CN116259405A - Robotic Procedure Automation (RPA) system and method for dyskinesia disease - Google Patents
Robotic Procedure Automation (RPA) system and method for dyskinesia disease Download PDFInfo
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Abstract
The present invention relates to a Robotic Procedure Automation (RPA) system and method for dyskinesia diseases, including patient-side, physician-side, and server-side RPA electronic archive systems. The invention has the technical effects that: the medical data is convenient to record and archive; the remote diagnosis and follow-up of the patient are enabled to be more feasible and convenient; auxiliary measurement is performed on the data uploaded by the patient by using artificial intelligence, and medical evaluation and interpretation are performed.
Description
Technical Field
The present invention relates to the field of automated medical assisted diagnosis and disease assessment, and in particular to Robotic Procedure Automation (RPA) systems and methods for dyskinesia diseases.
In particular, the present invention relates to processing and analyzing medical video images acquired by an on-line text and video scale in the medical field, and providing data references for diagnosis to doctors, and further relates to a system and method for implementing computer-aided diagnosis and follow-up robot flow automation (Robotic process automation, RPA for short).
Background
Movement disorders include a variety of conditions, such as parkinson's disease, essential tremor, facial spasm, dystonia, multiple sclerosis, and the like, as well as secondary movement disorders in patients due to stroke, trauma, infection, drug or neurodegeneration, and the like, with numerous patients worldwide. In the case of Parkinson's Disease (PD), this is the second largest neurodegenerative disease. With the aggravation of society aging, the existing 270 ten thousand PD patients in China are increasing at the speed of 10 ten thousand people per year, and besides classical four major signs (bradykinesia, resting tremor, myotonia and dysposture) PD is the main manifestation, and PD has a plurality of non-exercise symptoms: such as cognitive disorders, hyposmia, constipation, etc. In addition, patients with PD may develop catabolism after they have been effectively treated, and end-of-dose phenomena (WO). Currently, there are still a number of problems with diagnosis and follow-up for parkinson's disease patients, such as:
the comprehensive cost of the patient seeking medical treatment is high. The dyskinesia disease symptoms are generally diagnosed in a scale mode, the patient needs to finish the MDS-UPDRS, NMSS, HAMD, PDQ-39 and other character scales with the assistance of doctors or doctors' assistants after medical treatment, the time for finishing the scale process is different from 30 minutes to 180 minutes, and the time cost is high; meanwhile, the movement disorder disease symptoms expert is concentrated in a two-line city, patients in other areas need to go to medical treatment, and general parkinsonists need at least one accompanying person, so that the economic cost is high.
Medical data management is inefficient. Currently, the surface diagnosis table for PD mainly uses manual recording of doctors, and after finishing surface diagnosis, the doctor assistant records the paper surface recording information into the hospital system, so that secondary work is additionally added.
There is a lack of continuous communication between doctor and patient. At present, doctors and patients communicate only through facial diagnosis, and the doctors cannot master the recent or a period of change of the illness state of the patients, which is not beneficial to the analysis and evaluation of the illness state of the patients.
The digital development of the medical industry is advancing from informatization to intellectualization, and technologies such as remote medical treatment, robot Process Automation (RPA), artificial Intelligence (AI) and the like are continuously applied in the medical field, and innovative remote diagnosis and treatment schemes capable of being used for diagnosis and treatment of parkinson's disease are required in the industry, so that the problems and other technical defects can be solved or alleviated.
Parkinson's disease is one of the representative diseases among many dyskinesias. The nature of dyskinesias is a disorder in the planning, control or execution of movement, which can be traditionally divided into diseases that lead to hyperkinesia (hyperkinetic) and hypokinesia (hypokinetic) that lead to voluntary or involuntary movement pauses. Although with the progress of neuropathology and molecular genetics, we have had a deeper understanding of the pathogenesis of dyskinesia, and have developed more complex classification mechanisms, it is still the phenomenological characterization of dyskinesia that plays a guiding role in the initial clinical assessment and the formulation of personalized diagnosis and treatment protocols. Systematic assessment of dyskinesia patients includes detailed medical history exploration, and observation of a characteristic session of movement by experienced neurologists. Such as speed of movement, complexity, response to certain operations, diversity of gestures, and other parameters that aid in describing and classifying movement are important references for subsequent differential diagnosis and planning. In the field of dyskinesia, video recordings facilitate archiving of phenomenological features of movement for evaluation, review, and discussion of diagnosis and other clinically relevant topics among monographs. Symptomology definition of different diseases in the field of dyskinesia is the most important step in the diagnosis process, but is also a step which is easier to cause problems, so that a visual recording method is very critical, and after all, the description and diagnosis value of visual media on dyskinesia are incomparable by words. The scoring scale is also a basic tool in the evaluation of the neurology department, and aims to quantitatively record the directly observable results (such as tremor twitch and the like) in clinic and the indirectly observable subjective performances (such as fatigue and hypodynamia and the like). The cost is low, the method is simple and easy to use, and the capability of qualitative and quantitative information makes the method the most widely used tool in clinic and scientific research.
