94 results about "Bioelectric Impedance" patented technology

The invention discloses a pain management method based on bioelectrical impedance data and a machine learning algorithm, and relates to the technical field of medical health, and the method comprises the steps: carrying out the phase delay analysis of an impedance phase spectrum matrix, calculating the change rate of a phase angle of each frequency point along with time, and obtaining the maximum phase lag time; according to the temperature field sequence and the maximum phase lag time, time-space registration is carried out through a multi-modal data fusion method and impedance modulus changes of the corresponding areas, and a pain biomarker feature library is generated; through a bidirectional LSTM and a cross attention fusion method, time sequence modeling and cross-modal correlation analysis are carried out on bioelectricity signal characteristics extracted by a sliding window and historical pain data, a dynamic evolution rule of pain development is captured, a pain management triple containing pain intensity, intervention response and prognosis evaluation is generated, and a pain management result is obtained. And a quantitative basis is provided for personalized treatment.

The invention relates to the technical field of AI intelligent management, and discloses an AI-based body weight intelligent management method and system, and the method comprises the steps: carrying out the bioelectrical impedance signal collection of M measurement nodes of a target user through an Internet of Things front-end device, and obtaining a multi-node impedance signal set; biological impedance space-time atlas analysis is executed through the cloud processing platform, and human body composition feature data are obtained; aI analysis is carried out through a Deepseek multi-expert model of the cloud processing platform to obtain a weight management analysis result of each professional field; weight fusion and causal inference are carried out through the cloud processing platform in combination with weight management analysis results of all professional fields and doctor review results, and a personalized weight reduction management strategy is generated; the personalized weight reduction management strategy is received through the mobile terminal, dynamic adjustment is carried out in combination with daily execution feedback data of the target user, an intelligent weight management guidance scheme is generated, and the method improves the management effect of AI weight intelligent management and user experience.

The invention relates to a multi-frequency bioelectrical impedance-based body fat accurate measurement method and system. The method comprises the following steps: receiving a mode selection instruction sent by a user to determine a current working mode; if the mode is a rapid measurement mode, applying single reference frequency and reference current intensity excitation, obtaining a single group of bioelectrical impedance data, and calculating a rapid body fat rate result; if the measurement mode is an accurate measurement mode, acquiring bioelectrical impedance data under a plurality of preset frequencies and a plurality of preset current intensities through a complete electrode system, performing signal processing on the data, extracting impedance amplitude and phase information, constructing an impedance characteristic curve, extracting characteristic parameters related to the body fat rate from the impedance characteristic curve, and calculating the body fat rate based on the characteristic parameters. And obtaining a final body fat rate result of the accurate measurement mode. According to the method, the body fat rate can be accurately measured, and the accuracy and reliability of a measurement result are improved.

The invention discloses an Internet of Things equipment binding method and system based on AI behavior data, and relates to the technical field of Internet of Things, and the method comprises the steps: defining Internet of Things equipment as nodes, defining a space-time interaction feature set as node attributes, obtaining the weights of edges between the nodes through calculating the behavior relevance, completing the construction of a behavior relevance topological graph, and obtaining a behavior relevance topological graph; and verifying the behavior consistency of the new equipment by using the trained GNN through the behavior association topological graph, obtaining the legality probability of the new equipment, setting a trigger threshold value, collecting implicit bioelectrical impedance data of the new equipment when the legality probability of the new equipment is smaller than the trigger threshold value, performing cross-modal feature fusion, generating a user behavior identification vector, and performing user behavior identification on the user behavior identification vector. Performing final verification to obtain a final verification result of the new equipment; according to the method, the mining of the deep behavior relationship is realized, and the limitation of a static topological structure on the mining of a dynamic interaction mode is solved.

The invention relates to the technical field of medical data management and analysis, in particular to a lymphedema treatment monitoring data management method and system. Comprising the following steps: synchronously acquiring bioelectrical impedance, tissue thermodynamic characteristics and subcutaneous tissue morphological data of a patient through multi-modal data acquisition equipment; transmitting the data to a cloud processing platform in a distributed manner, and generating a three-dimensional illness state assessment map containing edema quantitative indexes through a machine learning assessment model; generating a personalized treatment strategy adjustment scheme with a confidence score based on dynamic comparison between the current evaluation map and historical data; feeding back the evaluation map and the adjustment scheme to the medical staff through the interaction terminal, and receiving a clinical correction instruction; according to the method, on the premise of strictly protecting patient data privacy, cross-mechanism multi-modal knowledge sharing can be realized, and the contradiction between a data island and a model effect is solved.

