Method for generating auxiliary diagnosis data report of patient with eyelid dyskinesia

Abstract

The invention provides a method for generating an auxiliary diagnosis data report of a patient with eyelid dyskinesia, and the method comprises the steps: collecting target part auxiliary diagnosis analysis data of a target part of the patient under a corresponding preset collection condition, and obtaining eyelid dyskinesia description information corresponding to the target part of the patient based on the target part auxiliary diagnosis analysis data, and generating an eyelid dyskinesia auxiliary diagnosis data report of the patient based on the eyelid dyskinesia description information. According to the method, the dependence on subjective experience and ability of doctors is reduced, the accuracy of auxiliary diagnosis and analysis of the eyelid dyskinesia is improved, errors caused by electromyography and neostigmine are avoided, meanwhile, radiation damage caused by a ray scanning mode to a human body is also avoided, the diagnosis and analysis cost is reduced, and the diagnosis and analysis time is shortened.

Description

Technical Field

[0001] This application relates to the technical field of auxiliary diagnosis and analysis of eyelid movement dysfunction. Specifically, it relates to a method for generating an auxiliary diagnosis data report for patients with eyelid movement dysfunction. Background Art

[0002] In the medical field, millions of patients have symptoms of blinking dysfunction characterized by incomplete closure of the upper and lower eyelids due to diseases such as facial nerve paralysis (Bell palsy), ptosis (blepharoptosis), blepharospasm, eyelid tumors or hyperplasia, neuromuscular diseases, meibomian gland dysfunction (meibomianitis), eyelid deformities, craniocerebral injuries or strokes, and eye infections or inflammations. These patients are called patients with eyelid movement dysfunction. Doctors need to perform auxiliary diagnosis and analysis on patients with eyelid movement dysfunction in order to carry out subsequent treatment and rehabilitation processes based on the results of the auxiliary diagnosis and analysis. The accuracy of the auxiliary diagnosis and analysis is directly related to the treatment and rehabilitation effects of eyelid movement dysfunction.

[0003] Currently, doctors mainly use manual visual methods, electromyography or neostigmine tests, and ray scanning methods such as CTA and MRI to perform auxiliary diagnosis and analysis of eyelid movement dysfunction on patients.

[0004] However, the manual visual method highly depends on the subjective experience and ability of doctors. It is difficult for the human eye to clearly see the details of the patient's blinking movements, and the accuracy of the auxiliary diagnosis and analysis is average. Electromyography is an invasive detection method; neostigmine is a drug detection method, so the errors of electromyography and neostigmine are relatively large. Ray scanning methods such as CTA and MRI have problems such as radiation damage to the human body, high cost, and long detection time. Summary of the Invention

[0005] In view of this, the purpose of this application is to provide a method for generating an auxiliary diagnosis data report for patients with eyelid movement dysfunction, which can obtain the description information of the patient's eyelid movement dysfunction from the auxiliary diagnosis and analysis data of the patient's eyes and face, and generate an auxiliary diagnosis data report for the patient's eyelid movement dysfunction based on the description information of the eyelid movement dysfunction. This enables doctors to perform auxiliary diagnosis and analysis of eyelid movement dysfunction on patients according to the visualized auxiliary diagnosis data report of eyelid movement dysfunction, reduces the dependence on the subjective experience and ability of doctors, improves the accuracy of the auxiliary diagnosis and analysis of eyelid movement dysfunction, avoids the errors caused by electromyography and neostigmine, and also avoids the radiation damage to the human body caused by the ray scanning method, reduces the diagnostic analysis cost, and reduces the diagnostic analysis time.

[0006] In a first aspect, an embodiment of this application provides a method for generating an auxiliary diagnosis data report for patients with eyelid movement dysfunction. The generating method includes:

[0007] Collect the target part auxiliary diagnosis analysis data of the patient under the corresponding preset collection conditions; wherein, the target part includes the eyes; the preset collection conditions at least include the blink cycle; the blink cycle includes the blink cycle of natural behavior and the blink cycle of active behavior;

[0008] Obtain the eyelid movement dysfunction description information corresponding to the target part of the patient based on the target part auxiliary diagnosis analysis data;

[0009] Generate an auxiliary diagnosis data report for the eyelid movement dysfunction of the patient based on the eyelid movement dysfunction description information.

[0010] In a possible implementation manner, the target part auxiliary diagnosis analysis data includes eyelid position data; the collection of the target part auxiliary diagnosis analysis data of the patient under the corresponding preset collection conditions includes:

[0011] For each blink cycle, determine multiple blink stages within the blink cycle;

[0012] Collect the eyelid position data of the target eye of the patient in each blink cycle at each of the multiple blink stages.

[0013] In a possible implementation manner, the collection of the eyelid position data of the target eye of the patient in each blink cycle at each of the multiple blink stages includes:

[0014] Based on the target eye, determine the collection points of the eye calibration positions with the target number on the target eye;

[0015] Collect the eye calibration position collection point data corresponding to each eye calibration position collection point on the target eye at each blink stage within each blink cycle;

[0016] Obtain the corresponding eyelid position data at this blink stage based on the eye calibration position collection point data corresponding to each eye calibration position collection point at each blink stage.

[0017] In a possible implementation manner, the eyelid movement dysfunction description information includes eyelid movement state description information; the obtaining of the eyelid movement dysfunction description information corresponding to the target part of the patient based on the target part auxiliary diagnosis analysis data includes:

[0018] Calculate the eyelid closure degree of the eyelid at each blink stage within each blink cycle in the eyelid position data based on a preset eye movement tracking algorithm;

[0019] Generate a corresponding eyelid closure curve graph based on the eyelid closure degree of each eyelid stage within each blink cycle and the eyelid position data; wherein, the eyelid closure curve graph is the eyelid closure curve graph of the target eye of the patient within this blink cycle; wherein, the eyelid closure curve graph characterizes the eyelid closure degree of the target eye of the patient within this blink cycle;

[0020] Determine the eyelid movement state description information of the patient based on the eyelid closure curve graph.

