A machine learning and optical myography based system for use in the detection and visualization of intramuscular injection sites and the working method thereof

Abstract

The invention relates to a machine learning and optical myography based system for use in anatomy education and ensuring accurate intramuscular injection applications, which enables real-time detection and visualization of the location of muscles in the body, and the working method thereof.

Description

[0001] DESCRIPTION

[0002] A MACHINE LEARNING AND OPTICAL MYOGRAPHY BASED SYSTEM FOR USE IN THE DETECTION AND VISUALIZATION OF INTRAMUSCULAR INJECTION SITES AND THE WORKING METHOD THEREOF

[0003] Technical Field of the Invention

[0004] The invention relates to a machine learning and optical myography based system for use in anatomy education and ensuring accurate intramuscular injection applications, which enables real-time detection and visualization of the location of muscles in the body, and the working method thereof.

[0005] State of the Art

[0006] Anatomy is the branch of biology concerned with the study of the structure of organisms and their parts. In the state of the art, traditional anatomy education is usually provided to students through atlases, models, reenactments, and human cadavers; however, these methods often present static and two-dimensional information to students, which hinders their ability to observe muscles and anatomical structures on a living body in real time

[0001] . This limitation restricts students' ability to visualize anatomy topics and develop practical skills. Traditional methods offer students a limited practical experience. In addition, studies on human cadavers are often shared with a limited number of students and do not allow each student to get enough practice. This makes it difficult for students to acquire real practice skills. Furthermore, the use of cadavers can lead to problems of precision and sterility. Since cadavers are in limited supply and must be preserved, this limits students' desire and opportunity to practice. Traditional anatomy education can cause problems of participation and motivation among students[2]. With the boring or complex materials used in traditional anatomy education, it can be difficult to engage students and ensure their active participation in the educational process.

[0007] In the present art, intramuscular injection, or intramuscular injection, is a parenteral administration method used to deliver drugs or fluids into the body. This method, used in clinical practice, is frequently preferred for reasons such as containing larger and more numerous blood vessels, providing faster absorption, and ensuring dosage stability, as some drugs are prepared for intramuscular administration only[3,4,5]. In intramuscular injection, selecting a safe injection site away from blood vessels, major nerves, and bones is an essential criterion for selecting the injection site[6]. In the state of the art, despite the availability of evidence and guidelines on intramuscular injection practices, many complications can still occur due to incorrect practices and individuals can be harmed due to these practices.

[0008] In the state of the art, intramuscular injection sites are identified by manually determining anatomical structures. The anatomical structure of the injection sites is marked with imaginary lines, and the site is determined by palpating the bone structures. Differences in nurses' hand structures, small anatomical structures in some injection sites, and obesity of individuals make it difficult to palpate anatomical bone structures and to determine the injection site safely.

[0009] Although intramuscular injections may seem like a simple technique, they can cause serious complications if not performed carefully. These potential complications include sciatic nerve injury, abscess, cellulitis, tissue necrosis, muscle fibrosis, contracture, intravascular infection, and hematoma[7]. Sciatic nerve injuries occur when the nerve is damaged, and if the abscess is left untreated, it can lead to serious problems such as the spread of infection[8]. Cellulite causes a dimpled / bumpy appearance on the skin surface, while tissue necrosis is cell damage resulting in pathological death. Muscle fibrosis can occur as a result of repetitive injury to muscle tissue and can make it difficult for the muscle to perform its normal functions[9]. Contracture is a condition that occurs when the tissues surrounding the joint lose their flexibility. Intravascular infection is an infection caused by intravascular contamination of the injected substance or microorganisms [9]. Hematoma is the collection of blood in the tissue due to damage in the vessel wall and if left untreated, can put pressure on organs and cause blood loss[7,9]. All these complications mentioned emphasize the importance of performing intramuscular injections carefully and appropriately. Due to all these complications, the most safe site for intramuscular injection in the hip region is the ventrogluteal region, known as the lateral hip region, which is distant from nerves and blood vessels (Figure 1). To locate the ventrogluteal region, the nurse places the palm of their hand on the greater trochanter (1) of the femur. The index finger points toward the anterior superior iliac crest (2), while the middle finger points toward the iliac spine (3). The injection site is the gluteus medius and gluteus minimus muscles (4), which are the midpoint of this triangle. However, despite being comprehensively included in the nursing education curriculum, the ventrogluteal region cannot be routinely used in the clinical setting due to the difficulty in locating it and differences in the practitioner's hand structure.

