A proximal humerus fracture Neer classification teaching system and method

Abstract

The application relates to the technical field of medical teaching, in particular to a proximal humerus fracture Neer classification teaching system and method, wherein the system comprises: a data mapping module, which is used for automatically identifying a finger state and analyzing the finger state into a fracture displacement parameter of corresponding proximal humerus anatomical structure according to a mapping relationship; and a rendering simulation module, which is used for driving a three-dimensional proximal humerus fracture model according to the fracture displacement parameter, superimposing the three-dimensional proximal humerus fracture model on the fingers or presenting the three-dimensional proximal humerus fracture model in a virtual space for rendering simulation in a visual environment. The application simulates fracture displacement according to a mapping relationship and a gesture rule by establishing a mapping relationship between the fingers and the anatomical structure, converts abstract medical classification into physical actions that can be personally perceived and operated by learners, and thus greatly reduces learning difficulty and improves understanding depth and memory persistence.

Description

Technical Field

[0001] This application relates to the field of medical teaching technology, and in particular to a Neer classification teaching system and method for proximal humeral fractures. Background Technology

[0002] In related technologies, the Neer classification of proximal humeral fractures is a core component that must be mastered in undergraduate and residency training in orthopedics and trauma surgery. This classification system is highly abstract, involving three-dimensional spatial visualization, and the dynamic changes of multiple anatomical structures and their spatial relationships. Students generally report difficulty in understanding and memorizing it using traditional teaching methods. Accurate mastery of the Neer classification is crucial for interpreting imaging data (such as X-rays and CT scans) and developing treatment plans.

[0003] Currently, the mainstream methods for teaching Neer classification in medical education include: text and two-dimensional diagram method: explaining through text descriptions and two-dimensional diagrams in textbooks and PPTs; three-dimensional physical model demonstration method: using a detachable model of the proximal humerus made of plastic or resin, where teachers manually separate different parts to simulate various fractures; three-dimensional computer graphics and animation method: using computer software to generate a three-dimensional model of the humerus and showing the fracture separation process through animation; and mnemonic or story-based memorization method: teachers create mnemonics or associative stories to help students remember the names and general characteristics of the classification. However, the above mainstream methods usually do not involve a deep understanding of spatial relationships, resulting in unsatisfactory teaching effects. Summary of the Invention

[0004] The main objective of this application is to propose a Neer classification teaching system and method for proximal humeral fractures, aiming to solve the above-mentioned problems.

[0005] To achieve the above objectives, one aspect of this application proposes a Neer classification teaching system for proximal humeral fractures, the system comprising: The data mapping module is used to establish the mapping relationship between the fingers and the anatomical structure of the humerus; The data parsing module is used to automatically identify the finger state and, based on the mapping relationship, parse the finger state into fracture displacement parameters of the corresponding humeral anatomical structure; The rendering simulation module is used to drive a three-dimensional proximal humeral fracture model based on the fracture displacement parameters. In a visualization environment, the three-dimensional proximal humeral fracture model is superimposed on a finger or presented in virtual space for rendering simulation.

[0006] In some embodiments, the system further includes: The simulation evaluation module is used to quantitatively evaluate the results of the rendering simulation.

[0007] In some embodiments, the quantitative evaluation of the rendering simulation results specifically includes: Obtain the three-dimensional coordinates of the finger, and output the corresponding Neer fractal based on the three-dimensional coordinates for rendering simulation; Based on the three-dimensional coordinates, the spatial deviation of the finger during the rendering simulation process is calculated; The score for a single rendering simulation is calculated based on the spatial deviation and its corresponding Neer fraction weights.

[0008] In some embodiments, the quantitative evaluation of the results of the rendering simulation further includes: Based on the three-dimensional coordinates, the corresponding Neer fractal is rendered and simulated, and the dynamic mastery index corresponding to each Neer fractal is calculated.

[0009] In some embodiments, the spatial deviation includes a resolution deviation, the expression for which is: ; in Indicates the separation deviation. This represents the Euclidean distance between the tips of two fingers. This represents the predefined standard effective shift distance.

[0010] In some embodiments, the spatial deviation includes an angular deviation, the expression for which is: ; in To create angular deviation, The actual angle formed by the two fingertips. This is a predefined standard angle threshold.

[0011] In some embodiments, the expression for the single-rendered simulated answer score is: ; in To get points for answering the question, The key indicator that needs to be evaluated in the current exam question is the number of relationships. Let be the deviation value of the i-th evaluation item. is the weight coefficient of the i-th evaluation item.