[0021] In a possible implementation manner, the generating the auxiliary diagnosis data report of the eyelid movement dysfunction of the patient based on the eyelid movement dysfunction description information includes:

[0022] Determine the corresponding eyelid movement ability analysis index based on the eyelid movement state description information;

[0023] Obtain the auxiliary diagnosis data report template for eyelid movement dysfunction and the user information of the patient;

[0024] Generate the auxiliary diagnosis data report of the eyelid movement dysfunction of the patient based on the eyelid movement ability analysis index, the auxiliary diagnosis data report template for eyelid movement dysfunction, and the user information.

[0025] In a second aspect, the embodiments of the present application further provide a generating device for the auxiliary diagnosis data report of a patient with eyelid movement dysfunction, and the generating device includes:

[0026] An acquisition module, configured to acquire the target part auxiliary diagnosis analysis data of the patient under the corresponding preset acquisition conditions; wherein, the target part includes the eyes; the preset acquisition conditions at least include the blink cycle; the blink cycle includes the blink cycle of natural behavior and the blink cycle of active behavior;

[0027] An obtaining module, configured to obtain the eyelid movement dysfunction description information corresponding to the target part of the patient based on the target part auxiliary diagnosis analysis data;

[0028] A generating module, configured to generate the auxiliary diagnosis data report of the eyelid movement dysfunction of the patient based on the eyelid movement dysfunction description information.

[0029] In a possible implementation manner, the target part auxiliary diagnosis analysis data includes eyelid position data; the acquisition module is specifically configured to:

[0030] For each blink cycle, determine multiple blink stages within this blink cycle;

[0031] Collect the eyelid position data of the target eye of the patient in each of the multiple blinking stages within each blinking cycle.

[0032] In a possible implementation manner, the acquisition module is specifically configured to:

[0033] Determine a target number of eye calibration position acquisition points on the target eye based on the target eye;

[0034] Collect the eye calibration position acquisition point data corresponding to each eye calibration position acquisition point of the target eye in each blinking stage within each blinking cycle;

[0035] Obtain the corresponding eyelid position data in this blinking stage based on the eye calibration position acquisition point data corresponding to each eye calibration position acquisition point in each blinking stage.

[0036] In a possible implementation manner, the eyelid movement dysfunction description information includes eyelid movement state description information; the acquisition module is specifically configured to:

[0037] Calculate the eyelid closure degree of the eyelid in each blinking stage within each blinking cycle in the eyelid position data based on a preset eye movement tracking algorithm;

[0038] Generate a corresponding eyelid closure degree curve graph based on the eyelid closure degree of the eyelid in each blinking stage within each blinking cycle and the eyelid position data; wherein, the eyelid closure degree curve graph is the eyelid closure degree curve graph of the target eye of the patient in this blinking cycle; wherein, the eyelid closure degree curve graph characterizes the eyelid closure degree of the target eye of the patient in this blinking cycle;

[0039] Determine the eyelid movement state description information of the patient based on the eyelid closure degree curve graph.

[0040] In a possible implementation manner, the generation module is specifically configured to:

[0041] Determine corresponding eyelid movement ability analysis indicators based on the eyelid movement state description information;

[0042] Obtain an eyelid movement dysfunction auxiliary diagnosis data report template and the user information of the patient;

[0043] Generate an eyelid movement dysfunction auxiliary diagnosis data report for this patient based on the eyelid movement ability analysis indicators, the eyelid movement dysfunction auxiliary diagnosis data report template, and the user information.

[0044] In a third aspect, an embodiment of the present application provides an electronic device, including: a processor, a storage medium, and a bus. The storage medium stores machine-readable instructions executable by the processor. When the electronic device runs, the processor communicates with the storage medium through the bus. The processor executes the machine-readable instructions to perform the steps of the method for generating an auxiliary diagnosis data report for patients with eyelid movement dysfunction according to any one of the first aspects.

[0045] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, it performs the steps of the method for generating an auxiliary diagnosis data report for patients with eyelid movement dysfunction according to any one of the first aspects.

[0046] An embodiment of the present application provides a method for generating an auxiliary diagnosis data report for patients with eyelid movement dysfunction, which collects target site auxiliary diagnosis analysis data of a patient under corresponding preset collection conditions, obtains eyelid movement dysfunction description information corresponding to the target site of the patient based on the target site auxiliary diagnosis analysis data, and generates an auxiliary diagnosis data report for the patient's eyelid movement dysfunction based on the eyelid movement dysfunction description information. In this application, the eyelid movement dysfunction description information of the patient can be obtained through the collected auxiliary diagnosis analysis data of the patient's eyes and face, and an auxiliary diagnosis data report for the patient's eyelid movement dysfunction is generated based on the eyelid movement dysfunction description information, so that doctors can perform auxiliary diagnosis analysis of eyelid movement dysfunction on the patient according to the visual auxiliary diagnosis data report of eyelid movement dysfunction, reducing the dependence on the subjective experience and ability of doctors, improving the accuracy of auxiliary diagnosis analysis of eyelid movement dysfunction, avoiding the errors caused by electromyography and neostigmine, and at the same time avoiding the radiation damage to the human body caused by the ray scanning method, reducing the diagnosis analysis cost, and reducing the diagnosis analysis time.

