WO2022100520A1 - 一种预测困难气道的计算机应用软件及气道管理数据系统 - Google Patents

一种预测困难气道的计算机应用软件及气道管理数据系统 Download PDF

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WO2022100520A1
WO2022100520A1 PCT/CN2021/128907 CN2021128907W WO2022100520A1 WO 2022100520 A1 WO2022100520 A1 WO 2022100520A1 CN 2021128907 W CN2021128907 W CN 2021128907W WO 2022100520 A1 WO2022100520 A1 WO 2022100520A1
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airway
difficult
patient
unit
prediction
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French (fr)
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姚卫东
王斌
吴玥
魏铁钢
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安徽玥璞医疗科技有限公司
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders

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  • the invention relates to the technical field of clinical medicine, in particular to a computer application software and an airway management data system for predicting difficult airways.
  • Difficult airway conditions refer to situations in which artificial ventilation is required due to illness or surgery during general anesthesia, emergency treatment, and diagnosis and treatment of critically ill patients, but difficulties are encountered in establishing an artificial ventilation channel.
  • Clinically difficult airway conditions include difficulty in mask ventilation, difficulty in revealing the glottis by laryngoscopy, difficulty in intubation, intubation failure, and difficulty in supraglottic airway ventilation, whether rescue intubation in critically ill patients or tracheal intubation after induction of general anesthesia All patients have lost their normal respiratory function. Once a patient cannot be successfully intubated in time and cannot be effectively ventilated, hypoxia will occur quickly. Just a few minutes of hypoxia may cause irreversible brain damage, directly endangering If a surgical airway cannot be established urgently, cricothyroidotomy or tracheotomy is performed, the patient will suffer from cardiac arrest, brain injury, and even death due to asphyxia. Therefore, it is particularly important to predict the difficult airway. It is necessary to check the patient before to identify whether there is a danger, and the process of predicting the difficult airway should not be cumbersome, and it is necessary to save time and deal with it in advance.
  • the difficult airway is formed by many factors, but the anatomical composition principle of the difficult airway has not been proved;
  • the purpose of the present invention is to provide a computer application software and an airway management data system for predicting difficult airways, so as to solve the problems raised in the above background art.
  • the present invention provides the following technical solutions:
  • a computer application software and an airway management data system for predicting difficult airways including a medical staff application module and a central control module, wherein the medical staff application module includes a difficult airway geometry computer simulation program unit, a difficult airway multi-data machine Learning prediction model program unit, difficult airway face recognition machine learning prediction model program unit, image information input and processing unit, prediction result output unit,
  • the difficult airway geometry computer simulation program unit is responsible for the extraction of upper airway anatomical feature points, the coordinate positioning simulation of the feature points, the movement law and trajectory of the anatomical feature points when the glottis is exposed by the laryngoscope, the parameter changes of the difficult airway patients, the parameters
  • the geometric interaction relationship and the mechanical interaction relationship between the two parameters are the rotation and displacement of each anatomical feature point when the laryngoscope exposes the glottis, so as to establish a geometric anatomical model of the upper airway.
  • the data machine learning prediction model program unit is responsible for using machine learning technology to establish an artificial intelligence prediction model program
  • the difficult airway face recognition machine learning prediction model program unit is responsible for using machine learning to establish a difficult airway face recognition artificial intelligence prediction model program
  • the The image information input and processing unit is responsible for calculating and processing the necessary patient information uploaded to the central server
  • the prediction result output unit is responsible for outputting the calculation results to the central database and the display and memory of the user terminal
  • the central control module includes a difficult airway prediction procedure performance optimization unit, and the difficult airway prediction procedure performance optimization unit is responsible for selecting the difficult airway procedure.
  • the difficult airway prediction includes the following steps:
  • S1 Construct the geometric analysis theory of the upper airway in the difficult airway. Through the geometric model, analyze the geometric principle of the anatomical features of the upper airway to form the difficult airway, and reconstruct the upper airway according to the anatomical characteristics of the patient's upper airway Two-dimensional schematic diagram of airway anatomy;
  • anatomical feature points include head, neck, The body of the tongue, mandible, pharynx, larynx, and their accessory tissues and boundary points;
  • S3 According to a certain amount of sample measurement data, determine the coordinates and dimensions of each anatomical feature point of the upper airway of the patient and the determinants of these dimensions, establish an anatomical simulation coordinate system of the upper airway, and then analyze the anatomy related to the formation of the difficult airway.
