Artificial intelligence auxiliary analysis system based on orthopedic images

Abstract

The invention relates to the field of image processing, in particular to an artificial intelligence auxiliary analysis system based on orthopedic images, which comprises an image acquisition module, a grade determination module, a fracture detection module, a treatment detection module and a processing module. The fracture degree and the corresponding healing condition can be more accurately judged by comprehensively analyzing the initial bone image and the post-treatment bone image in combination with factors such as the age and the gender of a patient, the diagnosis accuracy is greatly improved, the bone healing rate is obtained by comparing the initial bone fracture proportion and the post-treatment bone fracture proportion, and the diagnosis accuracy is improved. Reliable data support is provided for evaluating the treatment effect, diagnosis and evaluation time can be greatly shortened, the overall treatment efficiency can be improved, whether the treatment effect is good or not is judged according to clinical data, suggestions about whether an operation needs to be conducted again or rehabilitation guidance needs to be given, the risk of human judgment errors can be reduced, and the treatment efficiency is improved. And the accuracy of diagnosis and treatment decisions is improved.

Description

Technical Field

[0001] The present invention relates to the field of image processing, and in particular to an artificial intelligence-assisted analysis system based on orthopedic images. Background Art

[0002] Orthopedic diseases, especially fractures, are common and frequently occurring diseases in clinical medicine, which seriously affect patients' daily life and physical health. Their diagnosis and evaluation of treatment effects mainly rely on medical imaging examinations. With the development of medical technology, imaging examination methods such as X-rays, CT, and MRI have been continuously improved, providing orthopedic surgeons with a large amount of diagnostic basis. However, the current analysis and diagnosis of orthopedic images mainly rely on the doctor's experience and clinical judgment as well as simple imaging indicators. There are problems such as strong subjectivity and low efficiency, and there are certain limitations. For example, doctors rely solely on naked eye observation and simple imaging examinations, which makes it difficult to accurately evaluate the degree of fracture, healing status, and treatment effects, which can easily lead to missed diagnosis, misdiagnosis, or delayed treatment, ultimately affecting the patient's recovery.

[0003] The patent document with publication number CN112837264A discloses a rib positioning, fracture clinical outcome prediction device and automatic diagnosis system, wherein the rib positioning device is used to output the anatomical positioning label of the rib; the rib fracture clinical outcome prediction device is used to predict the clinical outcome of rib fracture; the rib fracture classification device can detect and classify rib fractures, and output refined rib fracture classification labels, including fresh dislocated / non-dislocated fractures, healing fractures, old fractures, and pathological fractures.

[0004] This shows the following problem: In the prior art, it is impossible to objectively evaluate the treatment effect of the patient during the postoperative recovery process according to the patient's condition, resulting in low treatment efficiency and low diagnostic accuracy. Summary of the invention

[0005] To this end, the present invention provides an artificial intelligence-assisted analysis system based on orthopedic images, which is used to determine the treatment effect according to the patient's condition and make auxiliary diagnosis feedback to overcome the problem in the prior art that it is impossible to objectively evaluate the treatment effect according to the patient's condition during the postoperative recovery process, resulting in low treatment efficiency and low diagnostic accuracy.

[0006] To achieve the above object, the present invention provides an artificial intelligence-assisted analysis system based on orthopedic images, comprising:

[0007] An image acquisition module, used to acquire bone images of the patient's fractured bones, including an initial bone image and a therapeutic orthopedic image;

[0008] a grade determination module connected to the image acquisition module, for determining the bone grade of the patient's bones based on the patient's bone width and a standard bone width range;

[0009] A fracture detection module, connected to the image acquisition module, for determining the fracture grade based on the standard bone fracture ratio range and the first bone fracture ratio;

[0010] a treatment detection module, connected to the image acquisition module, for determining a second bone fracture ratio of the patient according to the treatment bone image;

[0011] A processing module is connected to the grade determination module, the fracture detection module and the treatment detection module, and is used to determine the bone healing rate according to the first bone fracture ratio and the second bone fracture ratio, and to determine the difference value between the bone healing rate and the preset standard bone healing rate, and to construct recovery suggestions based on the difference value.

