19 results about "Gastric antrum" patented technology

The invention relates to the technical field of medical decision and artificial intelligence, and discloses an artificial intelligence-driven new adjuvant therapy scheme optimization method and system for antral sinus cancer, and the method comprises the steps: S1, obtaining clinical data of a antral sinus cancer patient, and carrying out the preprocessing; s2, screening core features by adopting an iterative ReliefF algorithm, and constructing composite features; s3, constructing a prediction model based on a Bayesian optimization K-nearest neighbor algorithm, taking the composite features as input of the prediction model to predict treatment response, and outputting individual tumor recession grade probability distribution of the patient; s4, taking the tumor recession grade probability distribution as a core component of a state space, inputting the tumor recession grade probability distribution into a double-layer reinforcement learning framework based on a penalty mechanism to optimize a treatment decision, and outputting a personalized treatment strategy parameter set; and S5, performing real-time scanning and verification on the drug dosage parameters in the strategy parameter set, and outputting a final approved personalized treatment scheme. The precision and intelligent level of gastric antrum cancer treatment is improved.

The utility model discloses a mould is placed under the mould of simulation ultrasonic real -time guidance of nasal -intestinal tube, including the digestive tract simulation body of simulation esophagus, stomach, duodenum and jejunum, it adopts the flexible material of ultrasonic wave penetration, front surface is not transparent, back surface is transparent, and its inside esophagus is used for filling air, and the part except esophagus is used for filling liquid, the digestive tract simulation body is fixed between two organic glass, front surface is not transparent, back surface is transparent, the organic glass of front surface opens at esophagus place, antrum gastricum place respectively, as the acoustic window of ultrasonic, is used for the inflation sign of esophagus and antrum gastricum nasal -intestinal tube catheter tip cloud sign of ultrasonic observation when placing nasal -intestinal tube respectively, is provided with many resistance positions at stomach and duodenum, for making nasal -intestinal tube catheter tip forward into the obstruction. The utility model can simulate the sign of ultrasonic under different conditions on the mould, effectively simulates the actual entering process of nasal -intestinal tube under the real -time guidance of ultrasonic, and facilitates simulation teaching demonstration.

The embodiment of the invention discloses an intelligent recognition and grading method for gastric intestinal metaplasia, and the method comprises the steps: collecting a gastric mucosa HE staining digital pathological image, and building a universal part recognition model through a convolutional neural network according to the pathological classification standards of different parts of a stomach body, gastric antrum and gastric horn. In order to reduce judgment errors caused by other glands, a semantic segmentation model is trained, and the gastric mucosa is accurately found out. On the basis, a developed high-precision detection and recognition algorithm is used for recognizing the enteric glands, and quantitative determination is carried out on the enteric glands in the gastric mucosa of the stomach body, the antrum and the corner of the stomach. And the identified target glands are automatically counted, and intelligent OLGIM grading is carried out.

The utility model discloses a kind of cutting gland cutting gizzard integrated machine processing, cutting gland cutting gizzard integrated machine processing's chicken gizzard system includes chicken gizzard, gland and gizzard. Cutting gland cutting gizzard integrated machine includes rack, the front end of rack is provided with feed hopper assembly, feed hopper assembly includes feed hopper and the inclination angle adjusting mechanism of adjusting feed hopper inclination angle. Along the length direction of rack, conveying device is provided on rack, the rear end of conveying device is also provided with clamping gizzard device, conveying device and clamping gizzard device partially overlap;From front to back, cutting gizzard device and cutting gland device are also sequentially provided on rack;Gland discharge channel is also provided on rack, gland discharge channel is located below cutting gland device;Chicken gizzard discharge channel is also provided on rack, chicken gizzard discharge channel is located below clamping gizzard device, the inclination angle of the feed hopper of cutting gland cutting gizzard integrated machine of the utility model can be adjusted, to further can better cooperate with hoist work, reduce feeding difficulty, improve production efficiency, to further improve the economic benefit of enterprise.