  • the feature points are located in the coordinate system;
  • the rotation amount and displacement amount of each anatomical feature point include: head rotation amount, direction angle and distance of mandibular advancement, glottis displacement angle and distance, and tongue compression direction angle and distance.
  • the image information input and processing unit calculates and analyzes the data and images uploaded to the central server, and for the patient's facial image information, the facial recognition program is preferentially called to identify the patient's eyes, and the code mosaic is used to hide the identifiable patient. to store, calculate, and analyze private information.
  • the output result of the prediction result output unit includes the simulation output of the image, the output of the calculation result of the glottal field of view, and the credible range of the value based on big data statistics.
  • the medical staff module also includes a software startup unit, a registration login unit, a home page interface design unit, a patient information input unit, and a first information management and retrieval unit,
  • the patient information input unit is logged in by the user through the real-name authentication, and the necessary information of the patient is input, and the necessary information includes the patient's age, gender, height, weight, mouth opening, nail-mental distance, tongue-chin distance, tongue.
  • Users can also input the patient’s actual surgical process data, such as whether there is a difficult airway,
  • the type, degree, and processing results of difficult airways are stored in the central server database, and each input box is followed by helpful information to guide users to standard data collection methods.
  • the central control module further includes a user registration authentication unit, a user management authorization unit, and a second information management and retrieval unit.
  • the user can modify and delete the patient data managed by himself, and can further retrieve and classify the patient data, and can also generate the patient's case report form.
  • Authorization to customize their own data category catalogue The system administrator can set various parameters of the system through the central control module, including user personnel management, authorized project content, calling program unit selection and mode selection, and the mode includes clinical application. Mode and scientific research mode, the system administrator can select, replace, update and optimize the difficult airway program through the central control module, and the system administrator can retrieve, classify, delete, backup, edit and analyze the data stored in the server.
  • the present invention has the following beneficial effects: the present invention is based on the inherent mechanism of difficult airway formation, thereby improving the prediction accuracy; Factor judgment process; through computer graphics computing technology, modeling and simulation, high detection authenticity; through artificial intelligence machine learning of large sample difficult airway clinical data and difficult airway facial feature data, respectively establish difficult airway artificial intelligence prediction model program And difficult airway facial recognition model program, which further improves the prediction performance; based on the core function model programming, it is convenient for clinical application; the big data management function is convenient for users to carry out corresponding scientific research work.
  • FIG. 1 is a block diagram of a computer application software and an airway management data system for predicting difficult airways of the present invention
  • FIG. 2 is a flowchart of a computer application software and an airway management data system for predicting a difficult airway according to the present invention
  • FIG. 3 is a coordinate diagram of anatomical feature points of a computer application software for predicting difficult airways and an airway management data system of the present invention
  • FIG. 4 is a schematic diagram of a home page interface of a computer application software for predicting difficult airways and an airway management data system according to the present invention
  • FIG. 5 is a schematic diagram of an information input interface of a computer application software for predicting difficult airways and an airway management data system according to the present invention
  • Figure 6 is a schematic diagram of the result output interface of a computer application software for predicting difficult airways and an airway management data system of the present invention.
  • a computer application software and an airway management data system for predicting a difficult airway including a medical staff application module and a central control module, the medical staff application module including a difficult airway geometry computer simulation program unit, a difficult airway multi-data machine learning prediction Model program unit, difficult airway face recognition machine learning prediction model program unit, image information input and processing unit, prediction result output unit, software startup unit, registration login unit, home page interface design unit, patient information input unit and first information management and retrieval unit.
  • the computer simulation program unit of difficult airway geometry is responsible for the extraction of upper airway anatomical feature points, the coordinate positioning simulation of feature points, the movement law and trajectory of anatomical feature points when laryngoscope exposes the glottis, the parameter changes of patients with difficult airway, and the relationship between parameters.
