Ear-nose-throat endoscope wireless image transmission system based on NFC near field triggering

Abstract

The application discloses an ear-nose-throat endoscope wireless image transmission system based on NFC near-field triggering and relates to the field of medical instruments, which solves the problem of poor image collection and transmission efficiency of the existing wireless image transmission system and comprises a data collection module, a data analysis module and an image transmission module. The data collection module collects endoscope images, analyzes the cross-sectional area of the cavity, obtains target endoscopic video analysis data, matches patients according to patient symptoms, obtains corresponding endoscopic video analysis data, and obtains initial matching data of the endoscopic patient. The data analysis module analyzes the cross-sectional area and the endoscopic depth of the endoscopic video stream, obtains the similarity of the endoscopic cavity, classifies the endoscopic video stream of the matched patient according to the similarity, and obtains video stream matching data. The image transmission module sets up the near-field triggering of the endoscope for the target endoscopic patient according to the video stream matching data and performs wireless transmission of the endoscopic image. The application can improve the collection efficiency and transmission efficiency of the endoscopic image.

Description

Technical Field

[0001] This invention belongs to the field of medical devices and relates to NFC near-field triggering technology, specifically a wireless image transmission system for ENT endoscopes based on NFC near-field triggering. Background Technology

[0002] Existing nasal endoscopes have the following drawbacks when performing wireless image acquisition and transmission:

[0003] 1. When acquiring and transmitting wireless images, existing nasal endoscopes require manual connection and setting of the initial brightness and focal length of the endoscope. They cannot be automated for different nasal patients, resulting in low efficiency of nasal endoscope image acquisition.

[0004] 2. Existing nasal endoscopes, when acquiring and transmitting wireless images, struggle to combine the similarity of the nasal cavity of the target patient with that of patients with the same condition to screen historical nasal endoscopy video streams. They also fail to combine matching video streams to set the initial endoscopic focal length and brightness of the endoscope, and cannot use NFC near-field triggering technology to complete the initial connection and invalid transmission settings of the endoscope. As a result, it is difficult to improve the efficiency and quality of endoscopic image transmission.

[0005] To address this, we propose a wireless image transmission system for ENT endoscopes based on NFC near-field triggering. Summary of the Invention

[0006] To address the shortcomings of existing technologies, the purpose of this invention is to provide a wireless image transmission system for ENT endoscopes based on NFC near-field triggering, aiming to improve the efficiency of endoscopic image acquisition and transmission.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: a wireless image transmission system for ENT endoscopes based on NFC near-field triggering, the specific working process of each module is as follows:

[0008] Data acquisition module: acquires the historical endoscopic video stream corresponding to the target endoscopic patient and performs frame-by-frame image capture. Combines the endoscopic depth with cavity cross-sectional area analysis of the captured images to obtain target endoscopic video analysis data. Based on the clinical symptoms of the target endoscopic patient, it performs symptom matching to obtain patients and acquires the endoscopic video analysis data corresponding to the symptom matching patients to obtain the initial matching data of endoscopic patients.

[0009] Data Analysis Module: Based on the initial matching data of endoscopic patients, the module performs cross-sectional area analysis and lens endoscopic depth analysis on the endoscopic video stream. Based on the analysis results, it obtains the endoscopic cavity similarity corresponding to the endoscopic video stream of the matched patients. Based on the endoscopic cavity similarity, the module classifies the endoscopic video stream of the matched patients into different types to obtain video stream matching data.

[0010] Image transmission module: Sets up near-field triggering of the endoscope for the target endoscopic patient and wirelessly transmits endoscopic images based on video stream matching data.

[0011] Furthermore, the initial matching data of endoscopic patients was obtained, as follows:

[0012] Patients requiring ENT endoscopy are identified, resulting in multiple endoscopy patients. A target endoscopy patient is then randomly selected from among these multiple endoscopy patients.

[0013] Video streams of historical endoscopic examinations corresponding to the target endoscopic patient are acquired to obtain historical endoscopic video streams. Image analysis is performed on the historical endoscopic video streams, and target endoscopic video analysis data is obtained based on the analysis results.