[0012] In some embodiments, the expression for the dynamic mastery index is: ; in This is a dynamic control index. , These are the contribution weights for accuracy and reaction efficiency, respectively. This represents the historical average accuracy rate. This is the historical average reaction time. This is the maximum permissible reaction time.

[0013] In some embodiments, the humeral anatomy includes the humeral shaft, greater tubercle, humeral head, and lesser tubercle.

[0014] To achieve the above objectives, another aspect of this application proposes a teaching method for the Neer classification of proximal humeral fractures, the method comprising: Establish a mapping relationship between the anatomical structures of the fingers and the humerus; Automatically identify finger status and, based on the mapping relationship, parse the finger status into fracture displacement parameters corresponding to the anatomical structure of the humerus; The three-dimensional proximal humeral fracture model is driven by the fracture displacement parameters. In a visualization environment, the three-dimensional proximal humeral fracture model is superimposed on a finger or presented in virtual space for rendering simulation.

[0015] The embodiments of this application include at least the following beneficial effects: This application provides a Neer classification teaching system and method for proximal humeral fractures. By establishing a mapping relationship between fingers and anatomical structures and simulating fracture displacement based on the mapping relationship, this application transforms abstract medical classifications into physical movements that learners can personally perceive and operate, thereby greatly reducing the learning difficulty and improving the depth of understanding and the persistence of memory. Attached Figure Description

[0016] Figure 1 A block diagram of a Neer classification teaching system for proximal humeral fractures provided in this application embodiment; Figure 2 A mapping diagram of the relationship between fingers and anatomical structures provided in the embodiments of this application; Figure 3 A simulated finger diagram illustrating a partial fracture, provided as an embodiment of this application; Figure 4 A simulated finger diagram illustrating a two-part fracture, provided as an embodiment of this application; Figure 5 This is a simulation diagram of index finger separation in the case of a three-part fracture, provided as an embodiment of this application. Figure 6 This application provides a simulation diagram of ring finger separation in the case of a three-part fracture. Figure 7 A simulated finger diagram illustrating a four-part fracture, provided as an embodiment of this application; Figure 8 A teaching flowchart for the Neer classification of proximal humeral fractures provided in this application embodiment. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0018] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0019] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0021] Before providing a detailed description of the embodiments of this application, some of the nouns and terms involved in the embodiments of this application will be explained first. The nouns and terms involved in the embodiments of this application are subject to the following interpretations.

[0022] Proximal humerus: refers to the end of the humerus closest to the shoulder joint, which is a common site of shoulder fractures.

[0023] Neer classification: Proposed by Charles S. Neer, this system classifies fractures based on the number and displacement of fracture fragments in the major anatomical structures of the proximal humerus (greater tuberosity, lesser tuberosity, humeral head, and humeral shaft). It is an important standard for guiding clinical treatment (such as surgical decisions).

[0024] Displacement / Angulation Criteria: Core quantitative indicators in the Neer classification that distinguish between "partial" and "multi-part" fractures. Displacement refers to a separation distance between fracture fragments greater than 1 cm; angulation refers to an angle between fracture fragments greater than 45 degrees.

[0025] The simulation mapping consists of four parts: the thumb represents the humeral shaft, the index finger represents the greater tubercle, the middle finger represents the humeral head, the ring finger represents the lesser tubercle, and the little finger does not directly participate in the core structure simulation in this scheme, but can be used to assist in representing other structures or to maintain a natural posture.

[0026] The related technologies have the following drawbacks: Abstraction is not intuitive: text and two-dimensional diagrams cannot effectively convey structural relationships and displacements in three-dimensional space, making it difficult for students to construct three-dimensional models in their minds.

[0027] High cost and inconvenient to use: High-quality 3D physical models are expensive, easily damaged, and inconvenient for students to review anytime and anywhere. 3D animation software usually requires specific equipment support.

[0028] Poor interactivity: The above methods mostly involve passively receiving information, and students lack active participation and physical awareness, which is not conducive to the formation of deep learning and long-term memory.

[0029] Disconnected from one's own cognition: Traditional methods do not make full use of the learner's own body (such as hands) as the most readily available and familiar "cognitive tool" to internalize complex spatial anatomical relationships.

[0030] In view of this, one aspect of the embodiments of this application proposes a Neer classification teaching system for proximal humeral fractures.