[0047] To make the above objects, features, and advantages of the present application more obvious and understandable, the following specific preferred embodiments are given in conjunction with the accompanying drawings and are described in detail as follows. Description of the Drawings

[0048] To more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present application and should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without creative efforts.

[0049] Figure 1 is a flowchart of a method for generating an auxiliary diagnosis data report for patients with eyelid movement dysfunction according to an embodiment of the present application;

[0050] Figure 2 is a schematic diagram of the eyelid closure degree curve Figure 1 of;

[0051] Figure 3 is a schematic diagram of the natural blink detection result;

[0052] Figure 4 is a schematic diagram of the key picture for evidencing the auxiliary diagnosis information;

[0053] Figure 5 is a flowchart of a method for generating an auxiliary diagnosis data report for a patient with eyelid movement dysfunction according to another embodiment of the present application;

[0054] Figure 6 is a schematic diagram of the eye calibration position acquisition points;

[0055] Figure 7 is a flowchart of a method for generating an auxiliary diagnosis data report for a patient with eyelid movement dysfunction according to another embodiment of the present application;

[0056] Figure 8 is the eyelid closure degree curve Figure 2 of;

[0057] Figure 9 is a schematic diagram of the bilateral eyelid closure degree symmetry curve graph;

[0058] Figure 10 is a flowchart of a method for generating an auxiliary diagnosis data report for a patient with eyelid movement dysfunction according to another embodiment of the present application;

[0059] Figure 11 is a schematic structural diagram of a device for generating an auxiliary diagnosis data report for a patient with eyelid movement dysfunction according to an embodiment of the present application;

[0060] Figure 12 is a schematic structural diagram of a computer device according to an embodiment of the present application. Detailed implementation manners

[0061] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. It should be understood that the accompanying drawings in the present application are only for the purposes of illustration and description, and are not used to limit the protection scope of the present application. In addition, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in the present application illustrate the operations implemented according to some embodiments of the present application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical context may be reversed or implemented simultaneously. In addition, those skilled in the art may add one or more other operations to the flowchart or remove one or more operations from the flowchart under the guidance of the content of the present application.

[0062] In addition, the described embodiments are only some embodiments of the present application, rather than all embodiments. The components of the embodiments of the present application described and illustrated in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the present application claimed, but merely represents the selected embodiments of the present application. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative efforts belong to the scope of protection of the present application.

[0063] It should be noted that the term "including" will be used in the embodiments of the present application to indicate the existence of the features stated thereafter, but does not exclude the addition of other features.

[0064] Considering that in the medical field, there are millions of patients with symptoms of blinking dysfunction characterized by incomplete closure of the upper and lower eyelids due to diseases such as facial nerve palsy (Bell palsy), ptosis (blepharoptosis), blepharospasm, eyelid tumors or hyperplasia, neuromuscular diseases, meibomian gland dysfunction (meibomianitis), eyelid deformities, craniocerebral injuries or strokes, and eye infections or inflammations. Such patients are called patients with eyelid motor dysfunction. Doctors need to perform auxiliary diagnostic analysis on patients with eyelid motor dysfunction in order to carry out subsequent treatment and rehabilitation processes based on the results of the auxiliary diagnostic analysis, and the accuracy of the auxiliary diagnostic analysis is directly related to the treatment and rehabilitation effects of eyelid motor dysfunction.

[0065] Currently, doctors mainly use artificial visual methods, electromyography or neostigmine tests, as well as ray scanning methods such as CTA and MRI to perform auxiliary diagnostic analysis on patients with eyelid motor dysfunction.

[0066] However, the manual visual method highly depends on the subjective experience and ability of doctors. It is difficult for the human eye to clearly see the details of the patient's blinking movements, and the accuracy of auxiliary diagnosis and analysis is generally average; electromyogram is an invasive detection method; neostigmine is a drug detection method, so the errors of electromyogram and neostigmine are relatively large; ray scanning methods such as CTA and MRI have problems such as radiation damage to the human body, high cost, and long detection time.

[0067] To address this problem, the present application provides a method for generating an auxiliary diagnosis data report for patients with eyelid movement dysfunction. By obtaining the description information of the patient's eyelid movement dysfunction from the auxiliary diagnosis and analysis data of the patient's eyes and face, and generating an auxiliary diagnosis data report for the patient's eyelid movement dysfunction based on the description information of the eyelid movement dysfunction, so that doctors can perform auxiliary diagnosis and analysis of the patient's eyelid movement dysfunction according to the visual auxiliary diagnosis data report of the eyelid movement dysfunction, reducing the dependence on the subjective experience and ability of doctors, improving the accuracy of the auxiliary diagnosis and analysis of the eyelid movement dysfunction, avoiding the errors caused by electromyogram and neostigmine, and at the same time avoiding the radiation damage to the human body caused by the ray scanning method, reducing the diagnosis and analysis cost, and reducing the diagnosis and analysis time.

[0068] Figure 1 It is a flowchart of a method for generating an auxiliary diagnosis data report for patients with eyelid movement dysfunction according to an embodiment of the present application. As Figure 1 shown, the method for generating an auxiliary diagnosis data report for patients with eyelid movement dysfunction according to the embodiment of the present application may specifically include:

[0069] S101, collect the auxiliary diagnosis and analysis data of the target part of the patient under the corresponding preset collection conditions.

