Risk prediction method and device for suffering from osteoporotic fracture

Abstract

The invention provides a risk prediction method and device for suffering from osteoporotic fracture. The method comprises the following steps: counting basic information of a target person; performing VBQ scoring based on the magnetic resonance imaging of the target person; calculating an HU value based on the CT image of the target person; and substituting the counted basic information of the target person and the calculated VBQ score and HU value into a score table to obtain the osteoporosis fracture risk grade of the target person. According to the invention, the risk of the osteoporotic fracture of the target person can be predicted more accurately, so that targeted prevention is carried out, and the harm of the osteoporotic fracture to middle-aged and elderly people is reduced.

Description

Technical Field

[0001] This invention relates to the field of fracture risk prediction technology, specifically to a method and apparatus for predicting the risk of osteoporotic fractures. Background Technology

[0002] With the increasing aging of my country's population, healthy aging has become a social focus. Osteoporotic fractures are the most common complication of osteoporosis, often recurring repeatedly, severely reducing the quality of life of the elderly and increasing mortality rates. However, osteoporotic fractures have an insidious onset, low clinical consultation rates, and high rates of missed diagnosis, making it difficult for targeted individuals to receive timely and effective treatment.

[0003] Bone strength is an important reference indicator for detecting and evaluating osteoporosis. However, due to the fact that this indicator can only be obtained through destructive testing methods, it is difficult to directly assess the bone biomechanical properties of target individuals in clinical practice. Bone mineral density (BMD), on the other hand, is closely related to bone strength and has a strong correlation with fracture risk. Currently, the main clinical methods for measuring BMD include dual-energy X-ray absorptiometry (DXA) and quantitative computed tomography (QCT). The use of DXA and QCT to measure BMD for diagnosing osteoporosis is widely used. DXA is economical, simple, and results in a low radiation dose for patients, and is currently considered the "gold standard" for diagnosing osteoporosis. However, DXA uses two-dimensional imaging technology, resulting in low measurement accuracy. Its projective scanning method cannot analyze the three-dimensional spatial structure of bone or differentiate mineral density changes between the cortex and trabeculae, only providing two-dimensional area BMD information. This characteristic can lead to errors in fracture risk assessment due to factors such as local osteophytes and pannus. Currently, there is no effective method for predicting the risk of fractures caused by osteoporosis. Clinically, there is an urgent need for more reliable and convenient bone mineral density assessment methods to compensate for the shortcomings of traditional methods, to more comprehensively assess bone health, to detect and intervene in osteoporosis early, and to reduce the risk of fractures. Summary of the Invention

[0004] In view of the deficiencies in the prior art, the purpose of this invention is to provide a method and device for predicting the risk of osteoporotic fractures, which can more accurately predict the risk of osteoporotic fractures in target individuals, thereby enabling targeted prevention and reducing the harm of osteoporotic fractures to middle-aged and elderly people.

[0005] To solve the above problems, the technical solution of the present invention is as follows:

[0006] A method for predicting the risk of osteoporotic fractures includes the following steps:

[0007] Collect basic information about the target personnel;

[0008] VBQ scoring based on magnetic resonance imaging of the target personnel;

[0009] Calculate HU value based on CT images of the target personnel;

[0010] Substitute the basic information of the target personnel, the calculated VBQ score and HU value into the scoring table to obtain the osteoporosis fracture risk level of the target personnel.

[0011] Preferably, in the step of collecting basic information of the target personnel, the basic information includes the target personnel's age, gender, and BMI information.

[0012] Preferably, the step of performing VBQ scoring based on magnetic resonance imaging of the target person specifically includes:

[0013] Image acquisition: Acquire T1-weighted MRI images of the lumbar spine of the target individual;

[0014] Signal measurement: Using image processing software, the signal intensity of the L1-L4 vertebral bodies and the signal intensity of the selected reference L3 cerebrospinal fluid were measured.

[0015] VBQ calculation: The average signal intensity of the vertebral body is divided by the signal intensity of the reference object to obtain the VBQ score.