[0031] refer to Figure 1 As shown, the system of this application includes: The data mapping module is used to establish the mapping relationship between the fingers and the anatomical structure of the humerus; refer to Figure 2 As shown, specifically in this application, the thumb represents the humeral shaft, the index finger represents the greater tubercle, the middle finger represents the humeral head, and the ring finger represents the lesser tubercle. The little finger is naturally curved and does not participate in the core mapping.

[0032] The data parsing module is used to automatically identify the finger state and, based on the mapping relationship, parse the finger state into fracture displacement parameters of the corresponding humeral anatomical structure; Specifically, before identifying the finger status, preset gesture rules are also included. The gesture rules are defined as starting with a "closed and straight" gesture (representing no displacement or slight displacement, i.e., "partial fracture"). The criteria for determining "displacement" or "angulation" are converted into finger movements: two fingers that are clearly separated (more than one finger width apart) are considered "displacement > 1 cm"; two fingers that form a clear angle (e.g., > 45°) are considered "angulation > 45°".

[0033] The rendering simulation module is used to drive a three-dimensional proximal humeral fracture model based on the fracture displacement parameters. In a visualization environment, the three-dimensional proximal humeral fracture model is superimposed on a finger or presented in virtual space for rendering simulation.

[0034] Specifically, the rendering simulation includes: refer to Figure 3 As shown, this is a partial fracture. At this point, the thumb, index finger, middle finger, and ring finger are basically together and pointing in roughly the same direction. This indicates that none of the fracture fragments have undergone significant displacement or angulation. refer to Figure 4 As shown, this is a two-part fracture. Of the four fingers, only one finger is clearly separated from the other three fingers or forms an angle. For example: Two-part fracture of the greater tubercle: the index finger is significantly separated / elevated on the dorsal (lateral) side, while the thumb, middle finger, and ring finger remain together.

[0035] Two-part fracture of the humeral shaft: The thumb (representing the humeral shaft) separates distally (downward) and may internally rotate, while the other three fingers (representing the humeral head and tubercle) remain as one.

[0036] refer to Figure 5-6 The image shows a three-part fracture. Two of the four fingers are considered as one unit and have separated from the other two. This is a common type of fracture simulation. Greater tubercle-humeral head three-part fracture: When the index and middle fingers are brought together, forming the "greater tubercle-humeral head complex," they separate from either the thumb (humeral shaft) or the ring finger (lesser tubercle).

[0037] In fact, classic three-part fractures are often "greater tubercle type" or "smaller tubercle type", that is, the index finger or ring finger is separated, while the remaining three fingers (including the thumb, middle finger and another tubercle) remain as one. In terms of hand gestures, it can be simulated as follows: the index finger is separated, while the thumb, middle finger and ring finger are together (but the small tubercle represented by the ring finger is already integrated with the humeral head and has not been separated separately); or the ring finger is separated, while the thumb, index finger and middle finger are together.

[0038] refer to Figure 7 As shown, this is a four-part fracture, where the thumb, index finger, middle finger, and ring finger are clearly separated from each other, presenting four independent directions or positions. The fourth type represents complete separation of the four main parts, the most severe type of displacement.

[0039] Furthermore, the system of this application also includes a simulation evaluation module for quantitatively evaluating the results of the rendering simulation.

[0040] Specifically, the quantitative assessment is as follows: Obtain the three-dimensional coordinates of the finger, and output the corresponding Neer fractal for rendering simulation based on the three-dimensional coordinates. Based on the three-dimensional coordinates, the spatial deviation of the finger during the rendering simulation operation is calculated; The score for a single rendering simulation is calculated based on the spatial deviation and its corresponding Neer fraction weights.

[0041] Furthermore, based on the three-dimensional coordinates, the corresponding Neer fractal is rendered and simulated, and the dynamic mastery index corresponding to each Neer fractal is calculated.

[0042] Users can use cameras / sensors to perform hand calibration and confirm the fixed mapping relationship between the four fingers and the four major anatomical structures.

[0043] The system tracks key points of the fingers in real time and automatically identifies the "closed", "separated" or "angled" state of the fingers according to predefined gesture rules, and parses them into fracture displacement parameters of the corresponding anatomical structures.