  • Geometric interaction relationship and mechanical interaction relationship the parameters are the rotation and displacement of each anatomical feature point when the laryngoscope exposes the glottis, so as to establish the upper airway geometric anatomical model, and multi-data machine learning prediction of difficult airways
  • the model program unit is responsible for using machine learning technology to establish an artificial intelligence prediction model program
  • the difficult airway face recognition machine learning prediction model program unit is responsible for using machine learning to establish an artificial intelligence prediction model program for difficult airway face recognition
  • the image information input and processing unit is responsible for The necessary patient information uploaded to the central server is processed for calculation
  • the prediction result output unit is responsible for outputting the calculation results to the central database and the display and memory of the user terminal.
  • the central control module includes a difficult airway prediction program performance optimization unit, a user registration authentication unit, a user management authorization unit, and a second information management and retrieval unit.
  • the difficult airway prediction program performance optimization unit is responsible for selecting difficult airway programs.
  • the difficult airway prediction includes the following steps:
  • S1 Construct the geometric analysis theory of the upper airway in the difficult airway. Through the geometric model, analyze the geometric principle of the anatomical features of the upper airway to form the difficult airway, and reconstruct the upper airway according to the anatomical characteristics of the patient's upper airway Two-dimensional schematic diagram of airway anatomy;
  • anatomical feature points include head, neck, tongue, jaw, pharynx, larynx;
  • S3 According to a certain amount of sample measurement data, determine the coordinates and dimensions of each anatomical feature point of the upper airway of the patient and the determinants of these dimensions, establish an anatomical simulation coordinate system of the upper airway, and then analyze the anatomy related to the formation of the difficult airway.
  • the feature points are located in the coordinate system;
  • the rotation amount and displacement amount of each anatomical feature point in step S5 include: the rotation amount of the head, the direction angle and distance of mandibular advancement, the glottis displacement angle and distance, the tongue compression direction angle and distance, the parameter regression equation
  • the example is as follows:
  • y is the calculated parameter value, that is, the direction or amount of the final displacement of the corresponding anatomical feature point
  • x is the input variable, that is, the clinical detection value
  • a and b are the adjustment coefficients and constant terms of the equation, and their values depend on the clinical data. The statistical results of this equation will determine the displacement trajectory of each anatomical feature point, and each parameter in it has the function of iterative modification and optimization.
  • the patient information input unit allows the user to enter the necessary information of the patient after logging in through real-name authentication.
  • the necessary information includes the patient's age, gender, height, weight, mouth opening, nail-chin distance, tongue-chin distance, tongue thickness, and temporomandibular joint activity. degree, the patient's face frontal photo, and the lateral head-up sniffing position photo, and upload them to the central server.
  • the user can also input the patient's real surgical process data, such as whether there is a difficult airway, and the type and degree of the difficult airway. As well as process the results and store them in a central server database.
  • the image information input and processing unit calculates and analyzes the data and images uploaded to the central server.
  • the facial recognition program is preferentially called to identify the patient's eyes, and the code mosaic is used to hide the private information that can identify the patient, thereby Store, compute, and analyze it.
  • the central server receives the data and images uploaded by the terminal, and invokes the corresponding program unit for calculation and analysis. After the calculation is completed, it outputs the calculation results to the central database and the display and memory of the user terminal.
  • the output results of the prediction result output unit include the analog output of the image, the The output of the calculation result of the door field of view and the credible range of this value based on big data statistics.
  • the system administrator can set various parameters of the system through the central control module, including user personnel management, authorized project content, calling program unit selection and mode selection.
  • the modes include clinical application mode and scientific research mode;
  • the system administrator can retrieve, classify, delete, backup, edit and analyze the data stored on the server.
  • the user can select items after logging in. If "New Patient” is selected, the information input interface shown in Figure 5 will be entered, and the user will input the patient's information. After the series of analysis, the analysis results are shown in the result output interface as shown in Figure 6, and the glottis field of view is calculated to predict whether the patient is a difficult airway patient; if you select "My Patient", you will enter the patient data interface. Users can modify and delete patient data managed by themselves, and can further retrieve and classify patient data, as well as generate patient case report forms.