[0012] Furthermore, the processing module includes:

[0013] a standard determination unit, for determining a standard bone healing rate according to the bone grade and the fracture grade;

[0014] a healing determination unit connected to the standard determination unit, and used to process the first bone fracture ratio and the second bone fracture ratio to obtain the bone healing rate representing the ratio relationship between the two;

[0015] A treatment determination unit is connected to the healing determination unit and is used to construct a recovery suggestion based on the difference between the bone healing rate and the standard bone healing rate.

[0016] Further, the treatment determination unit comprises:

[0017] A comparison subunit, used for comparing the bone healing rate with the standard bone healing rate to obtain a comparison result;

[0018] a difference calculation subunit, connected to the comparison subunit, for calculating the difference value by dividing the bone healing rate by the preset healing rate when the comparison result shows that the bone healing rate is less than the standard healing rate;

[0019] The feedback subunit is connected to the difference calculation subunit and is used to construct a recovery suggestion according to a comparison result between the difference value and a preset difference value.

[0020] Furthermore, the level determination module includes:

[0021] A determination unit, used to determine a standard bone width range consisting of a minimum standard bone width and a maximum standard bone width according to basic information of the patient, wherein the basic information of the patient includes age and gender;

[0022] A bone comparison unit connected to the determination unit, configured to determine the bone width of the patient through the initial bone image, and compare it with the standard bone width range to obtain a comparison result;

[0023] The determination unit and the bone comparison unit are used to determine the bone grade according to the comparison result.

[0024] Furthermore, the determination unit includes:

[0025] A first determination subunit, configured to determine that the patient's bone is a small bone when the bone width is smaller than the minimum standard bone width;

[0026] A second determination subunit is used to determine that the patient's bone is a standard bone when the bone width is neither less than the minimum standard bone width nor greater than the maximum standard bone width;

[0027] The first determination subunit is used to determine that the patient's bone is a large bone when the bone width is greater than the maximum standard bone width.

[0028] Furthermore, the fracture detection module comprises:

[0029] A setting unit, used to set a standard bone fracture ratio range consisting of a minimum standard bone fracture ratio and a maximum standard bone fracture ratio;

[0030] a comparison unit connected to the setting unit, and used to compare the first bone fracture ratio with the standard bone fracture ratio range to obtain a comparison result;

[0031] The grade determination unit is connected to the comparison unit and is used to determine the fracture grade based on the comparison result.

[0032] Furthermore, the comparison unit comprises:

[0033] The acquisition subunit is used to analyze the initial bone image to obtain the first bone fracture width:

[0034] a calculation subunit, connected to the acquisition subunit, for calculating the first bone fracture ratio according to the bone width and the first bone fracture width;

[0035] Comparison subunit: used to compare the first bone fracture ratio with the standard bone fracture ratio range to obtain a comparison result.

[0036] Furthermore, the level determination unit includes:

[0037] A first determination subunit, configured to determine a slight fracture when the first bone fracture ratio is less than a minimum standard bone fracture ratio;

[0038] A second determination subunit is used to determine that the fracture is a moderate fracture when the first bone fracture ratio is neither less than the minimum standard bone fracture ratio nor greater than the maximum standard bone fracture ratio;

[0039] The third determination subunit is used to determine that the fracture is severe when the first bone fracture ratio is greater than the maximum standard bone fracture ratio.

[0040] Furthermore, the treatment detection module comprises:

[0041] An analysis unit, used for analyzing the treated bone image to obtain a second bone width and a bone width;

[0042] A calculation unit is connected to the analysis unit and is used to take the quotient of the second bone width divided by the bone width as the second bone fracture ratio.

[0043] Furthermore, the image acquisition module includes:

[0044] A first acquisition unit is used to acquire the initial bone image obtained by photographing the fracture site of the patient when the fracture occurs;

[0045] The second acquisition unit is used to acquire the treated bone image of the patient after a period of time after the operation.

[0046] Compared with the prior art, the beneficial effect of the present invention lies in that, by comprehensively analyzing the initial bone images and the bone images after treatment, combined with factors such as the patient's age and gender, the degree of fracture and the corresponding healing condition can be more accurately judged, thereby greatly improving the accuracy of diagnosis. By comparing the initial and post-treatment bone fracture ratios, the bone healing rate can be obtained, providing reliable data support for evaluating the treatment effect, which can greatly shorten the diagnosis and evaluation time and improve the overall treatment efficiency. Based on clinical data, it can be judged whether the treatment effect is good, and recommendations on whether re-surgery or rehabilitation guidance is needed can be given, which can reduce the risk of human misjudgment and improve the accuracy of diagnosis and treatment decisions, thereby reducing potential medical errors and effectively assisting doctors in making clinical decisions.

