CN112138249B - Intravenous injection robot control method based on ultrasonic evaluation - Google Patents
Intravenous injection robot control method based on ultrasonic evaluation Download PDFInfo
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- CN112138249B CN112138249B CN202010853721.2A CN202010853721A CN112138249B CN 112138249 B CN112138249 B CN 112138249B CN 202010853721 A CN202010853721 A CN 202010853721A CN 112138249 B CN112138249 B CN 112138249B
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- blood vessel
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- intravenous injection
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
- A61M5/00—Devices for bringing media into the body in a subcutaneous, intra-vascular or intramuscular way; Accessories therefor, e.g. filling or cleaning devices, arm-rests
- A61M5/42—Devices for bringing media into the body in a subcutaneous, intra-vascular or intramuscular way; Accessories therefor, e.g. filling or cleaning devices, arm-rests having means for desensitising skin, for protruding skin to facilitate piercing, or for locating point where body is to be pierced
- A61M5/427—Locating point where body is to be pierced, e.g. vein location means using ultrasonic waves, injection site templates
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
- A61M2205/00—General characteristics of the apparatus
- A61M2205/70—General characteristics of the apparatus with testing or calibration facilities
Abstract
The invention relates to an intravenous injection robot needle insertion angle control method based on ultrasonic evaluation, wherein the intravenous injection robot comprises a mechanical arm and an ultrasonic imaging module, and the control method comprises the following steps: s1, obtaining a vein blood vessel sample; s2, carrying out ultrasonic identification on the vein blood vessel sample to obtain characteristic pixel points; s3, judging whether the thickness of the blood vessel is larger than a judgment threshold value, if so, setting the blood vessel as a thick blood vessel, and otherwise, setting the blood vessel as a thin blood vessel; s4, iteratively learning through a deep convolutional neural network to generate a blood vessel identification model; s5, an ultrasonic imaging module acquires a blood vessel ultrasonic image, identifies the thickness of a blood vessel and determines the needle inserting angle; s6, the mechanical arm penetrates the needle head into the blood vessel of the patient according to the needle inserting angle, ultrasonic positioning is carried out in real time, the center of the vein is tracked, the thickness of the residual blood vessel is determined, and the angle of the needle head is adjusted in real time. Compared with the prior art, the invention has the advantages of preventing the puncture angle from being unsuitable to cause damage, improving the safety of venipuncture and the like.
Description
Technical Field
The invention relates to a needle insertion angle control method, in particular to an intravenous injection robot control method based on ultrasonic evaluation.
Background
Infusion or taking of blood samples by venous blood flow is the most common clinical routine, accomplished by manual venipuncture techniques. The success rate of the manual venipuncture technique depends on the skill of a clinician and the physiological function of a patient, the failure rate of the difficult patient is high, the venipuncture is the main cause of medical injury, and the intravenous injection robot can reduce the incidence rate of loss to a certain extent. In addition, for infectious diseases like new coronary pneumonia, the intravenous injection robot can also prevent medical staff from being in direct contact with infectious patients, and the infection risk of the medical staff in the process of treating the infectious diseases is greatly reduced.
In the actual venipuncture procedure, the needle insertion angle is closely related to the thickness of the blood vessel. The thicker the blood vessel, the larger the angle of insertion, and the thinner the blood vessel, the smaller the angle of insertion. In addition, the needle insertion angle is reduced according to the real-time residual blood vessel thickness in the needle insertion process, so that the safety and the reliability of intravenous injection can be guaranteed.
Chinese patent CN107438408A discloses an ultrasound system and method for identifying a blood vessel of a subject, wherein the ultrasound system comprises: an ultrasound probe configured to simultaneously acquire a sequence of ultrasound blood flow data frames (e.g., a sequence of ultrasound doppler data frames) and a sequence of ultrasound B-mode data frames of a region of interest including a blood vessel over a predetermined time period; a blood flow region selection unit configured to select a blood flow region in a sequence of blood flow data frames; and a vessel segmentation unit configured to segment a vessel in at least one frame of the sequence of ultrasound B-mode data based on the selected blood flow region. The application scenario of the patent is to acquire one or more characteristics (such as size, blood pressure, blood flow velocity and the like) of a blood vessel through an ultrasound system, and the ultrasound identification method of the invention is applied to the determination of the needle insertion angle of the intravenous injection robot. In addition, the patent lacks the use of deep convolutional neural networks to classify ultrasound-identified vessel images.