The information included in this background section of the specification of the present invention, including any references cited herein and any descriptions or discussions thereof, is included solely for the purpose of technical reference and is not to be construed as a subject matter that would limit the scope of the present invention.
Disclosure of Invention
To solve the above and other problems, according to one or more embodiments of the present invention, the present invention is directed to providing a machine automation solution in PD diagnosis and follow-up procedures, where diagnosis and follow-up of dyskinesia disease can be accomplished using a mobile terminal device using a remote communication technology, and manual repeated work can be reduced during each procedure by an automated design, reducing the influence of human factors.
According to one aspect of the present invention, there is provided a Robotic Procedure Automation (RPA) system for movement disorder diseases, the Robotic Procedure Automation (RPA) system comprising a patient side, a physician side, and an RPA electronic archive system at a server side;
wherein the RPA electronic archive system is configured to store and process data from the patient side and the doctor side; and is also provided with
The RPA electronic archive system comprises:
the system comprises an artificial intelligent auxiliary measurement module, a server side and a control module, wherein the artificial intelligent auxiliary measurement module is configured to capture motion states of a patient with dyskinesia by using uploaded videos of the patient with dyskinesia at the server side, record images and measure counts of parameters related to the motion states, and is called by doctors at the doctor side for use; the motion capture comprises the steps of extracting and calculating human body characteristic point position coordinate data and moving speed data by adopting a human body motion characteristic point extraction model based on an artificial intelligence algorithm; and
An artificial intelligence auxiliary diagnosis module is configured to evaluate the collected data of the dyskinesia patient by means of experience obtained by machine learning through an artificial intelligence algorithm and output an evaluation result so as to provide auxiliary diagnosis and evaluation information for the dyskinesia patient at the patient side and/or doctors at the doctor side.
The system according to one of the above aspects, wherein the patient end comprises the following modules: the on-line character meter module is configured to enable a patient with dyskinesia to input information in a character meter related to dyskinesia disease symptoms on line through a patient end and transmit the information to an RPA electronic file system of a server end; and
and the online video scale module is configured for remotely recording videos for evaluating the movement symptoms of the movement disorder for the patient with the movement disorder, wherein the recorded videos are automatically recorded and transmitted to the RPA electronic file system at the server side.
The system according to one of the above aspects, wherein the doctor side is adapted for an authorized doctor to evaluate data of a patient with dyskinesia, the doctor side comprising the following modules:
the evaluation module is configured to enable an authorized doctor to remotely retrieve the entered text table and video table from the RPA electronic file system for evaluation, and transmit the evaluation result to the RPA electronic file system of the server side;
And the intelligent auxiliary diagnosis data analysis module is configured to provide diagnosis analysis customized by a patient according to the text table and the video table and transmit diagnosis results to the RPA electronic file system at the server side.
The system according to one of the above aspects, wherein the physician's end further comprises a follow-up module configured for an authorized physician to view and evaluate patient information and to remotely follow-up on a patient's end for a dyskinesia patient.
The system according to the above aspect, wherein the patient end further comprises a medication assistant module for performing medication reminding and medication recording on the patient according to the prescription prescribed by the doctor, forming a medication report for the patient to check, and authorizing the doctor to remotely follow-up for use and check; and is also provided with
Wherein the doctor end further comprises at least one of the following:
the disease graph module is used for carrying out data visualization processing on the scores of the patient multiple scales, converting the numbers into coordinate axis positions and automatically drawing according to the point positions, so as to complete data automation of disease monitoring in disease process management; and/or
And a prescription module, which is used for prescribing by an authorized doctor according to the evaluation result and/or the diagnosis result and transmitting the prescription to the RPA electronic file system.
The system according to the above aspect, wherein the RPA electronic archive system further includes a encrypted cloud storage module, configured to convert data from the patient side and the doctor side into ciphertext, perform encryption criticality management using a key encryption rule and a program, and store ciphertext data keys and ciphertext files in a server.
The system according to one of the above aspects, wherein the patient side and/or the doctor side is a mobile communication device or an intelligent device with corresponding functional modules, or a mobile communication device or an intelligent device with a built-in patient side APP and/or doctor side APP.
The system according to one of the above aspects, wherein the contents of the literal scale comprise one or more of NMSS, HAMD, MDS-UPDRS, PDQ-39, PDSS, KINGS, WOQ, moCA, SSA, BMLB, NMS, SCL-90, MMSE and HAMA.
The system according to one of the above aspects, wherein the video scale contains the contents of the third portion of the MDS-UPDRS.
The system according to one of the above aspects, wherein the dyskinesia disease is one of parkinson's disease, essential tremor, facial spasm, dystonia, multiple sclerosis, post-stroke dyskinesia.