A pet body fat rate measuring system disclosed by the present application comprises a body fat scale, a host and a body fat rate calculation module, the body fat scale is provided with a pet three-dimensional measuring device, a bioelectrical impedance measuring device and a weight measuring device, and the pet three-dimensional measuring device comprises a body width measuring scale, a body length measuring scale and a shoulder height measuring scale which are arranged in a relatively sliding manner. A capacitive grating displacement measuring device is formed; the body fat rate calculation module is provided with a pet body fat rate calculation model, the model is constructed through multiple groups of body shape data, electrical impedance data and body fat rate data of pets of different varieties, and the body fat rate calculation module obtains feature data, body shape data and electrical impedance data of the current pet; and calculating the body fat rate of the current pet by using the pet body fat rate calculation model. According to the utility model, the body fat scale is utilized to automatically measure and obtain the body shape data and the electrical impedance data of the pet, then the feature data of the pet and the pet body fat rate calculation model are combined to calculate and obtain the current body fat rate of the pet, the use is convenient, and the accuracy is high.

The invention provides a multi-frequency biological resistance antibody component prediction method and device and a storage medium, and relates to the technical field of biomedical signal processing. According to the method, multi-frequency current testing is carried out on different physiological segments of a subject, an initial training data set is constructed in combination with basic information of the subject, the fine capturing ability of the multi-frequency bioelectrical impedance technology on human body physiological information is brought into full play, and individual features are fused to improve data dimensions and relevance; the quality of training data is guaranteed through data preprocessing, and a reliable foundation is laid for model training; and a component prediction model is constructed and optimized by a multi-task regression loss function, so that volume component prediction indexes can be efficiently output, and the prediction accuracy and stability of different indexes can be improved. According to the method, the problems of single data dimension and insufficient model precision in traditional volume component prediction are effectively solved, and a systematic and efficient scheme is provided for precise volume component prediction.

This application provides a bioelectrical impedance measurement method, circuit, chip, and electronic device. The method includes: obtaining a first contact impedance value corresponding to a first excitation electrode and a second contact impedance value corresponding to a second excitation electrode through a preset method; determining the transmission direction of an excitation current between the first and second excitation electrodes based on the first and second contact impedance values; controlling the excitation current to transmit between the first and second excitation electrodes according to the transmission direction; determining a target voltage value between a first and second measuring electrode, and calculating the impedance of the measured object based on the target voltage value; wherein the first and second contact impedance values ​​are different. This application can reduce the influence of contact impedance on the bioelectrical impedance measurement results, reduce the measurement error of bioelectrical impedance, and improve the accuracy of bioelectrical impedance measurement.

The invention relates to the technical field of intelligent body fat measurement, and provides a body composition parameter adaptive calibration method and system based on an intelligent body fat scale. The method comprises the following steps: acquiring real-time weight data and real-time bioelectrical impedance signals of a user and real-time temperature and humidity data of a current environment; based on a preset bioelectrical impedance model, in combination with the real-time weight data, the real-time bioelectrical impedance signal and the real-time temperature and humidity data, initial body composition parameters are calculated and obtained; performing feature extraction on the calibration training data set according to a preset adaptive calibration algorithm to construct a real-time calibration model according to the adaptive calibration algorithm; and inputting the currently collected real-time weight data, the real-time bioelectrical impedance signal and the real-time environmental parameters to a real-time calibration model, outputting calibration correction parameters, and performing adaptive calibration on the initial human body composition parameters according to the calibration correction parameters to obtain target human body composition parameters. According to the invention, a systematic solution is provided for accurate measurement of the intelligent body fat scale.