Chinese patent CN106880891A discloses a robot with intravenous injection function, which comprises a rack for placing ampoule bottles, a clamping six-degree-of-freedom manipulator arranged on one side of the rack and rotating 360 degrees, an ampoule bottle clamping device arranged on the clamping six-degree-of-freedom manipulator, a knocking device arranged on one side of the clamping six-degree-of-freedom manipulator 90 degrees and used for opening ampoule bottles on the ampoule bottle clamping device, and the like. However, the intravenous injection robot in the patent does not consider the relationship between the thickness of the blood vessel and the needle insertion angle before venipuncture, does not determine the needle insertion angle according to the thickness of the blood vessel of the patient before venipuncture, does not conform to the clinical venipuncture process, and may cause damage to the blood vessel and other parts of the patient.
Disclosure of Invention
The invention aims to overcome the defects that the vein puncture lacks the determination of the needle inserting angle according to the thickness of the blood vessel of a patient and the blood vessel of the patient is damaged in the prior art, and provides an intravenous injection robot control method based on ultrasonic evaluation.
The purpose of the invention can be realized by the following technical scheme:
an intravenous injection robot control method based on ultrasonic evaluation comprises the following specific steps:
s1, obtaining original vein blood vessel samples with different thicknesses;
s2, carrying out ultrasonic identification on the vein blood vessel sample to obtain an original ultrasonic image, and obtaining characteristic pixel points in the original ultrasonic image;
s3, classifying the vein blood vessel samples, judging whether the thickness of the blood vessel is larger than a preset judgment threshold value, if so, setting the corresponding output label as a thick blood vessel, otherwise, setting the corresponding output label as a thin blood vessel;
s4, matching the characteristic pixel points with the output labels, and performing iterative learning on the matched characteristic pixel points and the output labels through a Deep Convolutional Neural Network (DCNN) to generate a corresponding blood vessel identification model;
s5, the ultrasonic imaging module acquires a blood vessel ultrasonic image of a patient, the blood vessel thickness of the blood vessel ultrasonic image is identified through the blood vessel identification model, and a corresponding needle inserting angle is determined according to the blood vessel thickness;
s6, the mechanical arm penetrates the needle head into the blood vessel of the patient according to the needle inserting angle, the ultrasonic imaging module ultrasonically positions and tracks the vein center in each frame of ultrasonic image in real time, the thickness of the residual blood vessel is determined, and the angle of the needle head is adjusted in real time according to the thickness of the residual blood vessel.
In step S5, if the blood vessel thickness corresponds to a thick blood vessel, a large-angle needle insertion angle is used, and if the blood vessel thickness corresponds to a thin blood vessel, a small-angle needle insertion angle is used.
Furthermore, the large-angle needle inserting angle is an angle between the needle head and the arm of the patient which is larger than or equal to 15 degrees, and the small-angle needle inserting angle is an angle between the needle head and the arm of the patient which is smaller than 15 degrees.
The ultrasound imaging module identifies a blood vessel of a patient through acoustic attenuation.
Further, the calculation formula of the acoustic attenuation is specifically as follows:
α(f)＝α1fn(dB/cm)
wherein f is the acoustic frequency, alpha1Is the attenuation of 1MHz, n isA power constant.
The deep convolutional neural network includes a convolutional layer and a fully-connected layer.
Furthermore, the convolution layer carries out weighted summation on the characteristics of the input characteristic pixel points and then uses an activation function for activation.
Further, the activation function is specifically a Relu activation function, and the Relu activation function is in a piecewise linear form in the forward transfer, inverse feedback and derivation processes of the deep convolutional neural network.
Further, the formula for weighted summation of the Relu activation function is specifically as follows:
yk＝f1∑ωk×xk-1+bk
wherein f is1Representing an activation function, k representing the number of network layers, ω representing a weight, b representing a bias parameter, and y representing an output feature image.
The moving mode of the mechanical arm comprises stretching and rotating, and the needle head at the top end of the mechanical arm is moved to a target needle inserting angle through the stretching and rotating of the mechanical arm.