[0016] Prior to this, the step of calculating the HU value based on the CT images of the target person specifically includes: selecting three axial layers of the vertebral body, namely the layer below the superior endplate, the middle layer of the vertebral body, and the layer above the inferior endplate; using image processing software, placing regions of interest (ROIs) on the three layers respectively; obtaining the HU values ​​of the ROIs on the three layers respectively; and then calculating the average HU value of each lumbar vertebral body, using the average HU value of the L1-L4 vertebral bodies as the HU value of the target person.

[0017] Prior to this, the step of substituting the basic information of the target personnel and the calculated VBQ score and HU value into the scoring table to obtain the osteoporotic fracture risk level of the target personnel specifically includes: substituting the basic information of the target personnel's age, gender and BMI into the scoring table based on the calculated VBQ and HU value; selecting the most suitable option from the five assessment dimensions and recording the score according to the specific situation of the target personnel; finally, adding the five scores together to obtain the total score and corresponding osteoporotic fracture risk level.

[0018] Furthermore, the present invention also provides a risk prediction device for osteoporotic fractures, including a processor and a memory for storing executable instructions of the processor, the processor being configured to perform the risk prediction method for osteoporotic fractures as described above by executing the executable instructions.

[0019] Compared with existing technologies, the method of this invention integrates bone mineral density assessment into the routine CT and MRI examination process. Based on the calculated VBQ score and HU value, the osteoporosis fracture risk level of the target individuals is obtained, thereby improving the screening efficiency of osteoporosis fracture risk and reducing screening costs. It has the advantages of simple operation, speed and non-invasiveness, making it convenient for large-scale screening in clinical practice, early detection of high-risk groups, and early diagnosis and treatment of osteoporosis. Attached Figure Description

[0020] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0021] Figure 1 This is a flowchart of the risk prediction method for osteoporotic fractures according to the present invention. Detailed Implementation

[0022] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0023] Specifically, this invention provides a method for predicting the risk of osteoporotic fractures, such as... Figure 1 As shown, the method includes the following steps:

[0024] S1: Collect basic information about the target personnel;

[0025] Specifically, the basic information includes the target person's age, gender, and BMI.

[0026] S2: VBQ scoring based on magnetic resonance imaging of the target personnel;

[0027] Specifically, Vertebral Bone Quality (VBQ) is a method for assessing bone quality based on magnetic resonance imaging (MRI). The VBQ score reflects bone quality and strength by analyzing the signal intensity of the vertebrae in MRI images. Compared to traditional bone mineral density (BMD) measurements, the VBQ score provides a more comprehensive assessment of bone microstructure and mechanical properties, offering more accurate information for the diagnosis and risk assessment of osteoporosis. The VBQ score is calculated primarily by comparing the signal intensity of the vertebral bone marrow and cerebrospinal fluid, indirectly reflecting bone marrow fat content. Specifically, it typically uses the average signal intensity of the L1-L4 vertebrae divided by the signal intensity of the L3 vertebral cerebrospinal fluid.

[0028] Compared to traditional DXA, the biggest advantages of VBQ are its convenience and cost-effectiveness. VBQ scoring can be performed on existing MRI scanners without the need for additional scans or equipment, thus reducing screening costs and radiation exposure for the target population. Furthermore, VBQ scoring is easy to learn and implement; clinicians can quickly master the technique and apply it to their daily clinical practice.

[0029] The VBQ scoring specifically includes the following steps:

[0030] (1) Image acquisition: Acquire T1-weighted MRI images of the lumbar spine of the target personnel. Ideally, the images should have clear vertebral structures and a good signal-to-noise ratio.

[0031] (2) Signal measurement: Using image processing software, the signal intensity of the L1-L4 vertebral bodies and the signal intensity of the selected reference object (such as L3 cerebrospinal fluid) were measured;

[0032] (3) VBQ calculation: Divide the average vertebral body signal intensity by the reference signal intensity to obtain the VBQ score. The formula is: VBQ = average vertebral body signal intensity / reference signal intensity.