[0070] In the embodiment of the present application, the patient is the patient to be subjected to the auxiliary diagnosis and analysis of eyelid movement dysfunction, the target part is the part of the patient for the auxiliary diagnosis and analysis of eyelid movement dysfunction, the target part includes the eyes, and the preset collection conditions are the conditions for collecting the auxiliary diagnosis and analysis data of this part of the patient's target part. The preset collection conditions at least include the blinking cycle. The blinking cycle includes the blinking cycle of natural behavior and the blinking cycle of active behavior. The blinking cycle is a complete cycle of a patient's one-time blinking activity. The auxiliary diagnosis and analysis data of the target part includes eyelid position data, iris position data, pupil center point data, etc. Among them, the eyelid position data is the data of the position where the patient's eyelid is located, which can also be understood as the eye closure movement data. The iris position data is the data of the position where the iris of the patient's eyeball is located. The pupil center point data is the data of the position of the center point of the pupil of the patient's eyeball. Collect the auxiliary diagnosis and analysis data of the patient's eyes under the preset blinking cycles, that is, the blinking cycles of natural behavior and active behavior, for subsequent processing.

[0071] Among them, the auxiliary diagnostic analysis data of the target part can be data in the form of a video, that is, the eyelid position data can be data in the form of a video. This application does not make excessive limitations on this and can be set according to the actual situation.

[0072] It should be noted that in one blink cycle of the left eye (or right eye), there are 5 critical moments from the start to the end: among them, T1 is the moment when closing the eyes starts, T2 is the moment when the eyes are just closed to the lowest steady state of this blink, T3 is the moment when the degree of eye closure is the highest or close to the highest during the process of keeping the eyes closed to the lowest, T4 is the moment when opening the eyes starts, and T5 is the moment when the eyes are opened to the steady state.

[0073] It should also be noted that the eyelid position data of this application is only one type of auxiliary diagnostic analysis data of the eye. This application takes the eyelid position data as an example to describe the auxiliary diagnostic analysis data of the eye, but does not constitute a limitation on the auxiliary diagnostic analysis data of the eye and can be set according to the actual situation. For example, the auxiliary diagnostic analysis data of the eye can be iris position data, corneal position data, pupil position data, etc.

[0074] S102. Obtain the description information of the eyelid movement dysfunction corresponding to the target part of the patient based on the auxiliary diagnostic analysis data of the target part.

[0075] In the embodiment of this application, the description information of the eyelid movement dysfunction is the eyelid position and movement information of the patient (for example, Figure 2 As shown, it represents the dynamic position changing in time sequence, eye closure represents eyelid closure, blink wave represents blink wave, and time (frame id) represents the identification and scheduling within the time frame), which characterizes the eyelid closing ability of the patient. Therefore, the description information of the eyelid movement dysfunction is also the description information of the eyelid closing ability corresponding to the eyelid position and time sequence relationship of the patient. Doctors can perform auxiliary diagnostic analysis of the eyelid movement dysfunction on the patient based on the description information of the eyelid movement dysfunction. The description information of the eyelid movement dysfunction includes the description information of the eyelid movement state. Based on the auxiliary diagnostic analysis data of the target part of the patient collected in step S101, further obtain the description information of the eyelid movement dysfunction corresponding to the target part of the patient, that is, obtain the description information of the eyelid movement dysfunction of the patient's eyes based on the eyelid position data of the patient.

[0076] It should be noted that the description information of the eyelid movement state represents the closing state of the patient's eyelids. According to the closing state of the patient's eyelids, it can provide clues and basis for doctors to analyze the execution ability of the patient's eye closure (for example, whether the eyes can be completely closed with different forms of movement (natural movement and active movement)), so as to perform auxiliary diagnostic analysis of the eyelid movement dysfunction on the patient.

[0077] S103. Generate an auxiliary diagnosis data report for the patient's eyelid movement dysfunction based on the description information of the eyelid movement dysfunction.

[0078] In the embodiment of the present application, the auxiliary diagnosis data report for eyelid movement dysfunction is a visual data report used to assist doctors in the auxiliary diagnosis and analysis of the patient's eyelid movement dysfunction. For example, Figure 3 As shown, it is a data report characterizing the natural blink detection result, including data such as number, index name, detection results of the left and right eyes, reference value, unit, and prompt; key pictures with evidence can also be provided together to corroborate the auxiliary diagnosis information. For example, Figure 4 As shown, it is the specific information under the maximum eye closure frame of the voluntary blink action, including blink order, position, time, number of frames, and degree of closure. Figure 4 The blurred picture below is the key picture. For example, a picture of the patient's eyes closed. Generate the auxiliary diagnosis data report for the patient's eyelid movement dysfunction based on the description information of the eyelid movement dysfunction corresponding to the target part of the patient obtained in step S102 for subsequent processing.

[0079] Optionally, after generating the auxiliary diagnosis data report for the patient's eyelid movement dysfunction, the auxiliary diagnosis data report for the patient's eyelid movement dysfunction can be sent to the doctor's auxiliary diagnosis and analysis platform for eyelid movement dysfunction. Optionally, the doctor can choose to remotely view the electronic auxiliary diagnosis data report for the patient's eyelid movement dysfunction after logging in to the corresponding auxiliary diagnosis and analysis platform for eyelid movement dysfunction on the computer side or in the cloud, or print out the paper version of the auxiliary diagnosis data report for the patient for use. Also, the patient can log in to the corresponding hospital system to remotely view or print their own auxiliary diagnosis data report for eyelid movement dysfunction.

[0080] Thus, under the premise of ensuring the analysis accuracy of the algorithm, the present application collects digital videos of multiple blink actions of patients with eyelid movement dysfunction, analyzes the digital videos of blink actions, obtains the data and indexes of the closure integrity of the patient's blink actions, and generates a data detection report for the patient's closure dysfunction with attached image evidence.