[0047] Furthermore, by comprehensively considering the bone grade and fracture grade, a personalized standard bone healing rate is determined for each patient. By comparing the actual healing rate with the standard healing rate, an objective basis is provided for determining the treatment effect, the treatment progress is accurately quantified, and the accuracy of judgment is greatly improved. This provides strong support for doctors' clinical decision-making and helps to formulate better follow-up treatment plans.

[0048] Furthermore, by comparing the actual bone healing rate with the standard bone healing rate, whether the treatment effect is good can be judged in time according to the specific situation of the bone healing rate, potential problems can be discovered early, and a basis can be provided for timely adjustment of treatment plans. It can distinguish patients who need reoperation or only need rehabilitation guidance, assist doctors in determining treatment plans, and help them make a more scientific choice between reoperation and conservative treatment, thus avoiding the risk of inadequate treatment, improving the overall quality of medical care, and helping to improve diagnostic efficiency.

[0049] Furthermore, by determining the corresponding standard range based on the patient's actual bone width and age and gender, the system can select the corresponding reference standard based on the patient's specific age and gender, thereby improving the accuracy of the assessment and providing a data basis for subsequent bone grade judgments, making the judgment more scientific and reasonable. The system automatically completes the entire process from determining the standard range based on age and gender, measuring the actual bone width to comparative analysis, thereby improving efficiency, providing doctors with important reference information, and helping to improve the accuracy of diagnosis.

[0050] Furthermore, by setting a standard bone width range according to the patient's age and gender and classifying the patient's actual bone width, it can more accurately reflect the bone characteristics of each patient, facilitate the formulation of subsequent treatment plans and postoperative evaluation, significantly improve diagnostic efficiency, and reduce the workload of doctors. The standard bone width range can be adjusted according to different populations or research needs, making the system adaptable and extensible.

[0051] Furthermore, by calculating the ratio of the bone break width to the overall bone width, the severity of the fracture can be accurately quantified. Taking into account the differences in bone width among each patient, the use of proportional indicators can better adapt to individual differences and provide more personalized assessment results, which helps to provide accuracy in evaluating treatment effectiveness.

[0052] Furthermore, through the preset standard range, the fracture conditions are uniformly divided into three levels: mild, moderate and severe, providing a quantitative reference for the degree of fracture, which is convenient for quickly judging the severity of the fracture and helps to more accurately evaluate the patient's recovery based on subsequent postoperative conditions. Taking into account the differences in bone structure among different patients, the corresponding fracture ratio is used to improve the universality and accuracy of the evaluation, assist clinical diagnosis, and reduce errors that may be caused by subjective judgment.

[0053] Furthermore, by analyzing the bone images after treatment, the width of the second bone fracture and the bone width can be accurately measured to provide an accurate data basis for subsequent calculations. By comparing the initial and post-treatment bone fracture ratios, the progress of fracture healing can be reflected, providing doctors with treatment effect evaluation, which helps to improve diagnostic efficiency.

[0054] Furthermore, by acquiring images at the early stage of fracture and after treatment, direct comparison material is provided, which can reflect the dynamic process of fracture healing, comprehensively grasp the degree of fracture healing of patients a period of time after surgery, provide a comprehensive data basis for subsequent analysis, and accurately evaluate the treatment effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a system framework diagram of an artificial intelligence-assisted analysis system based on orthopedic images according to an embodiment of the present invention;

[0056] Figure 2 It is a structural schematic diagram of a processing module according to an embodiment of the present invention;

[0057] Figure 3 This is a schematic diagram of the structure of a level determination module according to an embodiment of the present invention;

[0058] Figure 4 Schematic diagram of the structure of a fracture detection module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0059] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0060] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0061] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0062] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0063] See also Figure 1 ,like Figure 1As shown, it is a system framework diagram of an artificial intelligence-assisted analysis system based on orthopedic images according to an embodiment of the present invention;

[0064] Specifically, the artificial intelligence-assisted analysis system based on orthopedic images provided by the embodiment of the present invention includes:

[0065] An image acquisition module, used to acquire bone images of the patient's fractured bones, including an initial bone image and a therapeutic orthopedic image;