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Abstract

本发明公开了一种预测困难气道的计算机应用软件及气道管理数据系统，包括医务人员应用模块和中枢控制模块，所述医务人员应用模块和中枢控制模块各自包含多个单元，本发明的有益效果在于：该软件基于困难气道形成的内在机制，提高预测精确性；只保留少数困难气道形成的直接因素，省去繁琐的多因素判断过程；通过计算机图形计算技术，建模模拟，检测真实性高；通过大样本困难气道临床数据和困难气道面部特征数据的人工智能机器学习，分别建立困难气道人工智能预测模型程序和困难气道面部识别模型程序，进一步提高预测性能；基于核心功能模型程序编制以及大数据管理功能，方便使用者进行相应的临床应用和科学研究工作。

Description

一种预测困难气道的计算机应用软件及气道管理数据系统 技术领域
本发明涉及临床医学技术领域，具体为一种预测困难气道的计算机应用软件及气道管理数据系统。
背景技术
困难气道情形是指在全麻、急救、危重患者诊疗中，因病情或手术需要进行人工通气，但在建立人工通气管道时遇到困难的情况。
临床困难气道情形包括面罩通气困难、喉镜显露声门困难、插管困难、插管失败及声门上气道通气困难，无论是危急患者抢救性插管，还是全麻诱导后的气管插管，患者都已失去正常的呼吸功能，患者一旦遇到不能及时成功插管又不能有效通气的时候，缺氧将很快发生，仅仅几分钟的缺氧就可能造成不可逆的脑损伤，直接危及患者性命，如不能紧急建立外科气道，行环甲膜穿刺或气管切开，患者将因窒息继发生心脏骤停、脑损伤，甚至死亡，因此预测困难气道就显得尤为重要，需要在术前通过对患者进行检查，鉴别是否存在危险，而且预测困难气道过程不宜繁琐，需节省时间，好提前应对。
然而现有技术仍存在以下诸多不足：
1、困难气道是由多方因素形成，但困难气道的解剖构成原理未能探明；
2、目前已有超过20个预测因素，然而大部分因素与预测困难气道相关性差，在临床工作中难以得到有效实施；
3、缺少大样本系统性的数据支持；
4、大多基于体表解剖标志，与真正气道解剖表现相关性差；
5、参数与因素间的相互作用及互相影响形成困难气道的机制没能揭示。
基于上述问题，亟待提出一种预测困难气道的计算机应用软件及气道管理数据系统，以提高困难气道预测的精确性，只保留少数困难气道形成的直接因素，省去繁琐的多因素判断过程，提高检测真实性以及预测性能，方便临床应用和科学研究。
发明内容
本发明的目的在于提供一种预测困难气道的计算机应用软件及气道管理数据系统，以解决上述背景技术中提出的问题。
为了解决上述技术问题，本发明提供如下技术方案：
一种预测困难气道的计算机应用软件及气道管理数据系统，包括医务人员应用模块和中枢控制模块，所述医务人员应用模块包括困难气道几何学计算机模拟程序单元、困难气道多数据机器学习预测模型程序单元、困难气道人脸识别机器学习预测模型程序单元、图像信息输入及处理单元、预测结果输出单元，
所述困难气道几何学计算机模拟程序单元负责上气道解剖特征点提取、特征点坐标定位模拟、喉镜显露声门时的解剖特征点运动规律和轨迹、困难气道患者的参数变化、参数间的几何学相互作用关系以及力学相互作用关系，所述参数为喉镜显露声门时各解剖特征点的旋转量和位移量，从而建立上气道几何学解剖模型，所述困难气道多数据机器学习预测模型程序单元负责利用机器学习技术建立人工智能预测模型程序，所述困难气道人脸识别机器学习预测模型程序单元负责利用机器学习建立困难气道面部识别人工智能预测模型程序，所述图像信息输入及处理单元负责对上传至中央服务器的患者必要信息进行计算处理，所述预测结果输出单元负责输出计算结果至中央数据库和使用者终端显示器及存储器，
所述中枢控制模块包括困难气道预测程序性能优化单元，所述困难气道预测程序性能优化单元负责对困难气道程序进行选择。
进一步的，该困难气道预测包括以下步骤：
S1：构建困难气道上气道解剖几何学分析理论，通过几何学模型，分析上气道解剖特征构成困难气道的几何学原理，根据患者上气道解剖特征形成困难气道的原理，重建上气道解剖二维示意图形；