[0081] The method for generating an auxiliary diagnosis data report for patients with eyelid movement dysfunction provided by the embodiments of the present application collects the auxiliary diagnosis analysis data of the target part of the patient under the corresponding preset collection conditions, obtains the description information of the eyelid movement dysfunction corresponding to the target part of the patient based on the auxiliary diagnosis analysis data of the target part, and generates an auxiliary diagnosis data report for the eyelid movement dysfunction of the patient based on the description information of the eyelid movement dysfunction. The method for generating an auxiliary diagnosis data report for patients with eyelid movement dysfunction of the present application can obtain the description information of the eyelid movement dysfunction of the patient through the collected auxiliary diagnosis analysis data of the patient's eyes and face, and generate an auxiliary diagnosis data report for the eyelid movement dysfunction of the patient based on the description information of the eyelid movement dysfunction, so that doctors can perform auxiliary diagnosis analysis of the eyelid movement dysfunction of the patient according to the visual auxiliary diagnosis data report of the eyelid movement dysfunction, reducing the dependence on the subjective experience and ability of doctors, improving the accuracy of the auxiliary diagnosis analysis of the eyelid movement dysfunction, avoiding the errors caused by electromyography and neostigmine, and at the same time avoiding the radiation damage to the human body caused by the ray scanning method, reducing the diagnosis analysis cost and reducing the diagnosis analysis time.

[0082] Further, as Figure 5 shown, step S101 in the above embodiment, "collect the auxiliary diagnosis analysis data of the target part of the patient under the corresponding preset collection conditions", may specifically include the following steps:

[0083] S501, for each blink cycle, determine multiple blink stages within the blink cycle.

[0084] In the embodiments of the present application, for a complete cycle of a blink activity, determine multiple blink stages within the blink cycle. For example, the start stage, the closing stage, the late closing stage, the opening stage, and the recovery stage. Among them, each stage represents a different state of the patient's eyelids when blinking. After determining the multiple blink stages within the blink cycle, subsequent processing can be performed.

[0085] S502, collect the eyelid position data of the patient's target eye in multiple blink stages within each blink cycle.

[0086] In the embodiments of the present application, the target eye includes the left eye and the right eye. Within a blink cycle, collect the accurate position data of the patient's target eye in multiple blink stages within the blink cycle according to the time sequence for subsequent processing.

[0087] It should be noted that the present application does not make excessive limitations on the specific method of collecting eyelid position data, which can be set according to the actual situation.

[0088] As a possible implementation, based on the target-side eye, acquisition points of the eye calibration positions with the target number on the target-side eye are determined; acquisition data of the eye calibration positions corresponding to each acquisition point of the eye calibration positions at each blink stage within each blink cycle of the target-side eye is collected; and eyelid position data corresponding to this blink stage is obtained based on the acquisition data of the eye calibration positions corresponding to each acquisition point of the eye calibration positions at each blink stage. Herein, the acquisition points of the eye calibration positions are the acquisition points set on the patient's eye. For example, as Figure 6 shown, 18 acquisition points are evenly set on the four peripheral edges of the eye corneal region. Among them, P1 and P10 are the reference points for measurement. A total of 9 positioning points, namely P1 - P5 - P10, are the acquisition points for the upper eyelid edge positioning. A total of 9 positioning points, namely P1 - P14 - P10, are the acquisition points for the lower eyelid edge positioning. The change in the corneal region area data composed of these 18 acquisition and positioning points can directly map the change in the eyelid movement (closing and opening) data during the closing process of the eyes. Therefore, the data of these 18 acquisition points of the eye calibration positions is the acquisition data of the eye calibration positions of each point collected based on each acquisition point of the eye calibration positions. For example, as Figure 6 shown, 18 acquisition points of the eye calibration positions are determined on the patient's right eye. At each blink stage within one blink cycle of the patient's right eye, based on these 18 acquisition points of the eye calibration positions, the acquisition data of the eye calibration positions of each acquisition point of the eye calibration positions at each blink stage is collected, and the eyelid position data corresponding to each blink stage is obtained according to the collected acquisition data of the eye calibration positions.

[0089] Furthermore, as Figure 7 shown, in step S102 in the above embodiment, "Based on the auxiliary diagnostic analysis data of the target part, the description information of the eyelid movement dysfunction corresponding to the target part of the patient is obtained, which may specifically include the following steps:

[0090] S701, Calculate the eyelid closure degree of the eyelid at each blink stage within each blink cycle in the eyelid position data based on a preset eye movement tracking algorithm.

[0091] In the embodiment of the present application, the eyelid closure degree is the degree of eyelid closure of the patient (directly mapped as the visible corneal area in the calculation). Obviously, the maximum value of the eyelid closure degree is 1 (the minimum visible corneal area), the minimum value is 0 (the maximum visible corneal area), and the eyelid closure degree fluctuates between 0 and 1; the eye movement tracking algorithm is a preset eye movement tracking algorithm for calculating the eyelid closure degree. For example, a high-speed and high-definition eye movement tracking algorithm. Based on the eye movement tracking algorithm, the eyelid position data of the patient collected in the above embodiment is calculated to obtain the eyelid closure degree of the eyelid at each blink stage within each blink cycle.

[0092] Optionally, input the eyelid position data of the patient into a preset calculation platform, and the calculation platform calculates the eyelid closure degree of the patient's eyelids at each different blinking stage within each blinking cycle of the collected data based on the eye movement tracking algorithm and the eyelid position data.