The intravenous injection robot is further provided with an arm supporting plate for supporting the arm of the patient when the ultrasonic imaging module acquires the blood vessel ultrasonic image of the patient.
Compared with the prior art, the invention has the following beneficial effects:
1. the method carries out iterative learning on the characteristic pixel points of the ultrasonic image and the output labels of the blood vessel samples through the Deep Convolutional Neural Network (DCNN), and has strong robustness and wide application range; the generated blood vessel identification model identifies the thickness of the blood vessel so as to determine the needle inserting angle of the needle head, prevent the puncture angle from being unsuitable for causing damage to the blood vessel and improve the stability and the safety of venipuncture.
2. The invention can continuously carry out real-time ultrasonic positioning and tracking after the needle insertion, and adjust the needle insertion angle in real time according to the thickness of the residual blood vessel, thereby conforming to the venipuncture process of the actual medicine and ensuring the safety and reliability of the whole puncture process.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic structural diagram of an intravenous injection robot of the present invention;
FIG. 3 is a schematic view of the present invention for adjusting the needle insertion angle in real time according to the thickness of the remaining blood vessel.
Reference numerals:
1-an ultrasound imaging module; 2, a mechanical arm; 3-arm of patient; 4-arm supporting plate.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
An intravenous injection robot control method based on ultrasonic evaluation prevents a puncture angle from being unsuitable for causing damage to a blood vessel and improves stability and safety of intravenous puncture, as shown in fig. 2, the intravenous injection robot comprises a mechanical arm and an ultrasonic imaging module, as shown in fig. 1, and a needle insertion angle control method specifically comprises the following steps:
s1, obtaining original vein blood vessel samples with different thicknesses;
s2, carrying out ultrasonic identification on the vein blood vessel sample to obtain an original ultrasonic image, and obtaining characteristic pixel points in the original ultrasonic image;
s3, classifying the vein blood vessel samples, judging whether the thickness of the blood vessel is larger than a preset judgment threshold value, if so, setting the corresponding output label as a thick blood vessel, otherwise, setting the corresponding output label as a thin blood vessel;
s4, matching the characteristic pixel points with the output labels, and performing iterative learning on the matched characteristic pixel points and the output labels through a Deep Convolutional Neural Network (DCNN) to generate a corresponding blood vessel identification model;
s5, after the patient stretches into an arm, activating the ultrasonic imaging module 1, obtaining a blood vessel ultrasonic image of the patient by the ultrasonic imaging module 1, identifying the blood vessel thickness of the blood vessel ultrasonic image through a blood vessel identification model, selecting a blood vessel suitable for intravenous injection, and determining a corresponding needle insertion angle according to the blood vessel thickness of the blood vessel;
s6, the mechanical arm 2 pierces the needle into the blood vessel of the patient according to the needle inserting angle, as shown in figure 3, the ultrasonic imaging module ultrasonically positions and tracks the center of the vein in each frame of ultrasonic image in real time, the thickness of the residual blood vessel is determined, and the angle of the needle is adjusted in real time according to the thickness of the residual blood vessel.
In step S5, if the blood vessel thickness corresponds to a thick blood vessel, a large-angle needle insertion angle is used, and if the blood vessel thickness corresponds to a thin blood vessel, a small-angle needle insertion angle is used.
The large-angle needle inserting angle is the needle inserting angle of which the included angle between the needle head and the arm of the patient is larger than or equal to 15 degrees, and the small-angle needle inserting angle is the needle inserting angle of which the included angle between the needle head and the arm of the patient is smaller than 15 degrees.
The ultrasound imaging module 1 identifies the patient's blood vessels by acoustic attenuation.
The principle of acoustic attenuation is that strong reflection of incident sound waves occurs at a tissue interface, the generated acoustic attenuation is a measure of energy loss when sound propagates in tissue, and the calculation formula of the acoustic attenuation is as follows:
α(f)＝α1fn(dB/cm)
wherein f is the acoustic frequency, alpha1Is the attenuation of 1MHz, and n is the power constant. The acoustic properties of human skin and vascular tissue are shown in table 1:
TABLE 1 Acoustic characteristics of human skin and vascular tissue
The calculation formula of the pixel value of the characteristic pixel point is as follows:
wherein i is the pixel value of the characteristic pixel point.