S2：通过一定样本量的临床观察，根据统计学方法分析，确定数种形成困难气道的最关键最直接的解剖特征因子，从而进行解剖特征点提取，所述解剖特征点包括头、颈、舌体、下颌、咽腔、喉，以及它们的附属组织和边界点；
S3：依据一定量的样本测量数据，确定患者上气道各个解剖特征点的坐标及尺寸以及这些尺寸大小的决定因素，建立上气道解剖模拟坐标系，接着把与困难气道形成相关的解剖特征点定位到坐标系中；
S4：接着通过一定样本量临床实际患者，包括困难气道患者，观察各解剖 特征点在喉镜显露声门操作时的位移、变形、旋转的运动轨迹，通过观察一定样本量的困难气道患者，明确上述解剖特征点在困难气道患者中的变化特点及互相影响；
S5：通过解剖特征点之间的几何学相互作用关系以及力学相互作用关系，计算求出各解剖特征点在坐标系里的旋转量和位移量的参数回归方程；
S6：通过计算机图形编辑控制技术，建立可交互操作的上气道模拟图形，实现对这些解剖特征点参数的交互操作，通过图形控制的方法复现上述变化，最后根据前述的上气道解剖在喉镜检查时各个参数的回归方程，制定相应计算机图形控制规则，并根据患者术前评估的解剖特征形成最终图形计算患者在喉镜检查时的声门视野，从而准确预测患者是否是困难气道。
进一步的，所述各解剖特征点的旋转量和位移量包括：头的旋转量、下颌前移的方向角度和距离、声门位移角度和距离、舌体压缩方向角度和距离。
进一步的，所述图像信息输入及处理单元对上传至中央服务器的数据与图像进行计算分析，对患者的面部图像信息，优先调用面部识别程序识别患者的眼部，打码马赛克以隐藏可识别患者的隐私信息，从而对其进行存储、计算和分析。
进一步的，所述预测结果输出单元的输出结果包括图像的模拟输出、声门视野的计算结果输出以及依据大数据统计的该值的可信范围。
进一步的，所述医务人员模块还包括软件启动单元、注册登录单元、首页界面设计单元、患者信息输入单元以及第一信息管理与检索单元，
进一步的，所述患者信息输入单元由使用者经实名认证登录后，输入患者的必要信息，所述必要信息包括患者年龄、性别、身高、体重、张口度、甲颏距离、舌颏距离、舌体厚度、颞颌关节活动度、患者的面部正面照、侧面仰头嗅物位照，并将其上传至中央服务器，使用者还可输入患者的真实手术过程数据，如是否发生困难气道，困难气道的类型、程度以及处理结果，并将其存储在中央服务器数据库，且每个输入框后面，都附有帮助信息，以指导使用者知悉标准的数据采集方法。
进一步的，所述中枢控制模块还包括使用者注册认证单元、使用人员管理授权单元以及第二信息管理与检索单元。
进一步的，使用者可修改和删除自己管理的患者数据，且可进一步对患者数据进行检索以及分类，还可生成患者的病例报告表，使用者可反馈使用经验、体验和建议，通过系统管理者授权从而自定义自己的数据种类目录，系统管理者可通过中枢控制模块对系统各项参数进行设置，包括使用者人员管理、授权项目内容、调用程序单元选择和模式选择，所述模式包括临床应用模式和科学研究模式，系统管理者可通过中枢控制模块对困难气道程序进行选择、置换、更新以及优化，系统管理者可对服务器存储的数据进行检索、分类、删除、备份、编辑和分析。
与现有技术相比，本发明所达到的有益效果是：本发明基于困难气道形成的内在机制，提高预测精确性；只保留了少数困难气道形成的直接因素，省去了繁琐的多因素判断过程；通过计算机图形计算技术，建模模拟，检测真实性高；通过大样本困难气道临床数据和困难气道面部特征数据的人工智能机器学习，分别建立困难气道人工智能预测模型程序和困难气道面部识别模型程序，进一步提高了预测性能；基于核心功能模型程序编制，方便临床应用；大数据管理功能，方便使用者进行相应的科学研究工作。
附图说明
附图用来提供对本发明的进一步理解，并且构成说明书的一部分，与本发明的实施例一起用于解释本发明，并不构成对本发明的限制。在附图中：
图1是本发明一种预测困难气道的计算机应用软件及气道管理数据系统的模块框图；
图2是本发明一种预测困难气道的计算机应用软件及气道管理数据系统的流程框图；