[0093] Optionally, after collecting the eyelid position data of the patient's target eye at each blinking stage within each blinking cycle, determine the corresponding multi-diagnostic analysis requirement indicators for the eyelid position data; calculate the multi-diagnostic analysis requirement indicators based on the eyelid position data. Among them, the multi-diagnostic analysis requirement indicators at least include the unilateral eye closure speed, the bilateral eyelid closure speed, the unilateral eyelid opening speed, the eyelid opening speed, and the symmetry of the contralateral eyelid closure and opening.

[0094] It should be noted that the present application does not overly limit the specific data calculation method after collecting the eyelid position data, and it can be set according to the actual situation. The relevant indicators involved in data calculation include but are not limited to: the unilateral (detecting the target side) eyelid closure speed, the bilateral (detecting the target side) eyelid closure speed; the unilateral (detecting the target side) eyelid opening speed, the bilateral (detecting the target side) eyelid opening speed; the symmetry of the contralateral eyelid closure and opening and other extensible calculation indicators.

[0095] It can be supplemented that the above indicators can be comprehensively configured and calculated in combination with the doctor's needs for diagnosing different symptoms, which can meet the diagnostic analysis needs for various diseases including facial paralysis and myasthenia gravis, and has a wide application prospect. For example, by combining the time series axis and comparing and tracking the minimum values of the eyelid closure degrees of the patient's bilateral eyes, the symptoms of unilateral eye myasthenia can be found, thereby confirming the physical evidence of the symptoms of unilateral facial paralysis.

[0096] For example, by combining the maximum value of the unilateral or bilateral eyelid closure speed of the patient with the maximum value measured in the healthy population, the physical evidence of eye myasthenia (a typical symptom of myasthenia patients) can be confirmed.

[0097] For example, as Figure 8 shown, it represents the change in the eyelid closure degree of a unilateral eye in a blinking cycle. Based on the preset high-speed and high-definition eye movement tracking algorithm, the eyelid position data of the patient is calculated to obtain the eyelid closure degree of the patient at each blinking stage within a blinking cycle. Among them, the abscissa represents the time series (the time of the blinking action), T1 - T5 represent five blinking stages within the blinking cycle, and the ordinate represents the eyelid closure degree of the patient.

[0098] S702, generate a corresponding eyelid closure degree curve graph based on the eyelid closure degree and eyelid position data of each blinking stage within each blinking cycle of the eyelid in one or more blinking behaviors.

[0099] In the embodiments of the present application, the eyelid closure curve graph is the eyelid closure curve graph of the target - side eye of the patient within each blink cycle. The eyelid closure curve graph characterizes the closure degree of the eyelid of the target - side eye of the patient within the blink cycle. The eyelid closure curve graph corresponding to each blink stage of the eyelid within each blink cycle, calculated based on the eyelid closure degree and eyelid position data, is generated for subsequent processing. Optionally, the eyelid position data includes the timing data of eyelid movement. The timing data of eyelid movement is matched with the corresponding eyelid closure degree to obtain the eyelid closure curve graph of one or more complete blink action cycles.

[0100] It should be noted that the blink behavior includes natural blink behavior and active blink behavior. Natural blink behavior refers to the normal physiological behavior of a person, which is not affected by external factors and is autonomously controlled by the patient's nervous system. Its cycle and behavior pattern are mainly dominated by the patient's will. Active blink behavior is the blink behavior that occurs when the doctor issues an instruction during the detection, and the patient follows it. The cycle or behavior pattern of this blink action is affected by the doctor's instruction, and its detection (observation) result may be different from the behavior of the patient's natural blink behavior. Therefore, the test process needs to collect the blink behavior data of both behaviors for comparison.

[0101] For example, as Figure 9 shown, the vertical axis is the eyelid closure degree (Degree), and the horizontal axis is the time series (the time of the blink action, Time). One group of curves is the data of the right eye, and the other group of curves is the data of the left eye. In the embodiments of the present application, the eyelid closure curve graph is the eyelid closure curve graph of the eyelids of both eyes of the patient within multiple blink cycles. By comparing with the eyelid closure curve graph of the eyelids of the contralateral eyelid within each blink cycle, the symmetry index data of the binocular closure ability can be obtained.

[0102] S703. Determine the description information of the patient's eyelid movement state based on the eyelid closure curve graph.

[0103] In the embodiments of the present application, according to the eyelid closure curve graph of the eyelids of the target - side eye of the patient obtained in step S702 within one or more blink cycles, the description information of the patient's eyelid movement state is determined.

[0104] Furthermore, as Figure 10 shown, step S103 in the above - mentioned embodiment, "Generate the auxiliary diagnosis data report of the patient's eyelid movement dysfunction based on the description information of eyelid movement dysfunction", may specifically include the following steps:

[0105] S1001. Determine the corresponding eyelid movement ability analysis index based on the description information of the eyelid movement state.

[0106] S1002, obtain the auxiliary diagnosis data report template for eyelid movement dysfunction and the user information of the patient.

[0107] S1003, generate the auxiliary diagnosis data report for the eyelid movement dysfunction of this patient based on the eyelid movement ability analysis indicators, the auxiliary diagnosis data report template for eyelid movement dysfunction, and the user information.

[0108] In the embodiments of the present application, based on the eyelid movement state description information obtained in the above embodiments, the corresponding eyelid movement ability analysis indicators are determined, the pre-set auxiliary diagnosis data report template for eyelid movement dysfunction (for example, name, gender, age, ID number, medical history, affected side, attending physician, department, detection time, detection technician, etc.) and the user information of the patient are obtained, and the auxiliary diagnosis data report for the eyelid movement dysfunction of this patient is generated based on the eyelid movement ability analysis indicators, the auxiliary diagnosis data report template for eyelid movement dysfunction, and the user information.