[0010] Although in the state of the art there is a system

[0010] where the general positions of specific muscles can be observed on the body with augmented reality glasses, these systems only show the general positions of muscle groups and there is no system in the state of the art that precisely locates the exact position of specific muscles and points to the appropriate site for intramuscular injection. Due to the fact that there is no system introduced in the present art to ensure accurate detection of intramuscular injection sites and complications such as sciatic nerve injury, abscess, cellulitis, tissue necrosis, granuloma, muscle fibrosis, contracture, intravascular infection, and hematoma have occurred due to this, it has become necessary to introduce a system that eliminates the problem of incorrect detection of intramuscular injection sites.

[0011] Summary and Objects of the Invention

[0012] The invention describes a machine learning and optical myography based system for use in anatomy education and ensuring accurate intramuscular injection applications, which enables real-time detection and visualization of the location of muscles in the body, and the working method thereof.

[0013] An object of the invention is to introduce a machine learning and optical myography based system that enables intramuscular injection applications to be performed accurately. In the system subject to the invention, the locations of the muscles in the body are detected and imaged in real time, thus preventing possible complications (sciatic nerve injury, abscess, cellulitis, tissue necrosis, granuloma, muscle fibrosis, contracture, intravascular infection, and hematoma) encountered as a result of intramuscular injection applications. Although the present art includes a system

[0010] where the general positions of specific muscles can be observed on the body with augmented reality glasses, these systems only show the general positions of muscle groups. The system subject to the invention also accurately determines the precise location of specific muscles with the movement obtained from the camera system and also marks the injection site. Another object of the invention is to introduce a system that offers students an interactive learning experience in anatomy education, helping them gain a better understanding of muscles and anatomical structures. The system analyzes the data collected in real time and provides users with detailed reports on injection performance, allowing them to evaluate and improve their implementation skills. The system has a user-friendly interface and ergonomic features, making it easy for health professionals and students to use the system effectively. The system features wireless connectivity options and a portable structure, providing greater mobility and practicality for healthcare professionals and students. These technical impacts aim to extend the advantages offered by the muscle locator system in anatomical education and intramuscular injection applications and highlight the potential of the system to improve the user experience.

[0014] The invention provides a machine learning and optical myography based system that can easily identify the muscles in the hip region, thereby eliminating the difficulties encountered in identifying the ventrogluteal region and making the use of the ventrogluteal region routine in intramuscular injections administered to the hip region in clinical practice.

[0015] Description of the drawings

[0016] Fig. 1: Injection point in the lateral buttock area (ventrogluteal region) (Prior art).

[0017] Fig. 2: Representative illustration of the components of the system (" Camera System") that identifies safe sites for intramuscular injections. In this way, the image is captured by the camera (5) and then processed by the computer (6) using machine learning or computer vision methods. Algorithms running on the computer (6) determine which part of the body is being imaged, locate known muscles according to the camera's (5) viewpoint and calculate where the injection site should be in relation to these muscles. This calculated point is the injection point marked on the body (8) and is projected onto the body via the projector (7). When the body or system moves, the position of the point is continuously updated and continues to be projected.

[0018] Fig. 3: Representative illustration of the "display-based" version, another version of the system that identifies safe sites for intramuscular injections. The image captured by the camera (5) is processed by the computer (6) using machine learning or computer vision methods. Algorithms running on the computer (6) analyze which part of the body is being imaged, determine location of the known muscles according to the camera's (5) viewpoint and detect where the injection site should be in relation to these muscles. The image taken by the camera (5) and the detected point are shown to the user on the display (9). When the body or system moves, the position of this point is updated instantaneously and re-projected to the user on the display (9).

[0019] Fig. 4. Flow diagram of the working method of a machine learning and optical myography based system subject to the invention for use in anatomy education and ensuring accurate intramuscular injection applications, which enables real-time detection and visualization of the location of muscles in the body.

[0020] Definitions of the Elements / Features / Parts of the Invention

[0021] 1. Greater trochanter of the femur (prior art)

[0022] 2. Anterior superior iliac crest (prior art)

[0023] 3. Iliac spine (prior art)

[0024] 4. Gluteus medius and gluteus minimus muscles (prior art)

[0025] 5. Camera

[0026] 6. Computer

[0027] 7. Projector

[0028] 8. Injection point marked on the body

[0029] 9. Display

[0030] 100. Revealing the individual's hip area

[0031] 101. Bringing the system to the open position and bringing the camera (5) of the system to the injection site on the lateral hip

[0032] 102. Processing of the image taken with the camera (5) by the computer (6)