图3是本发明一种预测困难气道的计算机应用软件及气道管理数据系统的解剖特征点坐标图；
图4是本发明一种预测困难气道的计算机应用软件及气道管理数据系统的首页界面示意图；
图5是本发明一种预测困难气道的计算机应用软件及气道管理数据系统的信息输入界面示意图；
图6是本发明一种预测困难气道的计算机应用软件及气道管理数据系统 的结果输出界面示意图。
具体实施方式
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
请参阅图1-6，本发明提供技术方案：
一种预测困难气道的计算机应用软件及气道管理数据系统，包括医务人员应用模块和中枢控制模块，医务人员应用模块包括困难气道几何学计算机模拟程序单元、困难气道多数据机器学习预测模型程序单元、困难气道人脸识别机器学习预测模型程序单元、图像信息输入及处理单元、预测结果输出单元、软件启动单元、注册登录单元、首页界面设计单元、患者信息输入单元以及第一信息管理与检索单元。
困难气道几何学计算机模拟程序单元负责上气道解剖特征点提取、特征点坐标定位模拟、喉镜显露声门时的解剖特征点运动规律和轨迹、困难气道患者的参数变化、参数间的几何学相互作用关系以及力学相互作用关系，所述参数为喉镜显露声门时各解剖特征点的旋转量和位移量，从而建立上气道几何学解剖模型，困难气道多数据机器学习预测模型程序单元负责利用机器学习技术建立人工智能预测模型程序，困难气道人脸识别机器学习预测模型程序单元负责利用机器学习建立困难气道面部识别人工智能预测模型程序，图像信息输入及处理单元负责对上传至中央服务器的患者必要信息进行计算处理，预测结果输出单元负责输出计算结果至中央数据库和使用者终端显示器及存储器。
中枢控制模块包括困难气道预测程序性能优化单元、使用者注册认证单元、使用人员管理授权单元以及第二信息管理与检索单元，困难气道预测程序性能优化单元负责对困难气道程序进行选择。
该困难气道预测包括以下步骤：
S1：构建困难气道上气道解剖几何学分析理论，通过几何学模型，分析上气道解剖特征构成困难气道的几何学原理，根据患者上气道解剖特征形成困难气道的原理，重建上气道解剖二维示意图形；
S2：通过一定样本量的临床观察，根据统计学方法分析，确定数种形成困难气道的最关键最直接的解剖特征因子，从而进行解剖特征点提取，所述解剖特征点包括头、颈、舌体、下颌、咽腔、喉；
S3：依据一定量的样本测量数据，确定患者上气道各个解剖特征点的坐标及尺寸以及这些尺寸大小的决定因素，建立上气道解剖模拟坐标系，接着把与困难气道形成相关的解剖特征点定位到坐标系中；
S4：接着通过一定样本量临床实际患者，包括困难气道患者，观察各解剖特征点在喉镜显露声门操作时的位移、变形、旋转的运动轨迹，通过观察一定样本量的困难气道患者，明确上述解剖特征点在困难气道患者中的变化特点及互相影响；
S5：通过解剖特征点之间的几何学相互作用关系以及力学相互作用关系，计算求出各解剖特征点在坐标系里的旋转量和位移量的参数回归方程；
S6：通过计算机图形编辑控制技术，建立可交互操作的上气道模拟图形，实现对这些解剖特征点参数的交互操作，通过图形控制的方法复现上述变化，最后根据前述的上气道解剖在喉镜检查时各个参数的回归方程，制定相应计算机图形控制规则，并根据患者术前评估的解剖特征形成最终图形计算患者在喉镜检查时的声门视野，从而准确预测患者是否是困难气道。
其中，步骤S5里的各解剖特征点的旋转量和位移量包括：头的旋转量、下颌前移的方向角度和距离、声门位移角度和距离、舌体压缩方向角度和距离，参数回归方程的范例如下：
y＝a1x1+a2x2+a3x3+...+b
其中，y为所求参数值，即相应解剖特征点最终位移的方向或量，x为输入的变量，即临床检测值，a、b为方程的调节系数和常数项，其值依赖于临床数据的统计学结果，该方程将决定每个解剖特征点的位移轨迹，其中的每个参数都具备反复修改优化的功能。