According to another aspect of the present invention, there is provided a patient side of a Robotic Procedure Automation (RPA) system for movement disorder diseases, the patient side being connected to an RPA electronic archive system provided at a server side, the patient side comprising:
the online character meter module is configured to enable a patient with dyskinesia to enter information in a character meter related to dyskinesia disease symptoms online through a patient end and transmit the information to the server end; and
the online video scale module is configured to be used for remotely recording videos for evaluating movement symptoms of movement disorder for movement disorder patients, wherein the recorded videos are automatically recorded and transmitted to a server side;
the data in the text table and the video table stored in the server side are remotely fetched and evaluated by an authorized doctor through a doctor side of a Robot Program Automation (RPA) system.
According to another aspect of the present invention, there is provided a doctor side of a Robot Procedure Automation (RPA) system for movement disorder diseases, the doctor side being connected to an RPA electronic archive system provided at a server side, the doctor side being for an authorized doctor to evaluate data of a patient with movement disorder diseases, the doctor side comprising:
The evaluation module is configured to enable an authorized doctor to evaluate according to a literal scale and a video scale which are input from a patient end and related to the symptoms of the patient with the dyskinesia disease, and transmit the evaluation result to a server end; and
and the intelligent auxiliary diagnosis data analysis module is configured to provide auxiliary diagnosis analysis customized by a patient according to the text table and the video table and transmit the diagnosis result to a server side.
The doctor end according to one of the above aspects, further comprising a follow-up module and a prescription module, wherein,
the follow-up module is configured for an authorized doctor to check and evaluate the information of the patient and remotely follow-up the patient with the dyskinesia disease at the patient end;
and the prescription module is used for prescribing by an authorized doctor according to the evaluation result and/or the diagnosis result and transmitting the prescription to a server side.
According to another aspect of the present invention, there is provided a Robotic Procedure Automation (RPA) method for movement disorder diseases, the method comprising:
the method comprises the steps of inputting an online character scale, enabling a patient with dyskinesia to input information in the character scale related to dyskinesia disease symptoms online through a patient end, and transmitting the information to an RPA electronic file system of a server end; and
Recording an online video scale to enable a patient with dyskinesia to remotely record videos for evaluating movement symptoms of dyskinesia, wherein the recorded videos are automatically recorded and transmitted to an RPA electronic file system at a server side;
performing evaluation, namely remotely retrieving the entered text table and video table from the RPA electronic file system by an authorized doctor for evaluation, and transmitting the evaluation result to the RPA electronic file system of the server side;
performing intelligent auxiliary diagnosis data analysis, providing patient customized diagnosis analysis according to the text table and the video table, and transmitting diagnosis results to an RPA electronic archive system at a server side;
performing artificial intelligence auxiliary measurement, performing motion capture on the motion state of the patient with the movement disorder disease by using the uploaded video of the patient with the movement disorder disease at a server, and performing image recording and counting measurement on parameters related to the motion state for a doctor at a doctor end to call; the motion capture comprises the steps of extracting and calculating human body characteristic point position coordinate data and moving speed data by adopting a human body motion characteristic point extraction model based on an artificial intelligence algorithm; and
And performing artificial intelligence auxiliary diagnosis, performing medical evaluation on the collected data of the dyskinesia patient by means of experience obtained by machine learning through an artificial intelligence algorithm, and outputting a result of the medical evaluation so as to provide auxiliary diagnosis and evaluation for the dyskinesia patient at the patient end and/or doctors at the doctor end.
The method according to another aspect of the above, further comprising performing a follow-up, viewing and evaluating patient information by an authorized doctor, and performing a remote follow-up on the patient-side dyskinesia patient.
The method according to another aspect of the above, wherein the text and video scales are customized scales for patients with dyskinesia.
The method according to the other aspect, wherein the method further comprises the steps of reminding and recording the medication of the patient according to the prescription issued by the doctor, forming a medication report for the patient to check at any time, and authorizing the doctor to remotely follow-up for use and check;
the doctor is authorized by the patient to check and evaluate the information, and the method further comprises the steps of reminding and recording the medication of the patient according to the prescription issued by the doctor, forming a medication report for the patient to check at any time, and authorizing the doctor to remotely follow-up for use and check; and
Performing data visualization processing on multiple scale scores of patients, converting numbers into coordinate axis positions, automatically drawing according to point positions, completing data automation of disease monitoring in disease course management, and
prescribing and transmitting a prescription to an RPA electronic archive system by an authorized doctor based on the evaluation results and/or the diagnosis results.
The method according to the above another aspect, wherein the RPA electronic archive system further converts data from the patient side and the doctor side into ciphertext through encryption cloud storage, performs encryption criticality management using a key encryption rule and a program, and stores ciphertext data keys and ciphertext files in a server.
The method according to another aspect of the above, wherein the patient side and/or the doctor side are mobile communication devices with corresponding functional modules or mobile communication devices with a patient side APP and/or a doctor side APP built in.
The method according to another aspect of the above, wherein the contents of the literal scale comprise one or more of NMSS, HAMD, MDS-UPDRS, PDQ-39, PDSS, KINGS, WOQ, moCA, SSA, BMLB, NMS, SCL-90, MMSE and HAMA.
The method according to another aspect of the above, wherein the video scale contains the content of the third portion of the MDS-UPDRS.