The deep convolutional neural network includes convolutional layers and fully-connected layers.
And performing weighted summation on the characteristics of the input characteristic pixel points in the convolutional layer, and then activating by using an activation function.
The activation function is specifically a Relu activation function, and the Relu activation function is in a piecewise linear form in the forward transfer, reverse feedback and derivation processes of the deep convolutional neural network.
The formula for the weighted summation of the Relu activation function is specified as follows:
yk＝f1∑ωk×xk-1+bk
wherein f is1Representing an activation function, k representing the number of network layers, ω representing a weight, b representing a bias parameter, and y representing an output feature image.
The moving mode of the mechanical arm 2 comprises stretching and rotating, and the needle head at the top end of the mechanical arm is moved to a target needle inserting angle through the stretching and rotating of the mechanical arm 2.
The intravenous injection robot is further provided with an arm supporting plate 4 for supporting the arm 3 of the patient when the ultrasonic imaging module 1 acquires the blood vessel ultrasonic image of the patient.
In addition, it should be noted that the specific implementation examples described in this specification may have different names, and the above contents described in this specification are only illustrations of the structures of the present invention. All equivalent or simple changes in the structure, characteristics and principles of the invention are included in the protection scope of the invention. Various modifications or additions may be made to the described embodiments or methods may be similarly employed by those skilled in the art without departing from the scope of the invention as defined in the appending claims.
Claims (7)
1. An intravenous injection robot control method based on ultrasonic evaluation is characterized in that the intravenous injection robot comprises a mechanical arm and an ultrasonic imaging module, and the needle insertion angle control method comprises the following specific steps:
s1, obtaining original vein blood vessel samples with different thicknesses;
s2, carrying out ultrasonic identification on the vein blood vessel sample to obtain an original ultrasonic image, and obtaining characteristic pixel points in the original ultrasonic image;
s3, classifying the vein blood vessel samples, judging whether the thickness of the blood vessel is larger than a preset judgment threshold value, if so, setting the corresponding output label as a thick blood vessel, otherwise, setting the corresponding output label as a thin blood vessel;
s4, matching the characteristic pixel points with the output labels, and performing iterative learning on the matched characteristic pixel points and the matched output labels through a deep convolutional neural network to generate a corresponding blood vessel identification model;
s5, the ultrasonic imaging module (1) acquires a blood vessel ultrasonic image of a patient, the blood vessel thickness of the blood vessel ultrasonic image is identified through the blood vessel identification model, and a corresponding needle insertion angle is determined according to the blood vessel thickness;
in the step S5, if the blood vessel thickness corresponds to a thick blood vessel, a large-angle needle insertion angle is adopted, and if the blood vessel thickness corresponds to a thin blood vessel, a small-angle needle insertion angle is adopted; the large-angle needle inserting angle is a needle inserting angle of which the included angle between the needle head and the arm of the patient is larger than or equal to 15 degrees, and the small-angle needle inserting angle is a needle inserting angle of which the included angle between the needle head and the arm of the patient is smaller than 15 degrees.
2. The ultrasound assessment based intravenous robot control method according to claim 1, characterized in that the ultrasound imaging module (1) identifies the patient's blood vessels by acoustic attenuation.
3. The intravenous injection robot control method based on ultrasonic evaluation according to claim 2, characterized in that the calculation formula of the acoustic attenuation is specifically as follows:
α(f)＝α1fn(dB/cm)
wherein f is the acoustic frequency, alpha1Is the attenuation of 1MHz, and n is the power constant.
4. The intravenous injection robot control method based on ultrasonic assessment according to claim 1, characterized in that the deep convolutional neural network comprises convolutional layers and fully connected layers.
5. The intravenous injection robot control method based on ultrasonic evaluation as claimed in claim 4, wherein the convolution layer is activated by an activation function after weighted summation of the characteristics of the input characteristic pixel points.
6. An intravenous robot control method based on ultrasound assessment according to claim 5, characterized in that the activation function is in particular a Relu activation function.
7. The intravenous injection robot control method based on ultrasonic assessment according to claim 1, characterized in that the moving manner of the mechanical arm (2) comprises telescoping and rotating.