[0109] Thus, the present application uses a high-speed high-definition camera to record the video of the patient's blinking action, as well as a specially developed computer vision algorithm for analyzing this video, and the visualization detection report for finally describing the analysis results, that is, the auxiliary diagnosis data report for eyelid movement dysfunction. Doctors can directly view various analysis indicators on the auxiliary diagnosis data report for eyelid movement dysfunction to conduct auxiliary diagnosis analysis on the patient's eyelid movement dysfunction. For example, analyze the integrity of a patient's blinking action (divided into closing speed and the integrity of the upper and lower eyelids closing), use the key indicators and evidence pictures provided by the analysis report, and combine with observation to give a more accurate, objective and unified diagnosis result. On this basis, it has broad application value and important significance in medicine, such as early diagnosis of diseases, classification of diseases, condition monitoring, and evaluation of treatment effects.

[0110] Generally speaking, the combination of the high-speed high-definition camera and the computer vision algorithm in the present application is the collection of data and the calculation of various indicators, which can not only improve the accuracy and efficiency of the analysis of the closing ability, but also provide strong support for the research on eye health, the diagnosis and treatment of neuromuscular diseases, and the formulation of personalized medical plans. It has broad medical application prospects in early diagnosis, condition monitoring, efficacy evaluation, and optimization of medical resources, will bring great benefits to patients and doctors, and at the same time promote the further development of related technologies and medical research.

[0111] Figure 11 It is a flowchart of a device for generating an auxiliary diagnosis data report for patients with eyelid movement dysfunction according to an embodiment of the present application. As Figure 11 shown, the device for generating an auxiliary diagnosis data report for patients with eyelid movement dysfunction in the embodiments of the present application may specifically include:

[0112] The acquisition module 1101 is configured to acquire the target part auxiliary diagnostic analysis data of the patient's target part under the corresponding preset acquisition conditions; wherein, the target part includes the eyes; the preset acquisition conditions at least include the blink cycle; the blink cycle includes the blink cycle of natural behavior and the blink cycle of active behavior.

[0113] The obtaining module 1102 is configured to obtain the eyelid movement dysfunction description information corresponding to the patient's target part based on the target part auxiliary diagnostic analysis data.

[0114] The generation module 1103 is configured to generate an auxiliary diagnostic data report on the patient's eyelid movement dysfunction based on the eyelid movement dysfunction description information.

[0115] In a possible implementation manner, the target part auxiliary diagnostic analysis data includes eyelid position data; the acquisition module is specifically configured to:

[0116] For each blink cycle, determine multiple blink stages within the blink cycle;

[0117] Acquire the eyelid position data of the patient's target eye on the target side at multiple blink stages within each blink cycle.

[0118] In a possible implementation manner, the acquisition module is specifically configured to:

[0119] Determine the acquisition points of the eye calibration positions of the target number on the target eye based on the target eye;

[0120] Acquire the acquisition point data of the eye calibration positions corresponding to each eye calibration position acquisition point on the target eye at each blink stage within the blink cycle;

[0121] Obtain the corresponding eyelid position data at this blink stage based on the acquisition point data of the eye calibration positions corresponding to each eye calibration position acquisition point at each blink stage.

[0122] In a possible implementation manner, the eyelid movement dysfunction description information includes eyelid movement state description information; the obtaining module is specifically configured to:

[0123] Calculate the eyelid closure degree of the eyelid at each blink stage within each blink cycle in the eyelid position data based on a preset eye movement tracking algorithm;

[0124] Generate a corresponding eyelid closure degree curve graph based on the eyelid closure degree of the eyelid at each blink stage within each blink cycle and the eyelid position data; wherein, the eyelid closure degree curve graph is the eyelid closure degree curve graph of the eyelid of the patient's target eye within the blink cycle; wherein, the eyelid closure degree curve graph characterizes the eyelid closure degree of the eyelid of the patient's target eye within the blink cycle;

[0125] Determine the eyelid movement state description information of the patient based on the eyelid closure curve graph.

[0126] In a possible implementation manner, the generation module is specifically configured to:

[0127] Determine the corresponding eyelid movement ability analysis index based on the eyelid movement state description information;

[0128] Obtain the auxiliary diagnosis data report template for eyelid movement dysfunction and the user information of the patient;

[0129] Generate the auxiliary diagnosis data report for the eyelid movement dysfunction of the patient based on the eyelid movement ability analysis index, the auxiliary diagnosis data report template for eyelid movement dysfunction, and the user information.

[0130] The generation device for the auxiliary diagnosis data report of patients with eyelid movement dysfunction provided by the embodiments of the present application collects the auxiliary diagnosis analysis data of the target part of the patient under the corresponding preset collection conditions, obtains the description information of the eyelid movement dysfunction corresponding to the target part of the patient based on the auxiliary diagnosis analysis data of the target part, and generates the auxiliary diagnosis data report for the eyelid movement dysfunction of the patient based on the description information of the eyelid movement dysfunction. The generation device for the auxiliary diagnosis data report of patients with eyelid movement dysfunction of the present application can obtain the description information of the eyelid movement dysfunction of the patient through the collected auxiliary diagnosis analysis data of the patient's eyes and face, and generate the auxiliary diagnosis data report for the eyelid movement dysfunction of the patient based on the description information of the eyelid movement dysfunction, so that the doctor can perform the auxiliary diagnosis analysis of the eyelid movement dysfunction on the patient according to the visual auxiliary diagnosis data report of the eyelid movement dysfunction, reducing the dependence on the doctor's subjective experience and ability, improving the accuracy of the auxiliary diagnosis analysis of the eyelid movement dysfunction, avoiding the errors caused by electromyogram and neostigmine, and at the same time avoiding the radiation damage to the human body caused by the ray scanning method, reducing the diagnosis analysis cost, and reducing the diagnosis analysis time.