The method according to another aspect of the above, wherein the dyskinesia disease is one of parkinson's disease, essential tremor, facial spasm, dystonia, multiple sclerosis, post-stroke dyskinesia.
According to another aspect of the present invention, there is provided a Robotic Procedure Automation (RPA) system for remote diagnosis and follow-up of parkinson's disease, the Robotic Procedure Automation (RPA) system comprising a patient side, a physician side, and an RPA electronic archive system at a server side, the patient side comprising the following modules: the online character meter module is configured to enable a parkinsonism patient to input information in a character meter related to parkinsonism on line through a patient end and transmit the information to an RPA electronic file system of a server end; the online video scale module is configured to be used for remotely recording videos for evaluating the motion symptoms of the parkinsonism for parkinsonism patients, wherein the recorded videos are automatically recorded and transmitted to an RPA electronic file system at a server side; the doctor end is used for evaluating data of a parkinsonism patient by an authorized doctor, and comprises the following modules: the evaluation module is configured to enable an authorized doctor to retrieve the entered text table and video table from the RPA electronic file system for evaluation, and transmit the evaluation result to the RPA electronic file system of the server side; the intelligent auxiliary diagnosis data analysis module is configured to provide diagnosis analysis customized by a patient according to the text table and the video table and transmit diagnosis results to an RPA electronic archive system at a server side; and a follow-up module configured for an authorized doctor to view and evaluate the patient's information and to remotely follow-up the patient's parkinsonism patient; wherein the RPA electronic archive system is configured to store and process data from the patient side and the physician side, the RPA electronic archive system further comprising at least one of the following modules: the artificial intelligent auxiliary measurement module is configured to capture motion states of the parkinsonism patient by using the uploaded parkinsonism patient video at the server side, record images and measure counts of parameters related to the motion states, and call and use the parameters for doctors at the doctor side; and an artificial intelligence auxiliary diagnosis module configured to perform medical evaluation on the collected data of the parkinson's disease patient by means of experience obtained by machine learning by means of an artificial intelligence algorithm and output a result of the medical evaluation, so as to provide auxiliary diagnosis and evaluation for the parkinson's disease patient at the patient side and/or the doctor at the doctor side.
According to another aspect of the present invention, there is provided a patient end of a Robotic Procedure Automation (RPA) system for remote diagnosis of a condition for use with parkinson's disease patients, the patient end comprising: the online character meter module is configured to enable a parkinsonism patient to input information in a character meter related to parkinsonism on line through a patient end and transmit the information to a server end; the online video scale module is configured to be used for remotely recording videos for assessing the movement symptoms of the parkinsonism of parkinsonism patients, wherein the recorded videos are automatically recorded and transmitted to a server side; the data in the text table and the video table stored in the server side are remotely fetched and evaluated by an authorized doctor through a doctor side of a Robot Program Automation (RPA) system.
According to another aspect of the present invention, there is provided a doctor end of a Robotic Procedure Automation (RPA) system for remote diagnosis of a parkinson's disease patient, the doctor end for use by an authorized doctor in evaluating data of the parkinson's disease patient, the doctor end comprising the following modules: the evaluation module is configured to enable an authorized doctor to evaluate according to a character scale and a video scale which are input by a patient end and related to the symptoms of the parkinsonism patient, and transmit the evaluation result to a server end; the intelligent auxiliary diagnosis data analysis module is configured to provide diagnosis analysis customized by a patient according to the text table and the video table and transmit a diagnosis result to a server side; the follow-up module is configured for an authorized doctor to check and evaluate the information of the patient and remotely follow-up the parkinsonism patient at the patient end; and a prescription module for prescribing by an authorized doctor according to the evaluation result and/or the diagnosis result and transmitting the prescription to a server side.
According to another aspect of the present invention, there is provided a Robotic Procedure Automation (RPA) method for remote diagnosis and follow-up of parkinson's disease, the method comprising: performing an online character list, enabling a parkinsonism patient to input information in the character list related to parkinsonism on line through a patient end, and transmitting the information to an RPA electronic file system of a server end; performing an online video scale to enable a parkinsonism patient to remotely record a video for evaluating the motion symptoms of parkinsonism, wherein the recorded video is automatically recorded and transmitted to an RPA electronic archive system at a server side; evaluating, namely acquiring an input literal scale and a video scale from the RPA electronic file system by an authorized doctor, evaluating, and transmitting the evaluation result to the RPA electronic file system at a server side; performing intelligent auxiliary diagnosis data analysis, providing patient customized diagnosis analysis according to the text table and the video table, and transmitting diagnosis results to an RPA electronic archive system at a server side; carrying out follow-up, checking and evaluating information of the patient by an authorized doctor, and carrying out remote follow-up on the patient suffering from the Parkinson disease; performing artificial intelligence auxiliary measurement, performing motion capture on the motion state of the parkinsonism patient by using the uploaded parkinsonism patient video at a server, and performing image recording and counting measurement on parameters related to the motion state for a doctor at a doctor end to call; and performing artificial intelligence aided diagnosis, performing medical evaluation on the collected data of the parkinsonism patient by means of experience obtained by machine learning through an artificial intelligence algorithm, and outputting a result of the medical evaluation so as to provide aided diagnosis and evaluation for parkinsonism patients at a patient end and/or doctors at a doctor end.