[0066] a grade determination module connected to the image acquisition module, for determining the bone grade of the patient's bones based on the patient's bone width and a standard bone width range;

[0067] A fracture detection module, connected to the image acquisition module, for determining the fracture grade based on the standard bone fracture ratio range and the first bone fracture ratio;

[0068] a treatment detection module, connected to the image acquisition module, for determining a second bone fracture ratio of the patient according to the treatment bone image;

[0069] A processing module is connected to the grade determination module, the fracture detection module and the treatment detection module, and is used to determine the bone healing rate according to the first bone fracture ratio and the second bone fracture ratio, and to determine the difference value between the bone healing rate and the preset standard bone healing rate, and to construct recovery suggestions based on the difference value.

[0070] Specifically, when a patient has a fracture, an initial bone image of the bone at the fracture site is acquired, and a therapeutic bone image of the patient is acquired again some time after the operation. The patient's bone grade can be determined based on the patient's bone image, the patient's age and gender, and the detected bone width of the patient to reflect the patient's physical condition. Based on the patient's bone grade and the severity of his fracture, a certain prediction can be made about the recovery of bone healing after surgery to obtain his standard bone healing rate. The patient's bone healing rate can be calculated based on the therapeutic bone image acquired again some time after the operation. Based on the calculated bone healing rate, it can be determined whether the operation was successful, and whether certain measures have been taken to promote his own recovery after the operation.

[0071] Specifically, by comprehensively analyzing the initial bone images and the bone images after treatment, combined with factors such as the patient's age and gender, the degree of fracture and the corresponding healing condition can be judged more accurately, greatly improving the accuracy of diagnosis. By comparing the initial and post-treatment bone fracture ratios, the bone healing rate can be obtained, providing reliable data support for evaluating the treatment effect, which can greatly shorten the diagnosis and evaluation time and improve the overall treatment efficiency. Based on clinical data, it can be judged whether the treatment effect is good, and recommendations can be given on whether re-surgery or rehabilitation guidance is needed. This can reduce the risk of human misjudgment and improve the accuracy of diagnosis and treatment decisions, thereby reducing potential medical errors and effectively assisting doctors in making clinical decisions.

[0072] Please continue reading Figure 2 ,like Figure 2 As shown, it is a schematic diagram of the structure of the processing module of an embodiment of the present invention;

[0073] Specifically, the processing module includes:

[0074] a standard determination unit, for determining a standard bone healing rate according to the bone grade and the fracture grade;

[0075] a healing determination unit connected to the standard determination unit, and used to process the first bone fracture ratio and the second bone fracture ratio to obtain the bone healing rate representing the ratio relationship between the two;

[0076] A treatment determination unit is connected to the healing determination unit and is used to construct a recovery suggestion based on the difference between the bone healing rate and the standard bone healing rate.

[0077] Specifically, the standard bone healing rate is determined by comparing the patient's data with the data stored in the database.

[0078] Specifically, by comprehensively considering bone grade and fracture grade, a personalized standard bone healing rate is determined for each patient. By comparing the actual healing rate with the standard healing rate, an objective basis is provided for judging the treatment effect, and the treatment progress is accurately quantified, which greatly improves the accuracy of judgment, provides strong support for doctors' clinical decision-making, and helps to formulate better follow-up treatment plans.

[0079] Specifically, the treatment determination unit includes:

[0080] A comparison subunit, used for comparing the bone healing rate with the standard bone healing rate to obtain a comparison result;

[0081] a difference calculation subunit, connected to the comparison subunit, for calculating the difference value by dividing the bone healing rate by the preset healing rate when the comparison result shows that the bone healing rate is less than the standard healing rate;

[0082] The feedback subunit is connected to the difference calculation subunit and is used to construct a recovery suggestion according to a comparison result between the difference value and a preset difference value.

[0083] Specifically, when the difference value is less than or equal to the preset difference value, rehabilitation guidance is recommended, and when the difference value is greater than the preset difference value, surgical treatment is recommended.

[0084] During the specific implementation process, the stored big data revealed that the standard bone healing rate of a man with a fracture was 30%, the first bone fracture ratio was 10%, and the second bone fracture ratio was 9%. The bone healing rate during the period after treatment was calculated to be 10%, which is less than the standard bone healing rate of 30%, indicating that the treatment effect was not good. The difference value was calculated to be 33%, and the preset difference value was 30%. The difference value was greater than the preset difference value, and the recovery recommendation was constructed to undergo surgical treatment.