The invention discloses an intelligent biological eardrum based on individualized 3D printing and a preparation method thereof, and belongs to the technical field of medical instruments, and the eardrum comprises an individualized eardrum substrate, a PEDOT: PSS flexible electrode array integrated on the inner surface of the substrate, a multi-frequency bioelectrical impedance detection module, a VCT module and a central processing unit. Through multi-frequency bioelectrical impedance detection and VCT imaging, an auricle conductivity distribution map can be reconstructed, a nerve active area can be accurately identified, and personalized transcutaneous nerve electrical stimulation parameters can be generated. The invention further provides a corresponding intelligent nerve regulation and control system, a preparation method and a parameter adjustment method based on physiological signal feedback, an integrated solution from individualized manufacturing, precise recognition to self-adaptive closed-loop regulation and control is achieved, and the intelligent nerve regulation and control system is particularly suitable for individualized nerve regulation and control treatment of irritable bowel syndrome and other diseases; the problems of low stimulation precision, lack of individuation, poor comfort, no closed-loop feedback and the like in the prior art are effectively solved.

The invention is suitable for the technical field of biological measurement, and particularly relates to an intelligent body fat scale and a multi-frequency bioelectrical impedance measurement data fusion method thereof, and the method comprises the steps: carrying out the multi-frequency bioelectrical impedance measurement of a target measurement object, and obtaining a plurality of groups of bioelectrical impedance data sequences under different frequency bands; performing feature analysis on each bioelectrical impedance data sequence, determining a data analysis result corresponding to each frequency band, and determining data reliability corresponding to each frequency band according to the data analysis result; the fusion weight corresponding to each frequency band is determined based on the data reliability, and the limitation of a traditional fixed weight or simple average fusion method is overcome; and according to the fusion weight corresponding to each frequency band, weighting adjustment is performed on the corresponding bioelectrical impedance data sequence to obtain the adjustment data, and fusion processing is performed based on each adjustment data to obtain the measurement data, so that the technical advantages of multi-frequency measurement are fully exerted, and the overall competitiveness of the body fat scale product is enhanced.

The invention discloses a bioelectrical impedance tumor identification method based on multi-modal fusion. The method comprises the following steps: S1, acquiring electrical impedance signal data and tissue slice image data of a tumor sample; the tissue slice image data are pathological slice images corresponding to tumor tissues; s2, inputting the electrical impedance signal data and the tissue slice image data into a multi-modal feature coding module for asymmetric feature coding; s3, performing splicing and weighted fusion on the global semantic feature vector and the image semantic feature vector to form a multi-modal feature vector; and S4, inputting the multi-modal feature vector into a decoding network for classification prediction, and outputting a tumor identification result. According to the method, an asymmetric coding structure and a weighted fusion mechanism are constructed by fusing the electric signal modality and the image modality, so that the utilization efficiency and the feature discrimination capability of multi-modal information can be effectively improved, and the accuracy and the reliability of tumor recognition are remarkably improved.

The invention provides a physical weeding method and system based on double closed-loop feedback, and belongs to the technical field of intelligent agriculture. The method comprises the steps that before electroporation operation is executed, a target effectiveness index is calculated through first-time bioelectrical impedance measurement, and accurate contact with weed meristem targets is ensured in a closed-loop feedback mode; after the contact is effective, generating optimal electric pulse parameters in a self-adaptive manner according to physiological characteristics of weeds, and executing operation; after operation, the irreversible cracking index of the cells is calculated through subsequent bioelectrical impedance measurement, the operation effect is quantitatively evaluated, and supplementary strike is triggered. The problems that existing physical weeding is low in precision, high in energy consumption and unreliable in effect are solved through double closed loops of verification before contact and confirmation after effect in combination with an adaptive energy strategy, and the precision, reliability and energy utilization efficiency of operation are remarkably improved.

The invention relates to the technical field of medical instruments and clinical nursing, in particular to a radial artery puncture navigation system based on multi-modal sensing and digital projection, which comprises the following steps: a multi-modal blood vessel signal acquisition module, which is used for acquiring a blood vessel pulsation signal on the wrist of a patient through a photoplethysmography sensor array, the strongest pulsation line is positioned through the pressure sensor array, and the blood vessel depth and artery and vein distinguishing information are obtained through the bioelectrical impedance sensor array. According to the invention, through multi-modal blood vessel signal acquisition, blood vessel dynamic deformation modeling, personalized digital twinning construction and dynamic updating, projection path generation and control, and projection output and real-time error compensation, real-time visual projection of a blood vessel center line and local deformation is realized; therefore, the problem that puncture positioning is not accurate due to the fact that dynamic changes of blood vessels cannot be accurately reflected due to the fact that most traditional radial artery puncture adopts a hand feeling positioning method is solved.