According to one or more aspects of the present invention, the technical effects that can be achieved include: the medical data is convenient to record and archive; the remote diagnosis and follow-up of the patient are enabled to be more feasible and convenient; auxiliary measurement is performed on the data uploaded by the patient by using artificial intelligence, and medical evaluation and interpretation are performed. Specifically, the technical effects can be quantified by measuring and quantitatively presenting in several dimensions, including but not limited to, instantaneous eyes, mouth opening, tremors, finger rotation, arm rotation, leg lifting, beating, standing, walking and the like, and measuring information required for clinical judgment such as speed change, motion amplitude, motion frequency and the like through capturing key points and motion characteristics.
According to one or more embodiments of the present invention, the present invention may be widely applied to the management of various dyskinesia diseases, such as parkinson's disease, essential tremor, facial spasm, myotonia, multiple sclerosis, chorea and other primary dyskinesia, and secondary dyskinesia due to trauma, infection, stroke, drug or neurodegeneration and the like.
Further embodiments of the invention also enable other advantageous technical effects, not listed one by one, which may be partly described below and which are anticipated and understood by a person skilled in the art after reading the present invention.
Drawings
The above-mentioned and other features and advantages of embodiments of this invention, and the manner of attaining them, will become more apparent and the embodiments of the invention will be better understood by reference to the following description taken in conjunction with the accompanying drawings, wherein:
fig. 1 is a schematic structural view of a Robotic Procedure Automation (RPA) system for movement disorder diseases according to an embodiment of the present invention.
Fig. 2 is a flow chart of extracting motion feature parameters from a video using an artificial intelligence algorithm according to an embodiment of the invention.
Fig. 3 is a step of building and pre-training a human feature point extraction deep neural network model according to an embodiment of the present invention.
Detailed Description
The details of one or more embodiments of the invention are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the invention will be apparent from the description and drawings, and from the claims.
It is to be understood that the illustrated and described embodiments are not limited in application to the details of the arrangements set forth in the following description or illustrated in the drawings. The illustrated examples may be other embodiments and can be implemented or performed in various ways. Examples are provided by way of explanation, not limitation, of the disclosed embodiments. Indeed, it will be apparent to those skilled in the art that various modifications and variations can be made to the various embodiments of the invention without departing from the scope or spirit of the disclosure. For example, features illustrated or described as part of one embodiment can be used with another first embodiment to yield still a further embodiment. Accordingly, the present disclosure is intended to cover such modifications and variations as fall within the scope of the appended claims and their equivalents.
Also, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. The use of "including," "comprising," or "having" and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items.
The invention will be described in more detail below with reference to a few embodiments thereof.
It is an object of the present invention to provide a Robotic Procedure Automation (RPA) system for movement disorder diseases, an exemplary configuration of which is shown for example in fig. 1, the Robotic Procedure Automation (RPA) system comprising a patient side, a physician side and an RPA electronic archive system at a server side. The RPA electronic archive system is used to store and process data from the patient side and the physician side. The RPA electronic archive system comprises an artificial intelligence auxiliary measurement module and an artificial intelligence auxiliary diagnosis module.
In this embodiment, the artificial intelligence auxiliary measurement module is configured to use the uploaded video of the patient with dyskinesia to capture motion state of the patient with dyskinesia at the server side, record and count parameters related to motion state, and provide for a doctor at the doctor side to call. The parameters of the motion state can be blink frequency, hand and foot motion frequency, finger frequency, facial muscle tremor frequency and other physiological signs, and the parameters are presented in a specific digital chart or function waveform form. The motion capture comprises the steps of extracting and calculating the coordinate data and the moving speed data of the human body characteristic points by adopting a human body motion characteristic point extraction model based on an artificial intelligence algorithm. The artificial intelligence auxiliary diagnosis module is configured to evaluate the collected data of the dyskinesia patient by means of experience obtained by the artificial intelligence algorithm through machine learning and output an evaluation result so as to provide auxiliary diagnosis and evaluation information for the dyskinesia patient at the patient side and/or doctors at the doctor side.
In an embodiment, key nodes of a body part are identified through a video analysis technology, and motion characteristics of a patient with dyskinesia, including but not limited to facial motion, finger motion, standing posture, leg motion, gait and the like, are quantitatively analyzed to obtain motion characteristic parameters of the patient, such as amplitude, frequency, duration and angle of single or multiple actions of a human body and angle of inclination or bending of the human body when the human body is stationary. Providing an enhanced image with the motion characteristic parameter marks through recording the motion characteristic parameters, and vividly displaying the motion function of the patient with the motion disorder disease; the physician may evaluate the patient's motor function with reference to data provided by the software. Or uploading the non-motion symptom data of the patient by a doctor/patient, recording, encrypting, storing and diagrammatically presenting the non-motion symptom data as structured data arranged according to time sequence for later diagnosis and treatment reference of the doctor/patient.