[0085] Specifically, by comparing the actual bone healing rate with the standard bone healing rate, and judging whether the treatment effect is good in time according to the specific situation of the bone healing rate, potential problems can be discovered early, providing a basis for timely adjustment of treatment plans, distinguishing patients who need reoperation or only need rehabilitation guidance, assisting doctors in determining treatment plans, and helping them make a more scientific choice between reoperation and conservative treatment, avoiding the risk of inadequate treatment, improving the overall quality of medical care, and helping to improve diagnostic efficiency.

[0086] Please continue reading Figure 3 ,like Figure 3 As shown, it is a schematic diagram of the structure of the level determination module of an embodiment of the present invention;

[0087] Specifically, the level determination module includes:

[0088] A determination unit, used to determine a standard bone width range consisting of a minimum standard bone width and a maximum standard bone width according to basic information of the patient, wherein the basic information of the patient includes age and gender;

[0089] A bone comparison unit connected to the determination unit, configured to determine the bone width of the patient through the initial bone image, and compare it with the standard bone width range to obtain a comparison result;

[0090] The determination unit and the bone comparison unit are used to determine the bone grade according to the comparison result.

[0091] Specifically, the determination unit stores standard bone width ranges for people of different age and gender groups, and screens the standard range library based on the patient's actual situation to obtain a standard range corresponding to the patient. The standard is then compared with the patient's bone condition to determine the patient's bone grade.

[0092] Specifically, by determining the corresponding standard range based on the patient's actual bone width, age and gender, the system can select the corresponding reference standard based on the patient's specific age and gender, thereby improving the accuracy of the assessment and providing a data basis for subsequent bone grade judgments, making the judgment more scientific and reasonable. The system automatically completes the entire process from determining the standard range based on age and gender, measuring the actual bone width to comparative analysis, thereby improving efficiency, providing doctors with important reference information, and helping to improve the accuracy of diagnosis.

[0093] Specifically, the determination unit includes:

[0094] A first determination subunit, configured to determine that the patient's bone is a small bone when the bone width is smaller than the minimum standard bone width;

[0095] A second determination subunit is used to determine that the patient's bone is a standard bone when the bone width is neither less than the minimum standard bone width nor greater than the maximum standard bone width;

[0096] The first determination subunit is used to determine that the patient's bone is a large bone when the bone width is greater than the maximum standard bone width.

[0097] During the specific implementation process, a 35-year-old male patient had a fracture in the tibia shaft of his lower leg. In the standard range library, the corresponding standard bone width range is 25mm-35mm. His bone width was detected to be 30mm. His bone width was neither less than the minimum standard bone width nor greater than the maximum standard bone width. It was determined to be a standard bone within the standard bone width range.

[0098] Specifically, by setting a standard bone width range according to the patient's age and gender and classifying the patient's actual bone width, it can more accurately reflect the bone characteristics of each patient, facilitate the formulation of subsequent treatment plans and postoperative evaluation, significantly improve diagnostic efficiency, and reduce the workload of doctors. The standard bone width range can be adjusted according to different populations or research needs, making the system adaptable and extensible.

[0099] Please continue reading Figure 4 ,like Figure 4 As shown, it is a structural schematic diagram of a fracture detection module according to an embodiment of the present invention;

[0100] Specifically, the fracture detection module includes:

[0101] A setting unit, used to set a standard bone fracture ratio range consisting of a minimum standard bone fracture ratio and a maximum standard bone fracture ratio;

[0102] a comparison unit connected to the setting unit, and used to compare the first bone fracture ratio with the standard bone fracture ratio range to obtain a comparison result;

[0103] The grade determination unit is connected to the comparison unit and is used to determine the fracture grade based on the comparison result.

[0104] Specifically, the comparison unit includes:

[0105] The acquisition subunit is used to analyze the initial bone image to obtain the first bone fracture width:

[0106] a calculation subunit, connected to the acquisition subunit, for calculating the first bone fracture ratio according to the bone width and the first bone fracture width;

[0107] Comparison subunit: used to compare the first bone fracture ratio with the standard bone fracture ratio range to obtain a comparison result.