The application discloses a multi-task autoimmune gastritis gastroscope image classification method, equipment and medium, and the method comprises the following steps: acquiring a plurality of gastroscope images of a patient, registering all the gastroscope images to obtain a similarity matrix; clustering the gastroscope images according to the similarity matrix to group the gastroscope images and obtain a plurality of gastroscope image groups; inputting each gastroscope image group into a first classification network and a second classification network respectively to obtain a first classification label and a second classification label, and obtaining a (first classification label, second classification label) result pair corresponding to the current gastroscope image group; wherein the first classification label comprises a gastric anatomical site including a fundus, a body and an antrum, and the second classification label comprises a lesion condition including normal and atrophy. The application takes into account the requirements of large receptive field, local fine-grained information and induction bias in anatomical region classification and lesion condition classification, and accurately classifies the gastroscope images.

The present disclosure relates to a method for analyzing a gastric endoscopic image. The method includes: acquiring a gastric antrum endoscopic image, a gastric body endoscopic image, and a gastric cardia endoscopic image; inputting these images into a scaling feature fusion module which contains multiple shared-weights scaling sub-networks, each having a different field of view and outputting scale features of the antrum, body, and cardia, respectively; concatenating the scale features of the same section to obtain cross-view features of the antrum, body, and cardia; and inputting these cross-view features into a section correlation module to concatenate at least two features and generate a corpus-predominant gastritis index through a classifier.

The present invention belongs to the field of medical imaging and relates to a method for gastric antrum identification and gastric antrum motility index calculation based on deep learning, comprising: collecting a standard gastric antrum contraction ultrasound video of a preset time length; the standard gastric antrum contraction ultrasound video refers to a video of the gastric antrum under a standard gastric antrum slice obtained by ultrasound; performing fuzzy removal processing on the standard gastric antrum contraction ultrasound video to obtain a first gastric antrum frame image sequence; performing artifact suppression processing on the first gastric antrum frame image sequence to obtain a second gastric antrum frame image sequence; tracking the gastric antrum area in the second gastric antrum frame image sequence to obtain a gastric antrum binary segmentation image; and calculating indicators such as the gastric antrum motility index and number of contractions of the object to be detected in the current state under bedside ultrasound images based on a deep learning algorithm system.

The present application relates to the technical field of medical decision and artificial intelligence, and discloses an artificial intelligence driven method and system for optimizing a neoadjuvant therapy scheme for gastric antrum cancer, comprising: S1: obtaining clinical data of a patient with gastric antrum cancer and preprocessing; S2: screening core features and constructing compound features by using an iterative ReliefF algorithm; S3: constructing a prediction model based on a K nearest neighbor algorithm of Bayesian optimization, and taking the compound features as inputs of the prediction model to predict a treatment response, and outputting a tumor regression grade probability distribution of the patient; S4: taking the tumor regression grade probability distribution as a core component of a state space, inputting into a double-layer reinforcement learning framework based on a punishment mechanism to optimize a treatment decision, and outputting a set of personalized treatment strategy parameters; and S5: performing real-time scanning and checking on drug dosage parameters in the set of strategy parameters, and outputting a final approved personalized treatment scheme. The present application improves the precision and intelligence level of gastric antrum cancer treatment.

The present application relates to the field of intelligent medical treatment and artificial intelligence, and discloses a preoperative precise evaluation system for gastric antrum cancer with pyloric obstruction based on AI image recognition, which comprises: a data acquisition and preprocessing module, which acquires structured clinical data and multi-modal image data of a patient and performs preprocessing; a lesion identification and segmentation module, which performs lesion identification and segmentation processing on the preprocessed image data by using an improved nnU-Net framework; a feature extraction and screening module, which extracts image features from the segmentation result and screens out features related to clinical indicators; a multi-modal feature fusion module, which performs feature fusion on the screened image features and the preprocessed structured clinical data; a multi-task risk evaluation module, which establishes a multi-parameter risk prediction model by using a semi-structured distribution regression method and outputs multi-task prediction results; and an evaluation report generation module, which generates a structured evaluation report. The present application realizes intelligent and precise preoperative evaluation and postoperative risk prediction for gastric antrum cancer patients.

The application relates to an automatic device for simultaneously pulling a liver and a stomach during a laparoscopic pancreas operation. The device is simple to install and can be adjusted according to the situation during the operation. The gas bag device can prevent the liver from being sharply cut and bleeding during the pulling process. The two elastic pulling belts can help pull most of the tissues including the gastric antrum and the gastric body upwards and effectively protect the blood supply of the stomach. The pancreas can be fully exposed through the pulling device, so that the hands of the surgeon are greatly liberated, and the pancreas operation is more delicate and comfortable.