Specifically, according to an embodiment, the motion characteristic parameters of the video for the motion symptom are extracted by using an artificial intelligence algorithm, as shown in fig. 2, and specifically include the following steps:
s101, acquiring at least one video for evaluating the movement symptoms of movement disorder diseases;
S102, extracting each video frame of the video, and inputting each video frame into a trained human body characteristic point extraction model to obtain human body characteristic point position coordinate data corresponding to each frame of video;
s103, carrying out smooth filtering on the coordinate data of the human body characteristic points, and calculating the moving speed data of each human body characteristic point through inter-frame difference;
s104, according to the motion symptom type corresponding to the video, and the human body feature point position coordinate data and/or the moving speed data, calculating motion feature parameters of interest corresponding to the video, including but not limited to the relative position, the connecting line included angle, the motion amplitude, the motion periodicity, the motion repetition number, the motion frequency and the like of the feature points.
Specifically, when the inter-frame smoothing filtering is performed on the coordinate data of the human feature points, the inter-frame smoothing filtering method may be a common method such as moving average, exponential moving average, kalman filter, and an euro filter.
According to an embodiment, the invention further discloses a step of establishing and pre-training a deep neural network model for extracting human body feature points, as shown in fig. 3, specifically including:
S201, acquiring a human motion video by means of self-recording the video, downloading a public video data set, crawling network public video clips and the like, and extracting video frames in the video;
s202, labeling pixel coordinate positions of characteristic points of a human body in the video frame, such as eyes, ears, nose, mouth, shoulder joints, elbow joints, wrist joints, finger joints, hip joints, knee joints, ankle joints and the like, so as to form characteristic vectors;
s203, arranging a large number of video frames and corresponding human body characteristic point coordinate position characteristic vectors to form a training data set;
s204, selecting a deep neural network architecture, including but not limited to Vgg, resNet, acceptance, unet, transform and other public neural network architectures, and constructing a deep neural network model for identifying human body feature point punctuation position feature vectors;
s205, inputting the video frame into the deep neural network model, and calculating a prediction feature vector output by a network;
s206, calculating errors between the predicted feature vector and the actually marked feature vector, for example, adopting error measurement methods such as average absolute error, mean square error and the like;
s207 judges whether the error meets the requirement, such as whether the error is smaller than a set threshold;
S208, when the error is judged to not meet the requirement, calculating gradient errors of all layers of the network model through a back propagation algorithm;
s209, after calculating the gradient error, updating the weight of each layer of the network model through a set weight updating strategy, and then repeating the iteration of the steps S205 to S207;
s210, when the error meets the requirement, the training process is ended, and model parameters are saved.
According to one embodiment, the patient side may include an online literal scale module and an online video scale module. The online character meter module is configured to enable a patient with dyskinesia to input information in a character meter related to dyskinesia disease symptoms online through a patient end and transmit the information to the RPA electronic file system of the server end. The literal scale mainly refers to a scale for obtaining the recommended content of the domestic and foreign clinical diagnosis and treatment guidelines, and the content can comprise one or more of NMSS, HAMD, MDS-UPDRS, PDQ-39, PDSS, KINGS, WOQ, moCA, SSA, BMLB, NMS, SCL-90, MMSE and HAMA. The system transfers the traditional paper quality list items to the mobile terminal, draws the items one by one through webview and provides a selection button, a patient can use the mobile terminal device to sequentially complete each item, the RPA electronic file is automatically recorded and score calculation is completed, the RPA electronic file is stored in the cloud server in an encrypted form, an authorized doctor can use the doctor terminal to log in to check the answering condition of the patient, and disease diagnosis can be completed according to the grading of the system. The online video scale module is configured for remote recording of video for evaluation of motor symptoms of motor impairment disorders by motor impairment disorder patients, wherein the recorded video is automatically recorded and transmitted to the server-side RPA electronic archive system. In particular, the video scale may contain the contents of the third portion of the MDS-UPDRS. The online video meter function in the system can be transplanted to the mobile terminal, a patient can remotely record according to the action requirement of the prompt, the recorded video can be automatically recorded and transmitted to the cloud server by using the HTTPS encryption protocol for desensitization management, and a doctor is authorized to perform professional evaluation according to the video submitted by the patient and give corresponding comments.
The doctor end is used for the authorized doctor to evaluate the data of the patient with the dyskinesia. According to an embodiment, the doctor's end may include an evaluation module and an intelligent auxiliary diagnostic data analysis module. Specifically, the evaluation module is configured to enable an authorized doctor to remotely retrieve the entered text table and video table from the RPA electronic archive system for evaluation, and transmit the evaluation result to the RPA electronic archive system of the server side; and the intelligent auxiliary diagnosis data analysis module is configured to provide diagnosis analysis customized by a patient according to the text table and the video table and transmit diagnosis results to the RPA electronic file system at the server side. In addition, the doctor's end may also include a follow-up module configured for an authorized doctor to view and evaluate patient information and to remotely follow-up on patients with dyskinesia disease at the patient's end. The follow-up function can effectively solve the problem that a doctor communicates with a patient, establishes an asynchronous communication channel, and is helpful for the doctor to observe and evaluate the disease development of the patient at any time.