[0014] The clinical symptoms of nasal diseases in the target endoscopic patients were obtained, resulting in multiple target nasal symptoms. The target nasal symptoms were compared and analyzed with those of historical patients, and multiple patients matching the symptoms were obtained based on the analysis results.

[0015] The endoscopic video stream corresponding to each patient with a matching symptom is acquired to obtain multiple endoscopic video streams for matching patients.

[0016] Repeat the process of image analysis on historical endoscopic video streams to obtain endoscopic video analysis data corresponding to each segment of the patient's endoscopic video stream;

[0017] The endoscopic video analysis data corresponding to each segment of the matched patient's endoscopic video stream, as well as the target endoscopic video analysis data, are defined as the initial matching data for the endoscopic patient.

[0018] Furthermore, the target endoscopic video analysis data is acquired, as follows:

[0019] The historical endoscopic video stream is captured within a time period covered by the historical video stream. A motion screenshot time point is set within the historical video stream coverage period. The historical endoscopic video stream at the motion screenshot time point is used to capture images to obtain sample endoscopic images.

[0020] Perform cavity region circumferential analysis on the endoscopic images of the sample, and obtain the cross-sectional area value of the cavity corresponding to the endoscopic images of the sample based on the analysis results;

[0021] The historical video stream coverage period is traversed using the motion screenshot time points, and the cavity cross-sectional area value of the endoscope image corresponding to each time point is obtained. The obtained cavity cross-sectional area values ​​are set as cavity cross-sectional area from M1 to Ma in the order of the corresponding acquisition time points.

[0022] The endoscopic depth of the endoscope lens when the cross-sectional area of ​​the cavity is M1 is obtained, and the endoscopic depth of the M1 lens is obtained. Similarly, the endoscopic depth of the endoscope lens when the cross-sectional area of ​​the cavity is M2 is obtained, and the endoscopic depth of the M2 lens is obtained.

[0023] Set the cross-sectional area of ​​cavity M1 to that of cavity Ma and the endoscopic depth of lens M1 to that of lens Ma as the target endoscopic video analysis data.

[0024] Furthermore, the cavity region of the endoscopic images of the sample was analyzed by concentric circle analysis, as follows:

[0025] The nasal cavity region in the endoscopic image is marked, and a pixel is randomly selected in the marked nasal cavity region to obtain the cavity feature pixel. A parallel circle line perpendicular to the long axis of the nasal cavity is drawn through the cavity feature pixel to obtain the cavity feature circle line.

[0026] Draw a straight line perpendicular to the long axis of the nasal cavity through the feature pixel to obtain the cavity feature line. Set the center direction of the sample endoscope image as the distal extension direction of the cavity feature line, and set the edge direction of the sample endoscope image as the proximal extension direction corresponding to the cavity feature line.

[0027] The image pixels covered by the cavity feature circles are obtained, resulting in multiple circle-covered pixels.

[0028] Furthermore, the cavity region of the endoscopic images of the sample was analyzed by concentric circle analysis, as follows:

[0029] If all the pixels covered by the acquired circle line are located in the nasal cavity region, a new cavity feature pixel is selected in the proximal extension direction of the cavity feature line to obtain the cavity update pixel. The distance between the cavity update pixel and the cavity feature pixel is the update pixel distance. A parallel circle line perpendicular to the long axis of the nasal cavity is drawn through the cavity update pixel to obtain the cavity update circle line. The image pixels covered by the cavity feature circle line are acquired to obtain multiple circle line covered pixels. If there are pixels not located in the nasal cavity region among the acquired circle line covered pixels, the cavity feature circle line is set as the largest cavity circle line corresponding to the sample endoscopic cropped image. If all the pixels covered by the acquired circle line are located in the nasal cavity region, the above process is repeated until there are pixels not located in the nasal cavity region among the acquired circle line covered pixels.