This invention relates to an enteral nutrition infusion flow rate monitor with an automatic flushing tube, comprising at least: a monitoring module for real-time monitoring of gastric antrum parameters; and a flow rate control component for graded adjustment of the enteral nutrition infusion flow rate. The flow rate monitor also includes a control mechanism, the flow rate control component and the monitoring module being electrically connected to the control mechanism. The control mechanism is capable of controlling the flow rate control component to adjust the enteral nutrition infusion flow rate based at least on the cross-sectional area of ​​the gastric antrum monitored by the monitoring module.

The invention discloses a gastric emptying parameter detection system for gastric ultrasonic examination, and belongs to the field of medical images. The gastric emptying parameter detection system for the gastric ultrasonic examination comprises an ultrasonic image acquisition module, an image processing module and a gastric emptying parameter calculation module. The problem that an existing gastric emptying function detection method has limitation is solved, standard section image acquisition can be accurately carried out on the antral sinus part before and after each drinking node through the image acquisition module, and the image processing module extracts and draws gastral cavity boundaries in a time sequence ultrasonic image through an image segmentation algorithm; the gastric emptying parameter calculation module calculates gastric emptying parameters including initial emptying time, emptying curve slope, semi-emptying time, emptying rate at a specific time point and the like based on gastral cavity area data of a time sequence, and the gastric emptying parameter calculation module calculates the gastric emptying parameters including the initial emptying time, the emptying curve slope, the semi-emptying time, the emptying rate at the specific time point and the like based on the gastric cavity area data of the time sequence, so that the accuracy and reliability of gastric emptying area measurement are improved. And objective evaluation of the gastric emptying function can be realized.

The invention discloses an ultrasound-based antral sinus state detection method and device. The method comprises the following steps: acquiring an antral sinus ultrasound image of a target object; the antral sinus ultrasonic image is recognized, and the antral sinus state of the antral sinus ultrasonic image is obtained through recognition; segmenting the antral ultrasound image to obtain an antral normal area of the antral ultrasound image; determining the area of the normal gastric antrum region according to the normal gastric antrum region obtained through segmentation; and according to the antral state and the area of the antral normal region, determining a mistaken aspiration risk level. The functions of gastric antrum state evaluation, gastric antrum normal region area calculation, gastric antrum lesion type and lesion level evaluation, mistaken aspiration risk evaluation and the like can be realized by performing identification, segmentation and classification processing on the gastric antrum ultrasonic image of the target object; the accuracy and reliability of a detection result are improved, the medical cost is reduced, the repeatability is improved, and a more efficient, safer and more accurate gastric antrum detection and evaluation method is provided for clinic.

The invention discloses a digestive endoscopic ESD (Electro-Static Discharge) operation simulation training device which comprises an outer box and a stomach model located in the outer box, an inner shell used for lifting the stomach model is arranged in the outer box, a creeping simulation mechanism is arranged on the inner shell, and the creeping simulation mechanism comprises a plurality of capsule bodies located on the outer side of the inner shell and a driving assembly located in the inner shell; the driving assembly comprises a power module and a plurality of pushing and injecting modules corresponding to the bags, and the power module controls the bags to shrink and expand in sequence by sequentially driving the pushing and injecting modules to pump and inject the pressure transmitting media. According to the invention, a plurality of bag bodies are arranged at the great bending part of the stomach of the stomach model, and the bag bodies are sequentially filled with and extracted back from the pressure transmitting medium, so that directional peristaltic waves spread from the upper part of the stomach to the antral antrum can be manufactured by utilizing the sequential expansion and contraction of the bag bodies, and the peristaltic condition of the stomach of a human body is simulated; therefore, a more real and dynamic training scene can be provided, and trainees can obtain a more sufficient and robust training effect.