[0044] Fracture displacement parameters drive a 3D proximal humeral fracture model in real time. In the AR view, the model is precisely superimposed on the user's hand; in the VR teaching unit, the model is directly presented in the virtual space. Changes in hand gestures instantly cause displacement or rotation of the corresponding parts of the model, synchronously displaying the corresponding Neer classification name and key points.

[0045] The simulation assessment module offers three core modes: ① Guided instruction: Follow the prompts to make gestures and observe the model and classification results. ② Free exploration: Freely change gestures and observe different fracture morphologies in real time.

[0046] Specifically, the simulation assessment module includes an online teaching platform or application. This module achieves an objective assessment of the level of mastery of the Neer classification through quantitative analysis of the gesture operation process.

[0047] Furthermore, online teaching platforms or applications integrate gesture interaction testing engines, which are built based on a case image database. During the quantitative analysis process, the engine captures and digitizes the user's response gestures through a camera, scores and tracks the accuracy of operations, reaction time, and error patterns, and generates a radar chart of ability dimensions.

[0048] Furthermore, online teaching platforms or applications also provide targeted gesture correction guidance and predictive reinforcement exercises based on weaknesses.

[0049] Specifically, spatial bias includes separation bias, the expression for which is: ; in Indicates the separation deviation. This represents the Euclidean distance between the tips of two fingers. This represents the predefined standard effective shift distance.

[0050] Specifically, spatial deviation includes angular deviation, the expression for which is: ; in To create angular deviation, The actual angle formed by the two fingertips. This is a predefined standard angle threshold.

[0051] Specifically, the expression for the score of a single-render simulation answer is: ; in To get points for answering the question, The key indicator that needs to be evaluated in the current exam question is the number of relationships. Let be the deviation value of the i-th evaluation item. is the weight coefficient of the i-th evaluation item.

[0052] The expression for the dynamic mastery index is: ; in This is a dynamic control index. , These are the contribution weights for accuracy and reaction efficiency, respectively. This represents the historical average accuracy rate. This is the historical average reaction time. This is the maximum permissible reaction time.

[0053] refer to Figure 8 As shown, further, in another aspect of the embodiments of this application, a teaching method for the Neer classification of proximal humeral fractures is also proposed, the method comprising: S101: Establish the mapping relationship between the fingers and the anatomical structure of the humerus; S102: Automatically identify the finger state and, based on the mapping relationship, parse the finger state into fracture displacement parameters corresponding to the anatomical structure of the humerus; S103: Drive the three-dimensional proximal humeral fracture model according to the fracture displacement parameters, and render and simulate the three-dimensional proximal humeral fracture model by superimposing it on a finger or presenting it in virtual space in a visualization environment.

[0054] Through steps S101-S103, an objective and real-time evaluation mechanism is introduced while retaining the core advantages of "embodied" learning. This not only solves the ambiguity problem of purely subjective comparison, but also upgrades the learning method into a complete training cycle. The method of this application retains the advantages of low cost and high portability, and significantly improves the accuracy, interest and evaluation efficiency of training through intelligent modules, making it applicable to various scenarios from individual self-study to classroom group teaching and standardized assessment.

[0055] The following specific examples illustrate this application: 1. Mapping learning stage: Show learners the mapping relationship between fingers and anatomical structures, and through repeated practice, firmly establish the conditioned reflex correspondence of "thumb-humeral shaft, index finger-great tubercle, middle finger-humeral head, ring finger-less tubercle".

[0056] 2. Rule explanation stage: Clearly define the gesture rules and define the visual and spatial standards for "joining" (representing the unity of anatomical structures) and "separation / angle" (representing fracture displacement).

[0057] 3. Rendering simulation teaching stage: a. Basic exercise: Learners keep their four fingers together and simulate "partial fracture".

[0058] b. Instruction response: The teacher or system issues an instruction (such as "simulate a two-part fracture of the greater tuberosity"), and the learner independently operates the corresponding finger (index finger) to separate.

[0059] c. Dual-track verification and calibration: Subjective image comparison: Learners actively compare standard X-ray or CT images using AR or VR, observe and adjust their finger postures, and strive to match the spatial relationship of the hand gestures with the image representation.

[0060] Objective judgment: At the same time, through an application integrated into a mobile terminal (such as a mobile phone or tablet), the learner's real-time gestures are automatically captured and analyzed using the device's camera.

[0061] 4. Rendering evaluation stage: The built-in gesture recognition and classification algorithm (such as a computer vision-based joint detection and spatial relationship analysis model) will calculate gesture parameters in real time and automatically output the Neer classification results, confidence level and visual feedback, such as highlighting fingers that are not correctly separated.