[0108] In the specific implementation process, a man's bone width is 30mm. Through the initial bone image analysis, it is found that the width of the first bone fracture is 3mm. The first bone fracture ratio is the ratio of the first bone fracture width to the bone width, which is 10%.

[0109] Specifically, by calculating the ratio of the width of the bone break to the overall bone width, the severity of the fracture can be accurately quantified. Taking into account the differences in bone width among each patient, the use of proportional indicators can better adapt to individual differences, provide more personalized assessment results, and help provide accuracy in evaluating treatment effectiveness.

[0110] Specifically, the level determination unit includes:

[0111] A first determination subunit, configured to determine a slight fracture when the first bone fracture ratio is less than a minimum standard bone fracture ratio;

[0112] A second determination subunit is used to determine that the fracture is a moderate fracture when the first bone fracture ratio is neither less than the minimum standard bone fracture ratio nor greater than the maximum standard bone fracture ratio;

[0113] The third determination subunit is used to determine that the fracture is severe when the first bone fracture ratio is greater than the maximum standard bone fracture ratio.

[0114] In the specific implementation process, the standard bone fracture ratio is 15%-30%. A 35-year-old male patient suffered a fracture of the tibia shaft of his lower leg. The first bone fracture ratio at the time of his fracture was 10%, which was less than the minimum standard bone fracture ratio of 15%, and was determined to be a mild fracture.

[0115] Specifically, through the preset standard range, fracture conditions are uniformly divided into three levels: mild, moderate and severe, providing a quantitative reference for the degree of fracture, which is convenient for quickly judging the severity of the fracture and helps to more accurately evaluate the patient's recovery based on subsequent postoperative conditions. Taking into account the differences in bone structure among different patients, the corresponding fracture ratio is used to improve the universality and accuracy of the evaluation, assist clinical diagnosis, and reduce errors that may be caused by subjective judgment.

[0116] Specifically, the treatment detection module includes:

[0117] An analysis unit, used for analyzing the treated bone image to obtain a second bone width and a bone width;

[0118] A calculation unit is connected to the analysis unit and is used to take the quotient of the second bone width divided by the bone width as the second bone fracture ratio.

[0119] In the specific implementation process, a man's bone width was 30mm. Through bone image analysis after a period of time after surgical treatment, it was found that the width of the second bone fracture after treatment was 2.7mm, and the calculation showed that the second bone fracture ratio was 9%.

[0120] Specifically, by analyzing the bone images after treatment, the width of the second bone fracture and the bone width can be accurately measured to provide an accurate data basis for subsequent calculations. By comparing the initial and post-treatment bone fracture ratios, the progress of fracture healing can be reflected, providing doctors with treatment effect evaluation, which helps to improve diagnostic efficiency.

[0121] Specifically, the image acquisition module includes:

[0122] A first acquisition unit is used to acquire the initial bone image obtained by photographing the fracture site of the patient when the fracture occurs;

[0123] The second acquisition unit is used to acquire the treated bone image of the patient after a period of time after the operation.

[0124] Specifically, the image acquisition module acquires the initial bone image of the patient at the time of fracture and the treated bone image a period of time after surgical treatment through an image collector, wherein the image collector can be medical equipment used in hospitals such as X-ray and CT, and its specific structure is not limited here.

[0125] Specifically, by acquiring images at the early stage of fracture and after treatment, direct comparison material is provided, which can reflect the dynamic process of fracture healing, comprehensively grasp the degree of fracture healing of patients a period of time after surgery, provide a comprehensive data basis for subsequent analysis, and accurately evaluate the treatment effect.

[0126] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0127] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An artificial intelligence analysis auxiliary system based on orthopedic images, characterized in that: include: An image acquisition module, used to acquire bone images of the patient's fractured bones, including an initial bone image and a therapeutic orthopedic image; a grade determination module, connected to the image acquisition module, for determining the bone grade of the patient's bones based on the patient's bone width and a standard bone width range; A fracture detection module, connected to the image acquisition module, for determining the fracture grade based on the standard bone fracture ratio range and the first bone fracture ratio; a treatment detection module, connected to the image acquisition module, for determining a second bone fracture ratio of the patient according to the treatment bone image; A processing module is connected to the grade determination module, the fracture detection module and the treatment detection module, and is used to determine the bone healing rate according to the first bone fracture ratio and the second bone fracture ratio, and to determine the difference value between the bone healing rate and the preset standard bone healing rate, and to construct recovery suggestions based on the difference value.