The present invention relates to the field of disease prediction, and in particular, to a gastrointestinal electrical signal-based diabetes and hypertension prediction system and a construction method. The prediction system provided by the present invention comprises: a database, used for storing gastrointestinal electrical signal data, comprising the postprandial coupling percentage at the lesser curvature lead, the postprandial electrical dysrhythmia percentage at the gastric antrum lead, the preprandial dominant frequency at the lesser curvature lead, the postprandial average waveform frequency at the ascending colon lead, the postprandial normal slow wave percentage at the ascending colon lead, the postprandial coupling percentage at the descending colon lead, the postprandial average waveform frequency at the rectal lead, the preprandial average waveform frequency at the transverse colon lead, and the preprandial lead time difference at the rectal lead; and a prediction module, used for predicting a probability of the occurrence of diabetes and hypertension in a subject. Further provided in the present invention is a construction method for the prediction system. The present invention is adapted to performing diabetes and hypertension risk prediction and screening work in rural or community populations.

The application discloses an ultrasonic-based gastric wall peristalsis detection method and device, equipment and storage medium, relates to the technical field of ultrasonic imaging, and the method ensures high space-time resolution of original data by continuously collecting dynamic echo signals of the transverse plane of the anterior wall of the gastric antrum through a high-frame-rate ultrasonic probe; then, the difference between adjacent frames of images is analyzed by adopting a phase tracking and cross-correlation algorithm, and gastric wall movement is converted into a quantifiable discrete displacement vector field; in the next step, the discrete displacement field is fitted into a continuous propagation trajectory, and the propagation process of peristaltic waves from the gastric body to the pylorus is intuitively presented; secondly, key kinetic parameters such as propagation speed and peristaltic intensity are extracted from the trajectory by a multi-scale feature fusion algorithm, and the leap from qualitative observation to quantitative evaluation is realized; finally, the fusion visualization of vector arrows and color coding simultaneously presents three-dimensional information of the propagation direction, speed and intensity of peristaltic waves, and the detection precision of gastric wall peristalsis is relatively obviously improved.

The embodiment of the invention relates to a method and a device for predicting focal areas of gastric antrum and gastroesophagus. The method comprises the following steps: constructing a first segmentation model and a second segmentation model; constructing a first data set to train a first segmentation model for training; after the training is finished, constructing a second data set based on the first data set and the first segmentation model, and training a second segmentation model based on the second data set; and after training is finished, on the basis of the first segmentation model and the second segmentation model, gastric antrum and gastroesophagus focus area prediction is carried out on the stomach CT image. According to the invention, the focus area prediction accuracy can be improved.

The invention discloses a gastric antrum peristalsis image processing method and system based on a double-lens gastroscope capsule. The method comprises the following steps: acquiring a gastric antrum video sequence shot by the double-lens gastroscope capsule in the stomach of a human body; the double lenses are a first lens and a second lens which are arranged at two axial ends of the gastroscope capsule and are used for respectively acquiring images of a forward view angle and a backward view angle of the capsule; performing motion information processing on the video sequences from the first shot and the second shot to obtain motion features representing gastric wall deformation or image displacement; based on the motion features of the first lens and the second lens, consistency analysis is carried out on the motion directions, change trends and periodic features of the first lens and the second lens in the same time period, and when the motion features of the two lenses meet a preset consistency condition, it is judged that a gastric antrum wriggle event exists in the corresponding time period; and judging time sequence integrity and outputting a result. The accuracy and stability of gastric antrum peristalsis image recognition are improved.

This invention relates to the field of medical monitoring technology, and discloses a system and method for monitoring enteral nutrition in critically ill patients. The method includes: acquiring dynamic images of the gastric antrum region of critically ill patients, identifying the contraction frequency and amplitude of the gastric antrum to calculate the gastric emptying index; calculating the intestinal transport efficiency of critically ill patients using intestinal peristalsis data and the gastric emptying index; constructing an enteral nutrition delivery protocol for critically ill patients, and collecting transport conversion data of the critically ill patients under the nutrition delivery protocol; analyzing the enteral nutritional tolerance of critically ill patients, collecting metabolic data and immune status of critically ill patients after the nutrition delivery protocol, and constructing a target nutrition delivery protocol; and monitoring the enteral nutritional status of critically ill patients in real time under the target nutrition delivery protocol to obtain an enteral nutrition monitoring report. This invention can improve the comprehensiveness of enteral nutrition monitoring for critically ill patients, thereby enabling the construction of optimized nutritional combinations.