According to an embodiment, the patient side of the Robotic Process Automation (RPA) system may further include a medication assistant module for performing medication reminders and medication records on the patient according to the prescription prescribed by the doctor, and forming medication reports for the patient to view, and authorizing remote follow-up use by the doctor to view. The medication assistant is a treatment auxiliary function and can remind and record medication for patients according to the medication list of doctors. When the medication time node is reached, the patient is informed by the voice prompt, vibration prompt or system popup window at the patient end, and the medication name, the metering and the confirmation time are automatically recorded after the patient is confirmed. The medication report is formed, so that the patient can check at any time, and the doctor can be authorized to remotely follow-up for use and check.
The doctor end can also comprise a disease graph module which is used for carrying out data visualization processing on the scores of the patient multiple scales, converting the numbers into coordinate axis positions and automatically drawing according to the point positions, so as to complete the data automation of disease monitoring in the disease process management; and/or a prescription module for prescribing and transmitting the prescription to the RPA electronic archive system according to the evaluation result and/or the diagnosis result by an authorized doctor.
Meanwhile, in order to ensure the security of the data and protect the privacy, the RPA electronic archive system may further include an encryption cloud storage module, configured to convert the data from the patient side and the doctor side into ciphertext, use a key encryption rule and a program to perform encryption criticality management, and store a ciphertext data key and a ciphertext file into a server. Specifically, the method includes the steps of calling CreateKey to create a user master key, calling CreateDataKey to create a data key, generating a ciphertext file, destroying a plaintext key in a memory, and storing the ciphertext data key and the ciphertext file in a server.
For the convenience of remote operation, the patient side and/or doctor side may be a mobile communication device or an intelligent device with corresponding functional modules, or a mobile communication device or an intelligent device with the patient side APP and/or doctor side APP built therein.
The dyskinesia disease to which the system of the embodiments of the present invention is applicable may be one of parkinson's disease, essential tremor, facial spasm, dystonia, multiple sclerosis, post-stroke dyskinesia.
By using the system in the embodiment of the invention, a patient can use mobile terminal equipment to complete diagnosis and follow-up visit of dyskinesia diseases by using a remote communication technology, and manual repeated work is reduced by automatic design in each process, so that the influence of human factors is reduced. The system moves the meter recording mode from the traditional paper material to the mobile terminal equipment, any terminal, such as a mobile phone/a flat plate, can be used for carrying out online meter test, and a patient can finish meter test by himself at home according to guidance, so that a large amount of surface diagnosis time is saved.
In addition, the system can also assist the patient to collect and upload various medical examination report data and doctor prescription information pictures in a photographing or manual recording mode and the like, and identify and convert the information into characters, wherein the information comprises but not limited to electroencephalogram, myoelectricity, skin points, electrocardio, biochemistry, physicochemical, nuclear magnetism or other image data and the like, and the patient can authorize doctors or medical institutions on a platform to review related information in the subsequent diagnosis and treatment process.
The foregoing description of several embodiments of the invention has been presented for the purposes of illustration. It is not intended to be exhaustive or to limit the invention to the precise form and/or configuration disclosed, and the invention is not limited to the above-described exemplary embodiments. It will be apparent to those skilled in the art that other modifications and variations can be made to the disclosed embodiments of the present disclosure, which fall within the scope and spirit of the invention.
Claims (10)
1. A Robotic Procedure Automation (RPA) system for movement disorder diseases, the RPA system comprising a patient side, a physician side, and an RPA electronic archive system at a server side;
wherein the RPA electronic archive system is configured to store and process data from the patient side and the doctor side;
the RPA electronic archive system comprises:
the system comprises an artificial intelligent auxiliary measurement module, a server side and a control module, wherein the artificial intelligent auxiliary measurement module is configured to capture motion states of a patient with dyskinesia by using uploaded videos of the patient with dyskinesia at the server side, record images and measure counts of parameters related to the motion states, and is called by doctors at the doctor side for use; the motion capture comprises the steps of extracting and calculating human body characteristic point position coordinate data and moving speed data by adopting a human body motion characteristic point extraction model based on an artificial intelligence algorithm; and
An artificial intelligence auxiliary diagnosis module is configured to evaluate the collected data of the dyskinesia patient by means of experience obtained by machine learning through an artificial intelligence algorithm and output an evaluation result so as to provide auxiliary diagnosis and evaluation information for the dyskinesia patient at the patient side and/or doctors at the doctor side.
2. The Robotic Process Automation (RPA) system of claim 1, wherein the patient side comprises the following modules:
the on-line character meter module is configured to enable a patient with dyskinesia to input information in a character meter related to dyskinesia disease symptoms on line through a patient end and transmit the information to an RPA electronic file system of a server end; and
and the online video scale module is configured for remotely recording videos for evaluating the movement symptoms of the movement disorder for the patient with the movement disorder, wherein the recorded videos are automatically recorded and transmitted to the RPA electronic file system at the server side.