[0030] If any of the pixels covered by the acquired circle line are not located in the nasal cavity region, a new cavity feature pixel is selected in the direction of the far end extension of the cavity feature line to obtain the cavity update pixel. The distance between the cavity update pixel and the cavity feature pixel is the update pixel distance. A parallel circle line perpendicular to the long axis of the nasal cavity is drawn through the cavity update pixel to obtain the cavity update circle line. The image pixels covered by the cavity feature circle line are acquired to obtain multiple circle line covered pixels. If all the acquired circle line covered pixels are located in the nasal cavity region, the cavity feature circle line is set as the largest cavity circle line corresponding to the sample endoscopic cropped image. If any of the acquired circle line covered pixels are not located in the nasal cavity region, the above process is repeated until all the acquired circle line covered pixels are located in the nasal cavity region.

[0031] The image region marked by the maximum cavity circle line in the endoscopic image of the sample is obtained to obtain the spatial cross-sectional region of the sample cavity. The area value of the spatial cross-sectional region of the sample cavity is obtained to obtain the cross-sectional area value of the cavity corresponding to the endoscopic image of the sample.

[0032] Furthermore, patients are matched with symptoms, as follows:

[0033] The history of patients who have completed nasal endoscopy is obtained, resulting in multiple historical endoscopic examination patients. One sample endoscopic examination patient is randomly selected from the multiple historical endoscopic examination patients.

[0034] Acquire the nasal clinical symptoms of patients undergoing endoscopic examination, compare the acquired nasal clinical symptoms with the target nasal symptoms for symptom consistency, set the nasal clinical symptoms with consistent symptom consistency as consistent symptoms, and calculate the ratio of the number of consistent symptoms to the number of target nasal symptoms to obtain the clinical symptom matching degree corresponding to the endoscopic examination of the sample.

[0035] Repeat the process of obtaining the clinical symptom matching degree corresponding to the sample endoscopic examination, obtain the clinical symptom matching degree corresponding to each historical endoscopic examination patient, and set a preset range of symptom matching degree. If the clinical symptom matching degree is within the preset range of symptom matching degree, the corresponding historical endoscopic examination patient is classified as a disease-matched patient. If the clinical symptom matching degree is not within the preset range of symptom matching degree, the corresponding historical endoscopic examination patient is classified as a non-disease-matched patient.

[0036] Furthermore, the video stream matching data is acquired, as follows:

[0037] Acquire initial matching data for endoscopic patients, and based on the initial matching data for endoscopic patients, acquire target endoscopic video analysis data and endoscopic video analysis data corresponding to each segment of matched patient endoscopic video stream;

[0038] Based on the target endoscopic video analysis data, the cross-sectional area of ​​cavity M1 to cavity Ma and the endoscopic depth of lens M1 to lens Ma are obtained respectively. In the Cartesian coordinate system, the coordinate point with the endoscopic depth of lens M1 and the cross-sectional area of ​​cavity M1 is set as the target coordinate point H1. The coordinate point with the endoscopic depth of lens M2 and the cross-sectional area of ​​cavity M2 is set as the target coordinate point H2. And so on, the coordinate point with the endoscopic depth of lens Ma and the cross-sectional area of ​​cavity Ma is set as the target coordinate point Ha.

[0039] Connect the target coordinates H1 to Ha sequentially to obtain the target endoscopic cross-section change polyline. Set the Cartesian coordinate system where the target endoscopic cross-section change polyline is located as the endoscopic cross-section coordinate system.

[0040] In the acquired multi-segment matched patient endoscopic video streams, arbitrarily select a sample matched video stream, obtain the endoscopic cross-sectional change polyline corresponding to the sample matched video stream based on the endoscopic video analysis data, and mark it in the endoscopic cross-sectional coordinate system to obtain the sample endoscopic cross-sectional change polyline;

[0041] Geometric feature analysis was performed on the sample endoscope cross-section polygon and the target endoscope cross-section change polygon, and the endoscope cavity similarity corresponding to the sample matching video stream was obtained based on the analysis results;

[0042] Repeat the process of obtaining the endoscopic cavity similarity corresponding to the sample matching video stream, and obtain the endoscopic cavity similarity corresponding to each segment of the patient's endoscopic video stream;

[0043] Set an endoscope cavity similarity matching interval. If the endoscope cavity similarity is within the endoscope cavity similarity matching interval, then set the corresponding matched patient endoscope video stream as a matched video stream. If the endoscope cavity similarity is not within the endoscope cavity similarity matching interval, then set the corresponding matched patient endoscope video stream as a non-matched video stream, thus obtaining video stream matching data.