2. The artificial intelligence analysis auxiliary system based on orthopedic images according to claim 1 is characterized in that: The processing module comprises: a standard determination unit, for determining a standard bone healing rate according to the bone grade and the fracture grade; a healing determination unit connected to the standard determination unit, and used to process the first bone fracture ratio and the second bone fracture ratio to obtain the bone healing rate representing the ratio relationship between the two; A treatment determination unit is connected to the healing determination unit and is used to construct a recovery suggestion based on the difference between the bone healing rate and the standard bone healing rate.

3. The artificial intelligence analysis auxiliary system based on orthopedic images according to claim 2 is characterized in that: The treatment determination unit comprises: A comparison subunit, used for comparing the bone healing rate with the standard bone healing rate to obtain a comparison result; a difference calculation subunit, connected to the comparison subunit, for calculating the difference value by dividing the bone healing rate by the preset healing rate when the comparison result shows that the bone healing rate is less than the standard healing rate; The feedback subunit is connected to the difference calculation subunit and is used to construct a recovery suggestion according to a comparison result between the difference value and a preset difference value.

4. The artificial intelligence analysis auxiliary system based on orthopedic images according to claim 1 is characterized in that: The level determination module comprises: A determination unit, used to determine a standard bone width range consisting of a minimum standard bone width and a maximum standard bone width according to basic information of the patient, wherein the basic information of the patient includes age and gender; A bone comparison unit connected to the determination unit, configured to determine the bone width of the patient through the initial bone image, and compare it with the standard bone width range to obtain a comparison result; The determination unit and the bone comparison unit are used to determine the bone grade according to the comparison result.

5. The artificial intelligence analysis auxiliary system based on orthopedic images according to claim 4 is characterized in that: The determination unit comprises: A first determination subunit, configured to determine that the patient's bone is a small bone when the bone width is smaller than the minimum standard bone width; A second determination subunit is used to determine that the patient's bone is a standard bone when the bone width is neither less than the minimum standard bone width nor greater than the maximum standard bone width; The first determination subunit is used to determine that the patient's bone is a large bone when the bone width is greater than the maximum standard bone width.

6. The artificial intelligence analysis auxiliary system based on orthopedic images according to claim 1 is characterized in that: The fracture detection module comprises: A setting unit, used to set a standard bone fracture ratio range consisting of a minimum standard bone fracture ratio and a maximum standard bone fracture ratio; a comparison unit connected to the setting unit, and used to compare the first bone fracture ratio with the standard bone fracture ratio range to obtain a comparison result; The grade determination unit is connected to the comparison unit and is used to determine the fracture grade based on the comparison result.

7. The artificial intelligence analysis auxiliary system based on orthopedic images according to claim 6 is characterized in that: The comparison unit comprises: The acquisition subunit is used to analyze the initial bone image to obtain the first bone fracture width: a calculation subunit, connected to the acquisition subunit, for calculating the first bone fracture ratio according to the bone width and the first bone fracture width; The comparison subunit is used to compare the first bone fracture ratio with the standard bone fracture ratio range to obtain a comparison result.

8. The artificial intelligence analysis auxiliary system based on orthopedic images according to claim 6 is characterized in that: The level determination unit comprises: A first determination subunit, configured to determine a slight fracture when the first bone fracture ratio is less than a minimum standard bone fracture ratio; A second determination subunit is used to determine that the fracture is a moderate fracture when the first bone fracture ratio is neither less than the minimum standard bone fracture ratio nor greater than the maximum standard bone fracture ratio; The third determination subunit is used to determine that the fracture is severe when the first bone fracture ratio is greater than the maximum standard bone fracture ratio.

9. The artificial intelligence analysis auxiliary system based on orthopedic images according to claim 1 is characterized in that: The treatment detection module comprises: An analysis unit, used for analyzing the treated bone image to obtain a second bone width and a bone width; A calculation unit is connected to the analysis unit and is used to take the quotient of the second bone width divided by the bone width as the second bone fracture ratio.

10. The artificial intelligence analysis auxiliary system based on orthopedic images according to claim 1 is characterized in that: The image acquisition module comprises: A first acquisition unit is used to acquire the initial bone image obtained by photographing the fracture site of the patient when the fracture occurs; The second acquisition unit is used to acquire the treated bone image of the patient after a period of time after the operation.

Images