患者信息输入单元由使用者经实名认证登录后，输入患者的必要信息，必要信息包括患者年龄、性别、身高、体重、张口度、甲颏距离、舌颏距离、舌体厚度、颞颌关节活动度、患者的面部正面照、侧面仰头嗅物位照，并将其上传至中央服务器，使用者还可输入患者的真实手术过程数据，如是否发生困难气道， 困难气道的类型、程度以及处理结果，并将其存储在中央服务器数据库。
图像信息输入及处理单元对上传至中央服务器的数据与图像进行计算分析，对患者的面部图像信息，优先调用面部识别程序识别患者的眼部，打码马赛克以隐藏可识别患者的隐私信息，从而对其进行存储、计算和分析。
中央服务器接收终端上传的数据及图像，调用相应程序单元进行计算分析，计算完成后，输出计算结果至中央数据库和使用者终端显示器及存储器，预测结果输出单元的输出结果包括图像的模拟输出、声门视野的计算结果输出以及依据大数据统计的该值的可信范围。
使用者可反馈使用经验、体验和建议，通过系统管理者授权从而自定义自己的数据种类目录；
系统管理者可通过中枢控制模块对系统各项参数进行设置，包括使用者人员管理、授权项目内容、调用程序单元选择和模式选择，模式包括临床应用模式和科学研究模式；
系统管理者可通过中枢控制模块对困难气道程序进行选择、置换、更新以及优化；
系统管理者可对服务器存储的数据进行检索、分类、删除、备份、编辑和分析。
如图4首页界面示意图所示，使用者登录之后可以进行项目选择，若选择“新患者”，则会进入如图5所示的信息输入界面，使用者将患者的信息输入进去，经过系统一系列的分析之后，分析结果如图6所示的结果输出界面，计算得到声门视野，从而对该患者进行预测是否是困难气道患者；若选择“我的患者”，则进入患者数据界面，使用者可以修改和删除自己管理的患者数据，且可进一步对患者数据进行检索以及分类，还可生成患者的病例报告表。
需要说明的是，在本文中，诸如第一和第二等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来，而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。而且，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者设备所固有的要素。
最后应说明的是：以上所述仅为本发明的优选实施例而已，并不用于限制本发明，尽管参照前述实施例对本发明进行了详细的说明，对于本领域的技术人员来说，其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换。凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。

Claims (8)

  1. 一种预测困难气道的计算机应用软件及气道管理数据系统，包括医务人员应用模块和中枢控制模块，其特征在于：所述医务人员应用模块包括困难气道几何学计算机模拟程序单元、困难气道多数据机器学习预测模型程序单元、困难气道人脸识别机器学习预测模型程序单元、图像信息输入及处理单元、预测结果输出单元，
    所述困难气道几何学计算机模拟程序单元负责上气道解剖特征点提取、特征点坐标定位模拟、喉镜显露声门时的解剖特征点运动规律和轨迹、困难气道患者的参数变化、参数间的几何学相互作用关系以及力学相互作用关系，所述参数为喉镜显露声门时各解剖特征点的旋转量和位移量，从而建立上气道几何学解剖模型，所述困难气道多数据机器学习预测模型程序单元负责利用机器学习技术建立人工智能预测模型程序，所述困难气道人脸识别机器学习预测模型程序单元负责利用机器学习建立困难气道面部识别人工智能预测模型程序，所述图像信息输入及处理单元负责对上传至中央服务器的患者必要信息进行计算处理，所述预测结果输出单元负责输出计算结果至中央数据库和使用者终端显示器及存储器，
    所述中枢控制模块包括困难气道预测程序性能优化单元，所述困难气道预测程序性能优化单元负责对困难气道程序进行选择。
  2. 根据权利要求1所述的一种预测困难气道的计算机应用软件及气道管理数据系统，其特征在于：该困难气道预测包括以下步骤：