[0033] 103. Projecting a pattern on the body with the projector (7) before capturing an image with the projector (7)

[0034] 104. Locating the muscle by computer (6) using image processing and / or machine learning algorithms and determining the injection point based on the location and movements of the muscles

[0035] 105. Locating the muscles by analyzing the shifts of these points with structured light methods by the image processing algorithm as the individual moves the muscles, and determining the injection point based on the location and movements of the muscles

[0036] 106. Displaying the injection point on the video shown on the display (9)

[0037] 107. Display system

[0038] 108. Projector system

[0039] 109. After determining the injection point, marking the injection point on the body by the computer (6) by projecting it with the help of the projector (7)

[0040] 110. Administering the intramuscular injection at the marked site by the user

[0041] Detailed Description of the Invention

[0042] The invention relates to a machine learning and optical myography based system for use in anatomy education and ensuring accurate intramuscular injection applications, which enables real-time detection and visualization of the location of muscles in the body, and the working method thereof. The system subject to the invention is portable and can be used with one hand.

[0043] A machine learning and optical myography based system subject to the invention for use in anatomy education and ensuring accurate intramuscular injection applications, which enables real-time detection and visualization of the location of muscles in the body comprises:

[0044] • a camera for use in locating the injection site (5),

[0045] • a computer (6) comprising at least one processor to run image processing algorithms,

[0046] • a display for marking the injection site (9),

[0047] • a projector for use in real-time projection of muscle locations onto the skin in line with data analyzed by machine learning and optical myography based image processing algorithms in order to guide the user by projecting the selected muscle area directly onto the skin to show the injection site to the user (7).

[0048] In addition, the computer (6) can also be preferred as at least one microcontroller depending on the application area.

[0049] The working method of a machine learning and optical myography based system subject to the invention for use in anatomy education and ensuring accurate intramuscular injection applications, which enables real-time detection and visualization of the location of muscles in the body comprises the process steps of:

[0050] i. pointing the camera (5), which will be used to locate the injection site, at the patient,

[0051] ii. after the patient contracts the muscle in the area to be injected, placing the system in contact with the injection site, then determining the muscle contraction movement in the area using optical myography techniques, and generating a map of the muscles,

[0052] iii. determining the optimal injection site.

[0053] Said process step iii. of determining the optimal injection site herein comprises the process steps of:

[0054] i. matching the displayed region with the digital atlas using the general anatomy knowledge programmed into the system (digital anatomy atlas),

[0055] ii. subsequently, comparing the pixels in the frames captured by the camera (5) with the image processing system to identify differences between the frames captured by the camera (5), matching muscle information to the digital atlas, or indicating changes in muscle position using Eulerian Video Magnification or similar methods in the image processing system,

[0056] iii. locating the muscles with the image processing system using structured light algorithms based on a pattern projected onto the patient,

[0057] iv. locating muscles with the image processing system using interference patterns of laser beams projected onto the patient (laser speckle interferometry).

[0058] The system subject to the invention determines the optimal injection site by mapping the patient's muscles with machine learning image processing methods using the interference patterns of the camera (5) and optionally laser beams. The optimal injection site is shown on the display (9) or projected onto the patient's body with the help of the projector (7). This way, the person using the system can easily mark the area where the muscles are located and administer the injection at that exact point (the injection point (8) marked on the body).

[0059] Fig. 2 shows an application of the invention. The image taken with the camera (5) is processed by the computer (6). The muscles are located by the computer (6) using image processing algorithms. While doing this, it is also possible to measure muscle contraction using video processing methods. After determining the injection point, the computer (6) marks the injection point on the body by projecting it with the help of the projector (7). The system subject to the invention can also work by projecting a pattern on the body with the help of the projector (7) before the projector (7) captures an image and then analyzing the shifts of these points by using structured light methods by the image processing algorithm as the patient moves the muscles.

[0060] Fig. 3 shows another application of the invention. In this application, the images taken with the camera (5) are processed by the computer (6) and displayed on the display (9). The injection point (8), which is marked on the body as a result of the image processing algorithm, is shown on the video displayed on the display (9).

[0061] Fig. 4 shows a flow diagram of the working method of a machine learning and optical myography based system subject to the invention for use in anatomy education and ensuring accurate intramuscular injection applications, which enables real-time detection and visualization of the location of muscles in the body.