[0033] S3: Calculate the HU value based on the CT images of the target personnel;

[0034] The Hounsfield Unit (HU) is a standard unit used to quantify the attenuation coefficient of matter in X-ray computed tomography (CT) images. It provides an objective and repeatable method for assessing tissue density. The HU value distinguishes tissues of different densities; for example, bone tissue typically has a high HU value because it contains a large amount of calcium, which attenuates X-rays more effectively. Adipose tissue typically has a negative HU value, close to that of water. Analyzing the HU values ​​of different regions in CT images can be used to screen for osteoporosis. Existing research indicates that a HU value below 97.06 can be used as a reference for diagnosing osteoporosis. Furthermore, studies have shown that a HU value greater than 160 indicates a significantly reduced risk of osteoporosis, while a HU value less than 110 is significantly associated with osteoporosis.

[0035] The HU value is an objective quantitative indicator that can be obtained directly from CT images and reflects bone density. Clinicians or radiologists can measure the HU value of the vertebrae from existing CT images to provide an immediate assessment of bone density in target individuals. This greatly improves osteoporosis screening coverage, especially for those who present with acute problems but have never undergone bone density screening.

[0036] Specifically, three axial views of the vertebral body are selected: the layer below the superior endplate, the middle layer of the vertebral body, and the layer above the inferior endplate. Using image processing software, a region of interest (ROI) of the largest possible size is placed in each of the three layers, and the HU value of the ROI in each layer is obtained. Then, the average HU value of each lumbar vertebra is calculated, and the average HU value of the L1-L4 vertebrae is used as the HU value of the target person.

[0037] Compared to DXA, HU value provides three-dimensional bone mineral density information, enabling a more comprehensive assessment of bone quality and strength. More importantly, HU value can be obtained from routine CT scans, requiring no additional radiation exposure or examination costs, giving it a unique advantage in osteoporosis screening. Furthermore, HU value can be used to evaluate the effectiveness of osteoporosis treatment; by comparing changes in HU value before and after treatment, the efficacy of medications can be objectively assessed.

[0038] S4: Substitute the basic information of the target personnel, the calculated VBQ score and HU value into the scoring table to obtain the osteoporosis fracture risk level of the target personnel.

[0039] VBQ score and HU value are two different osteoporosis screening methods. Combining HU value and VBQ score can significantly improve screening accuracy and has superior screening value. Therefore, based on the target personnel's age, gender, BMI and other basic information, as well as VBQ score and HU value, a scoring table is summarized as shown in Table 1 below. For any target personnel, this scoring table can be used to determine their risk level of osteoporotic fracture, as shown in Table 2 below.

[0040] Specifically, based on the age, gender, and BMI of the target individuals, combined with the calculated VBQ and HU values, the scores are entered into a scoring table to obtain the osteoporotic fracture risk level of the target individuals, facilitating clinicians in making further treatment plans. Depending on the specific circumstances of the target individuals, the most suitable option is selected from five assessment dimensions, and the score is recorded. Finally, the scores of the five items are added together to obtain a total score and the corresponding risk level.

[0041] Evaluation Dimensions Scoring criteria and options Score 1. Age ≤50 years old (lower risk) 0 points 51-60 years old (risk increases with age) 1 point >60 years old (high-risk age group) 2 points 2. Gender male 0 points Women (postmenopausal women have a significantly increased risk) 1 point 3. BMI: Calculation formula: weight (kg) / height (m)² 20-26.9 (Normal or slightly overweight, with strong protective factors) 0 points ≥27 (Overweight, reduced protection) 1 point <20 (Underweight, risk significantly increased) 2 points 4. VBQ Rating ≤2.5 (Normal bone marrow fat content, good bone quality) 0 points 2.6 - 3.5 (Slightly elevated bone marrow fat content, decreased bone mass) 1 point >3.5 (Significantly elevated bone marrow fat content, poor bone quality) 2 points 5. HU value ≥160 HU (normal bone mineral density) 0 points 110 - 159 HU (Osteopenia) 1 point <110 HU (reaching the diagnostic threshold for osteoporosis) 2 points