[0062] Feedback loop formation: Learners compare the results of subjective adjustments with objective judgments. If they are inconsistent, they need to re-examine the images and gestures based on the specific feedback from the system (such as "unexpected angulation of the middle finger (humeral head) was detected") and make fine-tuning. Teachers can also understand common difficulties based on the collective data dashboard provided by the system.

[0063] Reverse reinforcement exercises: The teacher or system makes a gesture, and the learner must quickly say the corresponding classification; or the system randomly displays a fracture image, and the learner must make the correct gesture and the system will automatically score the result.

[0064] This application provides a teaching method for Neer classification of proximal humeral fractures, relating to the field of information technology. The method provided in this application can be applied to a terminal, a server, or software running on a terminal or server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or in-vehicle terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing the method, but is not limited to the above forms.

[0065] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0066] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0067] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0068] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0069] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0070] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0071] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0072] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0073] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0074] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0075] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A proximal humerus fracture Neer classification teaching system, characterized in that, The system includes: The data mapping module is used to establish the mapping relationship between the fingers and the anatomical structure of the humerus; The data parsing module is used to automatically identify the finger state and, based on the mapping relationship, parse the finger state into fracture displacement parameters of the corresponding humeral anatomical structure; The rendering simulation module is used to drive a three-dimensional proximal humeral fracture model based on the fracture displacement parameters. In a visualization environment, the three-dimensional proximal humeral fracture model is superimposed on a finger or presented in virtual space for rendering simulation.

2. The proximal humerus fracture Neer classification teaching system of claim 1, wherein, The system also includes: The simulation evaluation module is used to quantitatively evaluate the results of the rendering simulation.

3. The proximal humerus fracture Neer classification teaching system of claim 1, wherein, The quantitative evaluation of the rendering simulation results specifically includes: Obtain the three-dimensional coordinates of the finger, and output the corresponding Neer fractal based on the three-dimensional coordinates for rendering simulation; Based on the three-dimensional coordinates, the spatial deviation of the finger during the rendering simulation process is calculated; The score for a single rendering simulation is calculated based on the spatial deviation and its corresponding Neer fraction weights.

4. The proximal humerus fracture Neer teaching system according to claim 3, wherein, The quantitative evaluation of the rendering simulation results also includes: Based on the three-dimensional coordinates, the corresponding Neer fractal is rendered and simulated, and the dynamic mastery index corresponding to each Neer fractal is calculated.

5. The proximal humerus fracture Neer teaching system according to claim 3, wherein, The spatial deviation includes the resolution deviation, which is expressed as follows: ； wherein represents a separation deviation, represents the Euclidean distance of the two fingertips, represents a predefined standard effective shift distance.

6. The proximal humerus fracture Neer teaching system according to claim 3, wherein, The spatial deviation includes angular deviation, and the expression for the angular deviation is: ； wherein is an angular deviation, is an actual angular angle of the two fingertips, is a predefined standard angular threshold.

7. The proximal humerus fracture Neer teaching system according to claim 3, wherein, The expression for the score of a single-rendered simulated response is: ； wherein is the single rendering simulation answer score, is the number of key-to-key relationships that need to be evaluated for the current question, is the bias value for the i-th evaluation item, is the weight coefficient for the i-th evaluation item.

8. The proximal humerus fracture Neer teaching system according to claim 4, wherein, The expression for the dynamic mastery index is: ； in This is a dynamic control index. , These are the contribution weights for accuracy and reaction efficiency, respectively. This represents the historical average accuracy rate. This is the historical average reaction time. This is the maximum permissible reaction time.

9. The Neer classification teaching system for proximal humeral fractures according to claim 1, characterized in that, The anatomical structures of the humerus include the humeral shaft, greater tubercle, humeral head, and lesser tubercle.

10. A teaching method for Neer classification of proximal humeral fractures, characterized in that, The method includes: Establish a mapping relationship between the anatomical structures of the fingers and the humerus; Automatically identify finger status and, based on the mapping relationship, parse the finger status into fracture displacement parameters corresponding to the anatomical structure of the humerus; The three-dimensional proximal humeral fracture model is driven by the fracture displacement parameters. In a visualization environment, the three-dimensional proximal humeral fracture model is superimposed on a finger or presented in virtual space for rendering simulation.

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