[0062] The application method of a machine learning and optical myography based system subject to the invention for use in anatomy education and ensuring accurate intramuscular injection applications, which enables real-time detection and visualization of the location of muscles in the body comprises the process steps of:

[0063] i. revealing the individual's lateral hip area,

[0064] ii. bringing the system to the open position and bringing the camera (5) of the system to the injection site on the lateral hip of the individual,

[0065] iii. processing the image taken with the camera (5) by the computer (6) and locating the muscle by the computer (6) using image processing and / or machine learning algorithms, or projecting a pattern on the body with the help of a projector (7) before capturing an image with the projector (7) and locating the muscles by analyzing the shifts of these points with structural light methods by the image processing algorithm as the individual moves the muscles,

[0066] iv. determination of the injection point based on the location and movements of the muscles,

[0067] v. in the case of a system with a display, showing the injection point on the video displayed on the display (9); in the case of a system with a projector (7), after the computer (6) determines the injection point, marking the injection point by projecting onto the body with the help of the projector (7), vi. administering intramuscular injection at the marked site by the user. References

[0068] [1] Estai, M., & Bunt. S. (2016). Best teaching practices in anatomy education: A critical review. Annals of Anatomy-Anatomischer Anzeiger, 208, 151-157.

[0069] [2] Patra, A., Asghar, A., Chaudhary, P., & Ravi, K. S. (2022). Integration of innovative educational technologies in anatomy teaching: new normal in anatomy education. Surgical and Radiologic Anatomy, 44(1), 25-32.

[0070] [3] Nicoll, L. H., & Hesby, A. (2002). Intramuscular injection: an integrative research review and guideline for evidence-based practice. Applied nursing research, 15(3), 149-162.

[0071] [4] Hunter, J. (2008). Intramuscular injection techniques. Nursing Standard (through 2013), 22(24), 35.

[0072] [5] Rodger, M. A., & King, L. (2000). Drawing up and administering intramuscular injections: a review of the literature. Journal of advanced nursing, 31(3), 574-582.

[0073] [6] Workman, B. (2000). Safe injection techniques. Primary Health Care, 10(6).

[0074] [7] Buschmann, T., & Ohnesorge, B. (2015). Complications after intramuscular injections in Equids. Journal of Equine Veterinary Science, 35(6), 465-474.

[0075] [8] Small, S. P. (2004). Preventing sciatic nerve injury from intramuscular injections: literature review. Journal of advanced nursing, 47(3), 287-296.

[0076] [9] Cheng, J., & Abdi, S. (2007). Complications of joint, tendon, and muscle injections. Techniques in Regional Anesthesia and Pain Management, 11(3), 141-147.

[0077]

[0010] Aung, Y. M., & Al-Jumaily, A. (2011, December). Rehabilitation exercise with real¬ time muscle simulation based EMG and AR. In 2011 11th International Conference on Hybrid Intelligent Systems (HIS) (pp. 641-646). IEEE.

Claims

CLAIMS1. A computer (6) assisted system comprising at least one processor to enable real-time detection of the location of the muscles in the body in intramuscular injection applications, characterized in that it comprises:• a camera (5) for use in locating the injection site,• a computer (6) comprising at least one processor to run image processing algorithms,• a display (9) for marking the injection site,• a projector (7) for use in real-time projection of muscle locations onto the skin in line with data analyzed by machine learning and optical myography based image processing algorithms in order to guide the user by projecting the selected muscle area directly onto the skin to show the injection site to the user.

2. The system according to claim 1, characterized in that said computer (6) is portable.

3. The system according to claim 1, characterized in that said computer (6) is at least one microcontroller depending on the application area4. The working method of the computer (6) supported system comprising at least one processor to enable real-time detection of the location of the muscles in the body in intramuscular injection applications, characterized in that it comprises the process steps of:i. pointing the camera (5), which will be used to locate the injection site, at the patient,ii. after the patient contracts the muscle in the area to be injected, placing the system in contact with the injection site, then determining the muscle contraction movement in the area using optical myography techniques, and generating a map of the muscles,iii. determining the optimal injection site.

5. The method according to claim 4, characterized in that said process step iii. of determining the optimal injection site comprises the process steps of:i. matching the displayed region with the digital atlas using the general anatomy knowledge programmed into the system (digital anatomy atlas),ii. subsequently, comparing the pixels in the frames captured by the camera (5) with the image processing system to identify differences between the frames captured by the camera (5), matching muscle information to the digital atlas, or indicating changes in muscle position using Eulerian Video Magnification or similar methods in the image processing system,iii. locating the muscles with the image processing system using structured light algorithms based on a pattern projected onto the patient,iv. locating muscles with the image processing system using interference patterns of laser beams projected onto the patient (laser speckle interferometry).

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