Provided is a system that more accurately estimates the degree of urine accumulation by solving a problem in which the influence of a change in the amount of urine accumulation is obscured when the biological impedance in the vicinity of the bladder is affected as the muscles contract. This urine accumulation degree estimation system (2) comprises: a measurement means for measuring at least two types of biological impedance by energizing a bladder (60) at at least two different frequencies; and an estimation means that estimates the urine accumulation degree of the bladder (60) on the basis of the at least two types of biological impedance. The at least two frequencies include a frequency at which the bladder (60) and the muscles (66) around the bladder can be energized, and a frequency at which the muscles (66) around the bladder can be energized.

The invention provides an intelligent push-up device which comprises a base provided with a first bearing part and a second bearing part, and electrode plates are arranged on the first bearing part and the second bearing part respectively; the bioelectrical impedance acquisition module has a first acquisition frequency and a second acquisition frequency, and first dynamic electrical impedance data of a target object in a motion state and first static electrical impedance data of the target object in a static state are acquired at the first acquisition frequency; the second acquisition frequency acquires second dynamic electrical impedance data of the target object in a moving state and second static electrical impedance data of the target object in a static state; and the data processing module is connected with the bioelectrical impedance acquisition module and is used for evaluating the muscle amount of the target object according to the first static electrical impedance data, the second static electrical impedance data and the height information and the weight information of the target object and determining muscle contraction information according to the first dynamic electrical impedance data and the second dynamic electrical impedance data.

The invention discloses a biological high-frequency electrical impedance characteristic real-time acquisition method and system, and the method comprises the steps: transmitting a high-frequency excitation signal to a first electrode pair through a coaxial shielding line, and obtaining a voltage signal U0 of a sampling resistor R0 connected in series with a signal transmission loop of the first electrode pair; a signal transmission loop of the electrode pair I is connected with a contact resistor RL in series to form an RC filter circuit together with the coaxial shielding line, and passive RC compensation is arranged between the electrode pair I and the living body; the voltage UZ on the impedance of the living body is obtained by the coaxial shielding line through the electrode pair II, and passive RC compensation is arranged between the electrode pair II and the living body; according to the sampling resistance R0, the voltage U0 and the voltage UZ, the bioelectrical impedance value RZ of the to-be-measured organism is calculated.

The invention discloses a portable human heart imaging hardware system based on bioelectrical impedance. The system comprises an excitation signal processing module, a detection signal conditioning module and a digital processing module. The excitation signal processing module adopts a DAC (Digital-to-Analog Converter) to generate an excitation signal, and after voltage amplification and band-pass filtering, the excitation signal drives an excitation electrode through an analog switch; the detection signal conditioning module switches a measuring electrode through an analog switch, a programmable instrumentation amplifier is combined with an RDAC digital potentiometer to realize self-adaptive gain adjustment of a detection signal, flexible gain of the signal is realized, and the signal is sent to an ADC after being filtered; the digital processing module carries out digital demodulation on the collected signals, extracts amplitude and phase information and then transmits the amplitude and phase information to an upper computer for image reconstruction. The flexible signal gain mechanism of the system realizes a full-custom signal gain curve, breaks through the fixed magnification gain limitation of the traditional integrated PGA, adapts to the impedance change of different individuals and experimental media, and improves the signal-to-noise ratio of the system, thereby obtaining more accurate cardiac impedance acquisition data.

This application relates to the field of intelligent body fat measurement technology, and provides a method and system for adaptive calibration of body composition parameters based on an intelligent body fat scale. The method includes: acquiring real-time user weight data, real-time bioelectrical impedance signal, and real-time temperature and humidity data of the current environment; calculating initial body composition parameters based on a preset bioelectrical impedance model, combined with the real-time weight data, real-time bioelectrical impedance signal, and real-time temperature and humidity data; extracting features from the calibration training dataset according to a preset adaptive calibration algorithm to construct a real-time calibration model; inputting the currently collected real-time weight data, real-time bioelectrical impedance signal, and real-time environmental parameters into the real-time calibration model, outputting calibration correction parameters, and adaptively calibrating the initial body composition parameters according to the calibration correction parameters to obtain the target body composition parameters. This application provides a systematic solution for the accurate measurement of body fat by intelligent body fat scales.