3. The Robotic Process Automation (RPA) system of claim 1, wherein the doctor side is configured for an authorized doctor to evaluate data of a patient with dyskinesia, the doctor side comprising:
The evaluation module is configured to enable an authorized doctor to remotely retrieve the entered text table and video table from the RPA electronic file system for evaluation, and transmit the evaluation result to the RPA electronic file system of the server side;
and the intelligent auxiliary diagnosis data analysis module is configured to provide diagnosis analysis customized by a patient according to the text table and the video table and transmit diagnosis results to the RPA electronic file system at the server side.
4. The Robotic Process Automation (RPA) system of any one of claims 1-3, wherein the patient side includes a medication assistant module for taking medication reminders and medication records for the patient according to prescriptions prescribed by the doctor and forming medication reports for viewing by the patient and authorizing remote follow-up use viewing by the doctor; and is also provided with
Wherein the doctor end comprises at least one of the following:
the disease graph module is used for carrying out data visualization processing on the scores of the patient multiple scales, converting the numbers into coordinate axis positions and automatically drawing according to the point positions, so as to complete data automation of disease monitoring in disease process management; and
and a prescription module, which is used for prescribing by an authorized doctor according to the evaluation result and/or the diagnosis result and transmitting the prescription to the RPA electronic file system.
5. The Robotic Process Automation (RPA) system of any one of claims 1-4, wherein the patient side and/or the doctor side is a mobile communication device or an intelligent device with corresponding functional modules or a mobile communication device or an intelligent device with a patient side APP and/or a doctor side APP built therein.
6. The Robotic Process Automation (RPA) system of claim 2, wherein the contents of the literal scale comprise one or more of NMSS, HAMD, MDS-UPDRS, PDQ-39, PDSS, KINGS, WOQ9, moCA, SSA, BMLB, NMS, SCL-90, MMSE, and HAMA.
7. The robotic flow automation (RPA) system of any one of claims 1-4, wherein the dyskinesia disease is one of parkinson's disease, essential tremor, facial spasm, dystonia, multiple sclerosis, post-stroke dyskinesia.
8. A patient end of a Robotic Procedure Automation (RPA) system for movement disorder diseases, the patient end connected to an RPA electronic archive system provided at a server end, the patient end comprising:
the online character meter module is configured to enable a patient with dyskinesia to enter information in a character meter related to dyskinesia disease symptoms online through a patient end and transmit the information to the server end; and
The online video scale module is configured to be used for remotely recording videos for evaluating movement symptoms of movement disorder for movement disorder patients, wherein the recorded videos are automatically recorded and transmitted to a server side;
the data in the text table and the video table stored in the server side are remotely fetched and evaluated by an authorized doctor through a doctor side of a Robot Program Automation (RPA) system.
9. A doctor side of a Robotic Process Automation (RPA) system for movement disorder diseases, the doctor side being connected to an RPA electronic archive system provided at a server side, the doctor side for an authorized doctor to evaluate data of a patient for movement disorder diseases, the doctor side comprising:
the evaluation module is configured to enable an authorized doctor to evaluate according to a literal scale and a video scale which are input from a patient end and related to the symptoms of the patient with the dyskinesia disease, and transmit the evaluation result to a server end; and
and the intelligent auxiliary diagnosis data analysis module is configured to provide auxiliary diagnosis analysis customized by a patient according to the text table and the video table and transmit the diagnosis result to a server side.
10. A Robotic Procedure Automation (RPA) method for movement disorder diseases, the method comprising:
inputting an online character scale, wherein a patient with dyskinesia disease inputs information in the character scale related to dyskinesia disease symptoms online through a patient end and transmits the information to an RPA electronic file system of a server end; and
recording an online video meter, wherein a patient with dyskinesia diseases remotely records videos for evaluating movement symptoms of dyskinesia diseases, and the recorded videos are automatically recorded and transmitted to an RPA electronic file system at a server side;
evaluating, namely acquiring the entered text table and video table from the RPA electronic file system by an authorized doctor, evaluating, and transmitting the evaluated result to the RPA electronic file system of the server side;
performing intelligent auxiliary diagnosis data analysis, providing patient customized diagnosis analysis according to the text table and the video table, and transmitting diagnosis results to an RPA electronic archive system at a server side;
performing artificial intelligence auxiliary measurement, performing motion capture on the motion state of the patient with the movement disorder disease by using the uploaded video of the patient with the movement disorder disease at a server, and performing image recording and counting measurement on parameters related to the motion state for a doctor at a doctor end to call; the motion capture comprises the steps of extracting and calculating human body characteristic point position coordinate data and moving speed data by adopting a human body motion characteristic point extraction model based on an artificial intelligence algorithm; and
And performing artificial intelligence auxiliary diagnosis, performing medical evaluation on the collected data of the dyskinesia patient by means of experience obtained by machine learning through an artificial intelligence algorithm, and outputting a result of the medical evaluation so as to provide auxiliary diagnosis and evaluation for the dyskinesia patient at the patient end and/or doctors at the doctor end.
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