[0044] Furthermore, a geometric feature analysis was performed on the polygonal line representing the change in the endoscopic cross-section, as detailed below:

[0045] The abscissa values ​​of the left edge coordinate points corresponding to the broken line of the sample endoscopic cross section are obtained to obtain the first left edge abscissa value. The abscissa values ​​of the left edge coordinate points corresponding to the broken line of the target endoscopic cross section are obtained to obtain the second left edge abscissa value. If the first left edge abscissa value is less than or equal to the second left edge abscissa value, the left edge coordinate points corresponding to the broken line of the sample endoscopic cross section are set as the first edge feature points. If the first left edge abscissa value is greater than the second left edge abscissa value, the left edge coordinate points corresponding to the broken line of the target endoscopic cross section are set as the first edge feature points.

[0046] The abscissa values ​​of the right edge coordinate points corresponding to the broken line of the sample endoscopic cross section are obtained to obtain the first right edge abscissa value. The abscissa values ​​of the right edge coordinate points corresponding to the broken line of the target endoscopic cross section are obtained to obtain the second right edge abscissa value. If the first right edge abscissa value is less than or equal to the second right edge abscissa value, the left edge coordinate point corresponding to the broken line of the target endoscopic cross section is set as the second edge feature point. If the first right edge abscissa value is greater than the second right edge abscissa value, the left edge coordinate point corresponding to the broken line of the sample endoscopic cross section is set as the second edge feature point.

[0047] Furthermore, a geometric feature analysis was performed on the polygonal line representing the change in the endoscopic cross-section, as detailed below:

[0048] In the endoscopic cross-section coordinate system, a straight line perpendicular to the x-axis is drawn through the first edge feature point to obtain the first edge feature line. A straight line perpendicular to the x-axis is drawn through the second edge feature point to obtain the second edge feature line. The closed area enclosed by the first edge feature line, the second edge feature line, the sample endoscopic cross-section change line, and the target endoscopic cross-section change line is obtained to obtain the change line deviation area.

[0049] The area of ​​the variable polyline deviation region is obtained, and the area value of the polyline deviation region is obtained. The abscissa deviation between the second edge feature point and the first edge feature point is calculated to obtain the length value of the polyline deviation time period. The average ordinate of the variable polyline of the target endoscopic section is calculated to obtain the average longitudinal ordinate of the target polyline.

[0050] The similarity of the endoscopic cavity corresponding to the sample endoscopic section is obtained by calculating the area value of the broken line deviation region, the length value of the broken line deviation time period, and the longitudinal average value of the target broken line.

[0051] The similarity of the endoscopic cavities corresponding to the sample-matched video streams is calculated using the following formula:

[0052] ;

[0053] Where Jmp is the similarity of the endoscopic cavity corresponding to the sample matching video stream, Szp is the area of ​​the polygonal deviation region, Tzp is the length of the polygonal deviation time period, and Zpz is the longitudinal average value of the target polygonal line.

[0054] Furthermore, the near-field triggering settings for the endoscope and the wireless image transmission are detailed below:

[0055] Acquire video stream matching data, acquire matching video streams based on the video stream matching data, and mark the endoscope used to examine the target endoscopic patient as the target endoscope;

[0056] Obtain the initial adjustment focal length corresponding to each matching video stream, and calculate the average of the multiple initial adjustment focal lengths to obtain the initial endoscopic focal length. Obtain the initial endoscopic adjustment brightness corresponding to each matching video stream, and calculate the average of the multiple initial endoscopic adjustment brightnesses to obtain the initial endoscopic brightness.