This invention discloses a method for measuring cranial thickness based on bioelectrical impedance analysis (BIA), comprising: dividing the target region into subdivisions using the finite element method (FEM) to establish a finite element model; acquiring surface voltage signals of the skull using a four-electrode method on a brain electrical impedance imaging platform; obtaining the conductivity distribution of each node of the triangular element by solving the inverse voltage problem; inputting the conductivity distribution into the finite element model and calculating the distance between the node with the largest resistivity change and the corresponding boundary voltage node by solving the forward problem, thus obtaining the skull width at a set of signal locations; measuring the node voltages and calculating the corresponding skull thickness using a sixteen-electrode cyclic excitation method; and finally, assembling all node voltages and skull thicknesses into a plane to obtain the overall skull thickness. This invention designs a method for measuring cranial thickness based on bioelectrical impedance analysis (BIA), providing an accurate method for calculating cranial thickness.

The present invention discloses a waist circumference measuring instrument based on bioelectrical impedance and a method thereof. First, a waist circumference measurement model based on a BP neural network is constructed. The input vectors of the input layer of the model include height, weight, age, and abdominal impedance values ​​at three different frequencies; the output vectors of the output layer include waist circumference; and the waist circumference measurement model based on the BP neural network is trained using a training sample set. Before the formal measurement, two precision resistors are used to calibrate the system parameters of the waist circumference measuring instrument based on bioelectrical impedance, namely the slope and offset; during the measurement, the abdominal voltage value is collected and sent to the trained waist circumference measurement model based on the BP neural network to obtain the waist circumference of the tester. The present invention is mainly used for waist circumference measurement. Compared with the measurement method of a transmission mechanical tape measure, the measurement method based on electrical impedance can eliminate the errors caused by different tension forces and different positions during the measurement of the mechanical tape measure.

FIELD: medicine.SUBSTANCE: group of inventions relates to a method for multi-frequency bioelectrical impedance analysis and a device for implementing it. Alternating electric current signals are passed through the human body. Each signal has one of three different frequencies. At each frequency, the amplitude of the voltage of the signals passing through the human body is measured. An initial phase angle is selected for each specific impedance module. The phase angle characterizes the phase shift of the current of signals entering the human body relative to the phase of the voltage of signals passing through the human body. At each frequency, the phase angle is estimated using an equivalent electrical model of the human body, the initial phase angle and the impedance modulus. A body model is created for a specific person. The ratio of extracellular water content to total body water content in a human is determined by inputting extracted parameters of the human body model into a trained machine learning model. The device comprises a source of alternating electric current signals, electrodes for conducting and reading signals, a measuring device for measuring signal parameters, a processor and memory.EFFECT: device is compact and has small-area electrodes for integration into wearable and / or portable devices while maintaining measurement accuracy.14 cl, 6 dwg

The invention relates to the technical field of data processing, in particular to a bioelectrical impedance data analysis method for intestinal inflammation detection, which comprises the following steps: acquiring bioelectrical impedance data of a plurality of different monitoring points in an intestinal region, and determining the fluctuation normal degree of the bioelectrical impedance data of each monitoring point; determining a corresponding relationship between the fluctuation normal degree of the bioelectrical impedance data of each monitoring point and the food residue aggregation degree of each monitoring point, and obtaining an initial abnormal monitoring point; sorting the initial abnormal monitoring points according to the flowing sequence of the food in the intestinal tract, determining the association between the abnormal reasons of the initial abnormal monitoring points and the adjacent initial abnormal monitoring points under the sorting, and screening the initial abnormal monitoring points to obtain a target abnormal monitoring point, thereby realizing the accurate acquisition of the abnormal monitoring points, and improving the accuracy of the abnormal monitoring points. And the accuracy of bioelectrical impedance data acquisition is ensured.

The application provides a portable multi-modal muscle health state detection system and method suitable for patients with bone fracture, and relates to the technical field of medical diagnosis. The system comprises: the flexible sensing patch comprises a flexible substrate and a bioelectrical impedance detection module, a surface electromyography detection module and a near-infrared spectroscopy muscle oxygen detection module collectively integrated on the flexible substrate, and the flexible sensing patch is used for non-invasive collection of multi-modal original monitoring data of a muscle part to be detected. The handheld host is electrically connected with the flexible sensing patch, the handheld host is used for sending control instructions to each detection module and receiving the multi-modal original monitoring data, and a comprehensive evaluation report containing three dimensions of muscle structure, muscle function and muscle metabolism is generated based on each parameter. The application overcomes the technical defects of single evaluation dimension of the existing portable device.

