization communication, full-process audio and video diagnosis and treatment support, non-standard oral speech driven standard medical record automatic generation, automatically collects non-standard oral speech content in diagnosis and treatment full process, generates standardization medical record meeting medical quality control requirement, greatly improves diagnosis and treatment efficiency, patient comfort and diagnosis and treatment standardization, and can be widely integrated in dental treatment chair.

The application discloses a kind of multi-disease prediction systems based on binocular fundus image, including fundus feature extraction module, similarity-difference feature fusion module, clinical feature extraction module, multi-modal feature fusion module and multi-disease classification prediction module.System uses unified backbone network to extract left and right fundus image features, by similarity-difference feature fusion module, the features of two-way are added to each element to obtain preliminary fusion features, and the absolute value is obtained by subtracting each element to obtain different features, after normalization and 1 complement operation generates dissimilarity weight and similarity weight, separate out dissimilarity information and similarity information after weighting fusion according to hyperparameter α Fusion fundus feature is obtained;Multi-dimensional clinical variable features are extracted by two fully connected layers, after fundus features and clinical features are spliced, input multi-disease classification prediction module, and output disease prevalence probability.The application makes full use of the correlation and complementarity of binocular fundus image, and realizes high-precision, low-cost multi-disease screening combined with clinical indicators.

The invention relates to the technical field of data processing, and discloses a mild cognitive impairment detection system based on multi-level data, and the system comprises a terminal device which is used for collecting physiological monitoring data, clinical narrative data and multi-modal test data of a user; the data processing module is used for performing interference identification and information purification on the multi-modal test data to generate a purified cognitive response matrix; the cognitive evaluation module is used for performing cognitive ability decoupling and group benchmarking based on the purified cognitive response matrix and outputting an individual ability level; and the closed-loop management module is used for constructing a longitudinal decline slope model based on a score sequence of individual historical multiple evaluations and carrying out risk early warning and intervention recommendation. According to the method, passive disease screening of cognitive health management is converted into an active, personalized and continuous risk monitoring and intervention process, and a powerful technical tool is provided for delaying cognitive decline.

The invention discloses application of a prostatic hyperplasia related marker in preparation of a product for detecting prostatic hyperplasia. The prostatic hyperplasia related marker is selected from at least one of MINP1, ACTBL and APOC4 protein. In the technical scheme provided by the invention, MINP1, ACTBL and APOC4 are combined as the BPH detection marker, complementarity of the MINP1, ACTBL and APOC4 in different stages of diseases can be fully exerted, and the comprehensive efficiency of a detection product is remarkably improved. Wherein MINP1 and ACTBL participate in disease progression by regulating prostate cell proliferation and apoptosis, APOC4 can present significant expression change in blood cells of an early-stage BPH patient, and BPH disease screening can be accurately carried out. When the three markers are synergistically applied, early screening of BPH can be realized, lesion activity and severity can be evaluated, a detection system covering the whole course of disease occurrence and development is formed, the limitation of a single marker on the diagnosis dimension is effectively made up, and the comprehensiveness and accuracy of BPH detection are greatly improved.

The invention provides a diabetic foot data inspection and early screening method and system based on an AI model. Belongs to the technical field of disease screening. The method comprises the steps that diabetic foot data of a patient are collected in real time through multi-modal data collection equipment, and the collected diabetic foot data are transmitted into an edge computing network to be processed; the processed diabetic foot data are transmitted into a cloud space, a multi-modal AI model is constructed through a convolutional neural network, and key features are extracted from the diabetic foot data based on the multi-modal AI model. The edge computing network can reduce cloud burden and accelerate data processing speed by processing data, and cloud computing is responsible for training and optimization of a deep learning model, so that advanced analysis of data is realized, and prediction precision is improved.

The invention provides an anterior segment transmission interference microscopic imaging device, method and system, and relates to the technical field of medical optical imaging and ophthalmic diagnosis. The single-frame imaging field of view is not less than 2mm * 1mm and is larger than that of a common clinical confocal microscope and a corneal endothelial mirror, so that the statistical reliability and the lesion detection rate can be improved; through a transmission interference mechanism, corneal nerves, epithelial cells and crystalline lens fine structures can be displayed in a high contrast manner, and the method is particularly suitable for low-scattering transparent tissues; a common-path optical structure is adopted, a reference arm or a high-speed scanning unit is not needed, and the system is small in size, low in cost and good in stability; in the examination process, contact with the ocular surface or introduction of a contrast agent is not needed, and discomfort and risks possibly caused by traditional contact type or intrusive type examination are avoided; the method can be used for corneal nerve evaluation, endothelial disease screening, crystalline lens microstructure observation and preoperative / postoperative follow-up visit.