[0057] The initial endoscopic focus and initial endoscopic brightness are synchronized to the NFC near-field trigger terminal. The initial endoscopic focus and initial endoscopic brightness settings of the target endoscope are completed by directly connecting the NFC terminal built into the target endoscope to the NFC near-field trigger terminal.

[0058] Once the setup is complete, the target endoscope acquires real-time endoscopic images and transmits them wirelessly.

[0059] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0060] 1. When performing wireless image acquisition and transmission, this invention automates the connection of nasal endoscopes and automatically adjusts the initial shooting parameters according to the symptoms of different nasal patients and the internal geometry of the nasal cavity, which can effectively improve the image acquisition efficiency and quality of nasal endoscopes.

[0061] 2. This invention filters historical nasal endoscopy video streams by combining the nasal cavity similarity of the target patient with patients with the same condition. It sets the initial endoscopic focal length and brightness of the endoscope by matching consistent video streams and uses NFC near-field triggering technology to complete the initial connection and invalid transmission settings of the endoscope, thereby effectively improving the transmission efficiency and quality of endoscopic images. Attached Figure Description

[0062] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0063] Figure 1 This is an overall system block diagram of the present invention;

[0064] Figure 2 This is a schematic diagram of the cavity feature lines of the present invention;

[0065] Figure 3 This is a schematic diagram showing the extension direction of the present invention. Detailed Implementation

[0066] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0067] Example 1

[0068] Please see Figure 1 The present invention provides a technical solution: a wireless image transmission system for ENT endoscope based on NFC near-field triggering, comprising a data acquisition module, a data analysis module, an image transmission module and a server, wherein the data acquisition module, the data analysis module and the image transmission module are respectively connected to the server, and the server controls the data acquisition module, the data analysis module and the image transmission module respectively.

[0069] The data acquisition module acquires the historical endoscopic video stream corresponding to the target endoscopic patient and performs frame-by-frame image capture. It combines the endoscopic depth with cavity cross-sectional area analysis of the captured images to obtain target endoscopic video analysis data. Based on the clinical symptoms of the target endoscopic patient, it performs symptom matching to obtain the endoscopic video analysis data corresponding to the symptom matching patient, thus obtaining the initial matching data of the endoscopic patient.

[0070] Specifically as follows:

[0071] Patients requiring ENT endoscopy are identified, resulting in multiple endoscopy patients. A target endoscopy patient is then randomly selected from among these multiple endoscopy patients.

[0072] It should be noted here that:

[0073] In this application, the endoscopic examination performed on the target endoscopic patient referred to herein is a nasal endoscopy.

[0074] In this application, patients requiring ENT endoscopy do not include those undergoing endoscopy for the first time; that is, each patient has a historical endoscopic video stream.

[0075] Video streams of historical endoscopic examinations corresponding to the target endoscopic patient are acquired to obtain historical endoscopic video streams. Image analysis is performed on the historical endoscopic video streams, and target endoscopic video analysis data is obtained based on the analysis results.

[0076] It should be noted here that:

[0077] In this application, the historical endoscopic video stream referred to herein is specifically the video stream corresponding to the most recent endoscopic examination at the current time.

[0078] The historical endoscopic video stream is captured within a time period covered by the historical video stream. A motion screenshot time point is set within the historical video stream coverage period. The historical endoscopic video stream at the motion screenshot time point is used to capture images to obtain sample endoscopic images.

[0079] Perform cavity region circumferential analysis on the endoscopic images of the sample, and obtain the cross-sectional area value of the cavity corresponding to the endoscopic images of the sample based on the analysis results;

[0080] It should be noted here that:

[0081] In this application, within the coverage of the historical video stream, the endoscope lens is always parallel to the long axis of the nasal cavity and the geometric center point of the endoscope lens always coincides with the geometric center point of the nasal cavity cross section.

[0082] Please see Figure 2 The nasal cavity region in the endoscopic image is marked, and a pixel is randomly selected in the marked nasal cavity region to obtain the cavity feature pixel. A parallel circle line perpendicular to the long axis of the nasal cavity is drawn through the cavity feature pixel to obtain the cavity feature circle line.