[0131] As Figure 12 shown, an electronic device 1200 provided by the embodiments of the present application includes: a processor 1201, a memory 1202, and a bus. The memory 1202 stores machine-readable instructions executable by the processor 1201. When the electronic device runs, the processor 1201 communicates with the memory 1202 through the bus, and the processor 1201 executes the machine-readable instructions to perform the steps of the method for generating the auxiliary diagnosis data report of patients with eyelid movement dysfunction as described above.

[0132] Specifically, the above-mentioned memory 1202 and processor 1201 can be general-purpose memory and processor, which are not specifically limited here. When the processor 1201 runs the computer program stored in the memory 1202, it can execute the method for generating the auxiliary diagnosis data report for patients with eyelid movement dysfunction.

[0133] Corresponding to the method for generating the auxiliary diagnosis data report for patients with eyelid movement dysfunction, an embodiment of the present application also provides a computer-readable storage medium. A computer program is stored on the computer-readable storage medium, and when the computer program is run by a processor, it executes the steps of the method for generating the auxiliary diagnosis data report for patients with eyelid movement dysfunction.

[0134] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems and devices described above can refer to the corresponding processes in the method embodiments, which will not be elaborated in this application. In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there can be other division methods in actual implementation. For another example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some communication interfaces. The indirect couplings or communication connections of the devices or modules can be in electrical, mechanical, or other forms.

[0135] The modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0136] In addition, in each embodiment of this application, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0137] When the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the deployment method described in various embodiments of this application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical discs that can store program codes.

[0138] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A method for generating an auxiliary diagnosis data report for patients with eyelid motor dysfunction, characterized in that: The generation method comprises: Collecting target part auxiliary diagnosis and analysis data of the target part of the patient under corresponding preset collection conditions; wherein the target part includes the eye; the preset collection conditions at least include the blink cycle; the blink cycle includes the blink cycle of natural behavior and the blink cycle of active behavior; Acquiring eyelid movement dysfunction description information corresponding to the target part of the patient based on the target part auxiliary diagnosis analysis data; An eyelid movement dysfunction auxiliary diagnosis data report for the patient is generated based on the eyelid movement dysfunction description information.

2. The generation method according to claim 1, characterized in that: The target part auxiliary diagnosis and analysis data includes eyelid position data; the target part auxiliary diagnosis and analysis data collected from the target part of the patient under the corresponding preset collection conditions includes: For each blink cycle, determining a plurality of blink phases within the blink cycle; The eyelid position data of the target-side eye of the patient in each blink cycle are collected in the multiple blink stages.

3. The generation method according to claim 2, characterized in that: The collecting of the eyelid position data of the target eye of the patient in each blink cycle at the multiple blink stages respectively includes: Determine a target number of eye calibration position acquisition points on the target eye based on the target eye; Collecting eye calibration position collection point data corresponding to each eye calibration position collection point of the target side eye in each blink stage in the blink cycle in each blink cycle; Based on the eye calibration position acquisition point data corresponding to each eye calibration position acquisition point in each blink stage, the eyelid position data corresponding to the blink stage is obtained.

4. The generation method according to claim 1, characterized in that: The eyelid movement dysfunction description information includes eyelid movement state description information; the eyelid movement dysfunction description information corresponding to the target part of the patient obtained based on the target part auxiliary diagnosis analysis data includes: Calculating the eyelid closure degree of the eyelid in each blink stage in each blink cycle in the eyelid position data based on a preset eye tracking algorithm; Generate a corresponding eyelid closure curve graph based on the eyelid closure degree of the eyelid in each blink stage in each blink cycle and the eyelid position data; wherein the eyelid closure curve graph is an eyelid closure curve graph of the eyelid of the target side eye of the patient in the blink cycle; wherein the eyelid closure curve graph represents the closure degree of the eyelid of the target side eye of the patient in the blink cycle; The eyelid movement state description information of the patient is determined based on the eyelid closure degree curve graph.

5. The generation method according to claim 4, characterized in that: The step of generating the patient's eyelid movement dysfunction auxiliary diagnosis data report based on the eyelid movement dysfunction description information includes: Determine a corresponding eyelid movement ability analysis index based on the eyelid movement state description information; Obtaining an eyelid movement dysfunction auxiliary diagnosis data report template and user information of the patient; An eyelid movement dysfunction auxiliary diagnosis data report of the patient is generated based on the eyelid movement ability analysis index, the eyelid movement dysfunction auxiliary diagnosis data report template, and the user information.

6. A device for generating auxiliary diagnosis data report of patients with eyelid motor dysfunction, characterized in that: The generating device comprises: A collection module, used to collect target part auxiliary diagnosis and analysis data of a patient's target part under corresponding preset collection conditions; wherein the target part includes an eye; the preset collection conditions at least include a blink cycle; the blink cycle includes a blink cycle of natural behavior and a blink cycle of active behavior; An acquisition module, configured to acquire eyelid movement dysfunction description information corresponding to the target part of the patient based on the target part auxiliary diagnosis analysis data; A generating module is used to generate an auxiliary diagnosis data report of the patient's eyelid movement dysfunction based on the eyelid movement dysfunction description information.

7. An electronic device, characterized in that: include: A processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor. When the electronic device is running, the processor and the storage medium communicate via the bus, and the processor executes the machine-readable instructions to perform the steps of the method for generating an auxiliary diagnostic data report for patients with eyelid movement dysfunction as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for generating an auxiliary diagnosis data report for patients with eyelid movement dysfunction as described in any one of claims 1 to 5 are executed.

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