[0042] Table 1

[0043] Total score range Risk level Clinical interpretation and recommendations 0 - 3 points Low risk The current risk of fracture is low. It is recommended to maintain a healthy lifestyle, including a balanced diet (rich in calcium and vitamin D) and adequate outdoor activity. Bone density should be checked every 3-5 years. 4 - 6 points Medium risk There are clear risk factors for fracture. Further detailed examination (such as DXA bone mineral density testing) is recommended to confirm the diagnosis. Basic interventions should begin under the guidance of a physician, which may include calcium and vitamin D supplementation and strict fall prevention. ≥7 points High risk You are at high risk of osteoporotic fractures. Immediate clinical intervention is strongly recommended, including a visit to an osteoporosis specialist clinic and likely initiation of anti-osteoporosis medication under a doctor's guidance. It is also essential to strengthen fall prevention measures.

[0044] Table 2

[0045] Osteoporotic fractures depend on two major factors: bone mineral density (BMD) and bone quality. A patient may only have a slightly decreased BMD (DXA or HU), but if the VBQ score is significantly elevated, it indicates that their bone quality may be very poor, and the risk of fracture remains high. This invention's dual "density-quality" assessment model greatly enriches and improves the predictive ability of fracture risk, enabling more accurate individualized assessment.

[0046] Furthermore, the present invention also provides a risk prediction device for osteoporotic fractures, including a processor and a memory for storing executable instructions of the processor, the processor being configured to perform the risk prediction method for osteoporotic fractures as described above by executing the executable instructions.

[0047] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A method for predicting the risk of osteoporotic fractures, characterized in that, The method includes the following steps: Collect basic information about the target personnel; VBQ scoring based on magnetic resonance imaging of the target personnel; Calculate HU value based on CT images of the target personnel; Substitute the basic information of the target personnel, the calculated VBQ score and HU value into the scoring table to obtain the osteoporosis fracture risk level of the target personnel.

2. The risk prediction method for osteoporotic fractures according to claim 1, characterized in that, In the step of collecting basic information about the target personnel, the basic information includes the target personnel's age, gender, and BMI information.

3. The risk prediction method for osteoporotic fractures according to claim 1, characterized in that, The steps for VBQ scoring based on magnetic resonance imaging of the target person specifically include: Image acquisition: Acquire T1-weighted MRI images of the lumbar spine of the target individual; Signal measurement: Using image processing software, the signal intensity of the L1-L4 vertebral bodies and the signal intensity of the selected reference L3 cerebrospinal fluid were measured. VBQ calculation: The average signal intensity of the vertebral body is divided by the signal intensity of the reference object to obtain the VBQ score.

4. The risk prediction method for osteoporotic fractures according to claim 1, characterized in that, The steps for calculating the HU value based on the CT images of the target person specifically include: selecting three axial layers of the vertebral body, namely the layer below the superior endplate, the middle layer of the vertebral body, and the layer above the inferior endplate; using image processing software, placing regions of interest (ROIs) on the three layers respectively; obtaining the HU values ​​of the ROIs on the three layers respectively; and then calculating the average HU value of each lumbar vertebral body, using the average HU value of the L1-L4 vertebral bodies as the HU value of the target person.

5. The risk prediction method for osteoporotic fractures according to claim 1, characterized in that, The steps for obtaining the osteoporotic fracture risk level of the target personnel by substituting the basic information of the target personnel, the calculated VBQ score and HU value into the scoring table are as follows: based on the basic information of the target personnel's age, gender and BMI, combined with the calculated VBQ and HU value, the scores are substituted into the scoring table; based on the specific circumstances of the target personnel, the most suitable option is selected from the five assessment dimensions and the score is recorded; finally, the scores of the five items are added together to obtain the total score and the corresponding osteoporotic fracture risk level.

6. A risk prediction device for osteoporotic fractures, characterized in that, The device includes a processor and a memory for storing executable instructions of the processor, the processor being configured to perform the risk prediction method for osteoporotic fractures as described in any one of claims 1 to 5 by executing the executable instructions.

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