The application discloses a kind of based on biological electrical impedance detection peripheral circulation function evaluation device, comprising: airbag finger sleeve, electrode, air pump and control evaluation device.This device compared with other detection devices innovative use electrical impedance detection method, reliable result;Its simple structure, convenient operation, not affected by using environment;In addition to be applicable to routine patient, it can also be applicable to critical patients, neonates and other patients with poor cooperation ability.

The invention discloses an internal arteriovenous fistula blood vessel noninvasive intelligent monitor, and belongs to the technical field of medical monitoring equipment. The sensor unit is composed of a photoelectric volume pulse wave sensor, a miniature ultrasonic sensor and a bioelectrical impedance sensor and used for collecting information such as heart rate, blood oxygen saturation, blood flow, angiography, tremor frequency, impedance parameters and vascular tension; the monitoring chip unit carries out amplification, filtering, analog-to-digital conversion and feature extraction on the information; the storage and preprocessing unit is used for realizing real-time storage and standardized processing of data; the communication unit uploads the data to a medical institution end or a cloud end through Bluetooth or Wi-Fi; the power management unit provides power and realizes low-power-consumption control; and the early warning unit performs risk assessment on the data based on an artificial intelligence model, and generates early warning information when a potential anomaly is detected. According to the invention, continuous, non-invasive and intelligent monitoring of internal arteriovenous fistula can be realized, and the early recognition and early warning capability of complications can be improved.

Electrical treatment device (20, 200) comprising: an impedance measurement unit (304) configured to measure the bioelectrical impedance of a site on a user's body by using multiple electrodes in contact with that site; a voltage control unit (308) configured to perform treatment of the site by controlling a treatment voltage applied to the multiple electrodes; and a time setting unit (306) configured to set a target time from the start of treatment of the site by the voltage control unit (308) until the voltage value of the applied treatment voltage reaches a target voltage value, based on the measured bioelectrical impedance, wherein the voltage control unit (308) increases the voltage value of the applied treatment voltage to the target voltage value based on the set target time, such that the time setting unit (306) sets the target time to be shorter the greater the measured bioelectrical impedance.

The invention discloses a tumor identification method fusing an electrical impedance signal and a frequency domain enhanced image. The method comprises the following steps: S1, obtaining a bioelectrical impedance signal and medical pathological image data of a to-be-identified sample; s2, performing intra-modal coding processing on the bioelectrical impedance signal and the pathological image; s3, inputting the electric signal feature matrix and the image fusion feature matrix into a multi-modal fusion network to perform dimension unification, constructing a modal similarity matrix to perform weighted convergence on image features, and generating a multi-modal fusion feature matrix; and S4, constructing a decoding network of an asymmetric structure, and inputting the multi-modal fusion feature matrix into the decoding network for tumor recognition. According to the method, the bioelectricity signals and the medical image information are fused, and the frequency domain enhancement and modal fusion strategies are combined, so that the accuracy and robustness of tumor recognition are effectively improved.

The invention discloses a chicken body fat rate phenotype determination system and a chicken selection method. The chicken body fat rate phenotype determination system comprises an eight-electrode system, a multi-channel signal receiving and transmitting module, a processor module and a power supply module, the eight-electrode system is used for sending excitation current and receiving potential difference signals, and a local closed-loop measurement loop can be formed on the same limb; the multi-channel signal receiving and transmitting module is used for controlling excitation and measurement among the electrode groups and realizing biological impedance data acquisition in multiple directions; the processor module is used for constructing data models of the upper limbs, the trunk and the lower limbs of chicken body fat distribution according to the multi-channel impedance data; the power supply module is connected with the processor module and the eight-electrode system and provides power for the system. The method adopts the bioelectrical impedance value to measure and detect the body fat rate of the live chicken, and is high in detection precision, convenient to operate, high in adaptability and convenient to popularize and apply in the field of poultry breeding.

Attachment devices for analyzing the density of breast tissue are described herein, as well as methods of using thereof which rely on bioelectrical impedance analysis. Use of such techniques can be used to pre-screen individuals with dense breast tissue which are at higher risk for breast malignancies and direct them toward other breast screening tests.