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1551033A (en) * | 2003-03-20 | 2004-12-01 | ��ʽ���綫֥ | Processor for analyzing tubelike structure such as blood vessel |
| CN103028185A (en) * | 2011-09-30 | 2013-04-10 | Ge医疗系统环球技术有限公司 | Automatic vessel intervention device, system and method based on real-time volume ultrasonic waves |
| CN103750866A (en) * | 2014-01-25 | 2014-04-30 | 汕头市超声仪器研究所有限公司 | Ultrasonic-Doppler-effect positioning puncturing device for vessel |
| CN103841898A (en) * | 2011-09-30 | 2014-06-04 | 皇家飞利浦有限公司 | Ultrasound system with dynamically automated doppler flow settings as a sample volume is moved |
| CN104224129A (en) * | 2014-09-24 | 2014-12-24 | 深圳市中科微光医疗器械技术有限公司 | Identification method and prompting system for depth of vein blood vessel |
| CN105107067A (en) * | 2015-07-16 | 2015-12-02 | 执鼎医疗科技江苏有限公司 | Venipuncture system with infrared guidance and ultrasonic location |
| CN105726097A (en) * | 2014-12-12 | 2016-07-06 | 刘明威 | Ultrasonic puncture guide device |
| CN108433791A (en) * | 2018-04-26 | 2018-08-24 | 王燕青 | Adjustable needle angle sting device and its system and control method |
| CN109635618A (en) * | 2018-08-07 | 2019-04-16 | 南京航空航天大学 | Visible images vein developing method based on convolutional neural networks |
| CN111035437A (en) * | 2019-05-28 | 2020-04-21 | 上海交通大学医学院附属第九人民医院 | Blood vessel auxiliary puncture system, robot, method and storage medium |
| CN112138249A (en) * | 2020-08-24 | 2020-12-29 | 同济大学 | Intravenous injection robot needle insertion angle control method based on ultrasonic evaluation |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| ATE440629T1 (en) * | 2005-05-18 | 2009-09-15 | Koninkl Philips Electronics Nv | CANNULA INSERTION SYSTEM |
| WO2013150549A2 (en) * | 2012-04-04 | 2013-10-10 | Saxena Sulakshna | System and method for locating blood vessels and analysing blood |
-
2020
- 2020-08-24 CN CN202010853721.2A patent/CN112138249B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1551033A (en) * | 2003-03-20 | 2004-12-01 | ��ʽ���綫֥ | Processor for analyzing tubelike structure such as blood vessel |
| CN103028185A (en) * | 2011-09-30 | 2013-04-10 | Ge医疗系统环球技术有限公司 | Automatic vessel intervention device, system and method based on real-time volume ultrasonic waves |
| CN103841898A (en) * | 2011-09-30 | 2014-06-04 | 皇家飞利浦有限公司 | Ultrasound system with dynamically automated doppler flow settings as a sample volume is moved |
| CN103750866A (en) * | 2014-01-25 | 2014-04-30 | 汕头市超声仪器研究所有限公司 | Ultrasonic-Doppler-effect positioning puncturing device for vessel |
| CN104224129A (en) * | 2014-09-24 | 2014-12-24 | 深圳市中科微光医疗器械技术有限公司 | Identification method and prompting system for depth of vein blood vessel |
| CN105726097A (en) * | 2014-12-12 | 2016-07-06 | 刘明威 | Ultrasonic puncture guide device |
| CN105107067A (en) * | 2015-07-16 | 2015-12-02 | 执鼎医疗科技江苏有限公司 | Venipuncture system with infrared guidance and ultrasonic location |
| CN108433791A (en) * | 2018-04-26 | 2018-08-24 | 王燕青 | Adjustable needle angle sting device and its system and control method |
| CN109635618A (en) * | 2018-08-07 | 2019-04-16 | 南京航空航天大学 | Visible images vein developing method based on convolutional neural networks |
| CN111035437A (en) * | 2019-05-28 | 2020-04-21 | 上海交通大学医学院附属第九人民医院 | Blood vessel auxiliary puncture system, robot, method and storage medium |
| CN112138249A (en) * | 2020-08-24 | 2020-12-29 | 同济大学 | Intravenous injection robot needle insertion angle control method based on ultrasonic evaluation |
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