An artificial intelligence-based, wearable microwave imaging system for detecting tibia fractures

Abstract

The present invention relates to an artificial intelligence-based, wearable microwave imaging system for detecting tibia fractures, wherein the system is provided as an artificial-based, non-ionizing, cost-effective, portable, and non-invasive imaging system for initial fracture screening and routine monitoring in the medical field across various environments requiring fracture imaging, including hospitals, ambulances, field environments, and military applications.

Description

[0001] AN ARTIFICIAL INTELLIGENCE-BASED, WEARABLE MICROWAVE IMAGING SYSTEM FOR DETECTING TIBIA FRACTURES

[0002] Technical Field

[0003] The present invention relates to an artificial intelligence-based, wearable microwave imaging system for detecting tibia fractures, wherein the system is provided as an artificialbased, non-ionizing, cost-effective, portable, and non-invasive imaging system for initial fracture screening and routine monitoring in the medical field across various environments requiring fracture imaging, including hospitals, ambulances, field environments, and military applications.

[0004] Background of the Invention

[0005] In the current state of the art, the detection and monitoring of bone fractures are performed using various medical imaging techniques such as X-ray imaging, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI). In general, X-ray imaging is employed as the initial screening test when a patient presents to a hospital. Due to their high cost, CT and MRI are typically preferred only in cases where X-ray imaging fails to provide sufficient diagnostic detail. X-ray imaging is one of the most widely used diagnostic imaging techniques for visualizing specific regions of the body by means of X-rays. While X-ray images may not provide high resolution, they are generally capable of diagnosing bone fractures. However, repeated follow-up examinations using X-rays result in exposure to elevated levels of ionizing radiation, which makes this approach unsuitable for children and pregnant women. CT imaging also relies on X-rays to produce more detailed, cross-sectional images of the body. However, when performed repeatedly, this technique exposes patients to substantial levels of ionizing radiation, which may pose potential health risks to the patient. In contrast to X-ray and CT imaging, MRI does not use X-ray radiation. Instead, an MRI scanner generates an image of an internal organ using a strong static magnetic field and pulsed radio waves. MRI is an expensive and time-consuming technique, and it may also interfere with the operation of certain medical implants, such as cardiac pacemakers.Therefore, the abovementioned imaging techniques are subject to several technical limitations, including restricted portability, reliance on ionizing radiation, high cost, and a lack of suitability for real-time fracture monitoring.

[0006] Studies in the existing art reveal that X-ray and CT systems emit ionizing radiation due to their use of X-rays. After a fracture has been detected, ongoing real-time monitoring is often required; however, such monitoring cannot be effectively supported by ionizing imaging methods or by non-portable, high-cost systems such as MRI. Unlike current solutions, the artificial intelligence-based, wearable microwave imaging system for detecting tibia fractures enables real-time monitoring through a non-ionizing, portable, and cost-effective system.

[0007] Description of the Invention

[0008] In the present description, the artificial intelligence-based, wearable microwave imaging system for detecting tibia fractures is provided solely for facilitating a clearer understanding of the subject matter, without any intention to impose a limiting effect.

[0009] The present invention relates to an artificial intelligence-based, wearable microwave imaging system for detecting tibia fractures, which satisfies the aforementioned requirements, eliminates existing disadvantages, and provides additional advantages.

[0010] The primary object of the invention is to provide an alternative imaging system for use in initial fracture screening and routine monitoring in the medical field, which is a non-ionizing, cost-effective, portable, and non-invasive system equipped with an artificial intelligencebased decision mechanism.

[0011] The invention relates to an artificial intelligence-based, wearable microwave imaging system for detecting tibia fractures, which is provided as an artificial-based, non-ionizing, cost-effective, portable, and non-invasive imaging system for initial fracture screening and routine monitoring in the medical field.

[0012] Preferably, the invention provides a non-ionizing, artificial intelligence-based, wearable microwave imaging system for detecting tibia fractures.More preferably, the invention provides an artificial intelligence-based, wearable microwave imaging system for detecting tibia fractures, which is cost-effective and portable.

[0013] More preferably, the invention provides an artificial intelligence-based, wearable microwave imaging system for detecting tibia fractures, which is a non-invasive system equipped with an artificial intelligence-based decision mechanism.

[0014] More preferably, the invention provides an artificial intelligence-based, wearable microwave imaging system for detecting tibia fractures.

[0015] The invention comprises the following components:

[0016] a) Antenna

[0017] b) Adjustable fixation frame

[0018] c) Adjustable outer casing

[0019] d) Wi-Fi module

[0020] e) Portable VNA

[0021] f) Portable result display

[0022] Manufacturing method for the invention is as follows:

[0023] 1. Antenna: An element configured to generate radiation with a directional radiation pattern, operating in the 1-12 GHz frequency range with a gain value of 5 dBi. 2. Adjustable fixation frame: Used to securely hold the extremity within the outer casing without compression, and to enable the antenna elements to center the tissue with respect to the far-field point.

[0024] 3. Adjustable outer casing: A casing system around the body tissue into which the antennas are integrated. Owing to its adjustable structure, it provides general usability across all age groups.

[0025] 4. Wi-Fi module: By means of a Wi-Fi module mounted on the VNA, data are transmitted to a portable evaluation and display mechanism (Raspberry Pi 5). 5. Portable VNA: By means of a portable vector network analyzer (VNA), the scattering parameters of the antennas are read in real time.

[0026] 6. Portable evaluation and display mechanism: The data transmitted via the Wi-Fi module are processed by the portable evaluation and display mechanism, wherein fracture location images and diagnostic information relating to fracture type andfracture width are presented on the display based on reflection and transmission coefficient results derived from the measured scattering parameters using the artificial intelligence algorithm.

[0027] Figure 1 illustrates the antenna (1), antenna connections (2), VNA (3), and result display of the artificial intelligence-based, wearable microwave imaging system for detecting tibia fractures.

[0028] Figure 2 illustrates the adjustable fixation frame (1), adjustable outer casing (2), and antenna (3) of the artificial intelligence-based, wearable microwave imaging system for detecting tibia fractures.

Claims

CLAIMS1. An artificial intelligence-based, wearable microwave imaging system for detecting tibia fractures, suitable for use in various environments requiring fracture imaging, including hospitals, ambulances, field environments, and military applications, characterized in that the system is non-ionizing.

2. A system according to claim 1, characterized in that the system is cost-effective and portable.

3. A system according to claim 1, characterized in that the system is provided as an artificial intelligence-based, non-invasive system.

4. A system according to claim 1, characterized in that the system includes an adjustable outer casing suitable for different age groups.

5. A system according to claim 1, characterized in that the system includes a Wi-Fi module for data transmission.

6. A system according to claim 1 , characterized in that the system includes a portable evaluation and display mechanism enabled by artificial intelligence for visualizing the data.

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