    S1：构建困难气道上气道解剖几何学分析理论，通过几何学模型，分析上气道解剖特征构成困难气道的几何学原理，根据患者上气道解剖特征形成困难气道的原理，重建上气道解剖二维示意图形；
    S2：通过一定样本量的临床观察，根据统计学方法分析，确定数种形成困难气道的最关键最直接的解剖特征因子，从而进行解剖特征点提取，所述解剖特征点包括头、颈、舌体、下颌、咽腔、喉，以及它们的附属组织和边界点；
    S3：依据一定量的样本测量数据，确定患者上气道各个解剖特征点的坐标及尺寸以及这些尺寸大小的决定因素，建立上气道解剖模拟坐标系，接着将与困难气道形成相关的解剖特征点定位到坐标系中；
    S4：接着通过一定样本量临床实际患者，包括困难气道患者，观察各解剖特征点在喉镜显露声门操作时的位移、变形、旋转的运动轨迹，通过观察一定样本量的困难气道患者，明确上述解剖特征点在困难气道患者中的变化特点及互相影响；
    S5：通过解剖特征点之间的几何学相互作用关系以及力学相互作用关系，计算求出各解剖特征点在坐标系里的旋转量和位移量的参数回归方程；
    S6：通过计算机图形编辑控制技术，建立可交互操作的上气道模拟图形，实现对这些解剖特征点参数的交互操作，通过图形控制的方法复现上述变化，最后根据前述的上气道解剖在喉镜检查时各个参数的回归方程，制定相应计算机图形控制规则，并根据患者术前评估的解剖特征形成最终图形计算患者在喉镜检查时的声门视野，从而准确预测患者是否是困难气道。
  3. 根据权利要求1所述的一种预测困难气道的计算机应用软件及气道管理数据系统，其特征在于：所述各解剖特征点的旋转量和位移量包括：头的旋转量、下颌前移的方向角度和距离、声门位移角度和距离、舌体压缩方向角度和距离。
  4. 根据权利要求1所述的一种预测困难气道的计算机应用软件及气道管理数据系统，其特征在于：所述图像信息输入及处理单元对上传至中央服务器的数据与图像进行计算分析，对患者的面部图像信息，优先调用面部识别程序识别患者的眼部，打码马赛克以隐藏可识别患者的隐私信息，从而对其进行存储、计算和分析。
  5. 根据权利要求1所述的一种预测困难气道的计算机应用软件及气道管理数据系统，其特征在于：所述预测结果输出单元的输出结果包括图像的模拟输出、声门视野的计算结果输出以及依据大数据统计的该值的可信范围。
  6. 根据权利要求1所述的一种预测困难气道的计算机应用软件及气道管理数据系统，其特征在于：所述医务人员模块还包括软件启动单元、注册登录单元、首页界面设计单元、患者信息输入单元以及第一信息管理与检索单元，
    所述患者信息输入单元由使用者经实名认证登录后，输入患者的必要信息，所述必要信息包括患者年龄、性别、身高、体重、张口度、甲颏距离、舌颏距离、舌体厚度、颞颌关节活动度、患者的面部正面照、侧面仰头嗅物位照，并将其上传至中央服务器；使用者还可输入患者的真实手术过程数据，如是否发生困难气道，困难气道的类型、程度以及处理结果，并将其存储在中央服务器数据库，且每个输入框后面，都附有帮助信息。
  7. 根据权利要求1所述的一种预测困难气道的计算机应用软件及气道管理数据系统，其特征在于：所述中枢控制模块还包括使用者注册认证单元、使用人员管理授权单元以及第二信息管理与检索单元。
  8. 根据权利要求1所述的一种预测困难气道的计算机应用软件及气道管理数据系统，其特征在于：
    使用者可修改和删除自己管理的患者数据，且可进一步对患者数据进行检索以及分类，还可生成患者的病例报告表，使用者可反馈使用经验、体验和建议，通过系统管理者授权从而自定义自己的数据种类目录，
    系统管理者可通过中枢控制模块对系统各项参数进行设置，包括使用者人员管理、授权项目内容、调用程序单元选择和模式选择，所述模式包括临床应用模式和科学研究模式，系统管理者可通过中枢控制模块对困难气道程序进行选择、置换、更新以及优化，系统管理者可对服务器存储的数据进行检索、分类、删除、备份、编辑和分析。
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