[0083] Please see Figure 3Draw a straight line perpendicular to the long axis of the nasal cavity through the feature pixel to obtain the cavity feature line. Set the center direction of the sample endoscope image as the distal extension direction of the cavity feature line, and set the edge direction of the sample endoscope image as the proximal extension direction corresponding to the cavity feature line.

[0084] The image pixels covered by the cavity feature circles are obtained, resulting in multiple circle-covered pixels;

[0085] If all the pixels covered by the acquired circle line are located in the nasal cavity region, a new cavity feature pixel is selected in the proximal extension direction of the cavity feature line to obtain the cavity update pixel. The distance between the cavity update pixel and the cavity feature pixel is the update pixel distance. A parallel circle line perpendicular to the long axis of the nasal cavity is drawn through the cavity update pixel to obtain the cavity update circle line. The image pixels covered by the cavity feature circle line are acquired to obtain multiple circle line covered pixels. If there are pixels not located in the nasal cavity region among the acquired circle line covered pixels, the cavity feature circle line is set as the largest cavity circle line corresponding to the sample endoscopic cropped image. If all the pixels covered by the acquired circle line are located in the nasal cavity region, the above process is repeated until there are pixels not located in the nasal cavity region among the acquired circle line covered pixels.

[0086] If any of the pixels covered by the acquired circle line are not located in the nasal cavity region, a new cavity feature pixel is selected in the direction of the far end extension of the cavity feature line to obtain the cavity update pixel. The distance between the cavity update pixel and the cavity feature pixel is the update pixel distance. A parallel circle line perpendicular to the long axis of the nasal cavity is drawn through the cavity update pixel to obtain the cavity update circle line. The image pixels covered by the cavity feature circle line are acquired to obtain multiple circle line covered pixels. If all the acquired circle line covered pixels are located in the nasal cavity region, the cavity feature circle line is set as the largest cavity circle line corresponding to the sample endoscopic cropped image. If any of the acquired circle line covered pixels are not located in the nasal cavity region, the above process is repeated until all the acquired circle line covered pixels are located in the nasal cavity region.

[0087] It should be noted here that:

[0088] In this application, the updated pixel distance is specifically the coverage distance of 10 pixels in the cavity feature line;

[0089] In this application, parallel concentric circles perpendicular to the long axis of the nasal cavity are set in the nasal endoscopy image, and the nasal cavity region in the nasal endoscopy image is identified by the complete coverage state of the pixels of the concentric circles. Based on the identification results, the cross-sectional area of ​​the cavity at different endoscopic depths is obtained, and historical patients are screened for nasal cavity similarity analysis. This approach has the following advantages:

[0090] In the nasal endoscopy image analysis process, a parallel circle perpendicular to the long axis of the nasal cavity is first established. The complete coverage of its pixels is then used to accurately identify the nasal cavity region. This method is unaffected by the complex shape of the nasal cavity, clearly delineates the nasal cavity from surrounding tissues, effectively filters image noise, and ensures accurate identification. Based on accurate nasal cavity region identification, the cross-sectional area of ​​the cavity at different endoscopic depths is obtained, quantifying the structural features of the nasal cavity. This helps doctors intuitively understand changes in the size and shape of the nasal cavity, providing strong evidence for disease diagnosis and dynamically tracking disease progression and treatment effectiveness.

[0091] Subsequently, by screening historical patients and conducting nasal cavity similarity analysis, cases with nasal cavity conditions highly similar to those of the target patient were identified. Using the historical endoscopic initial focal length and initial brightness data of these similar patients, the brightness and focal length of the endoscope for the target patient were initialized and adjusted. In this way, the endoscope can quickly reach the parameter state suitable for the nasal cavity examination of the target patient, reducing adjustment time and trial and error costs, improving examination efficiency and accuracy, and laying a good foundation for subsequent accurate diagnosis and treatment.

[0092] The image region marked by the maximum cavity circle line in the sample endoscope image is obtained to obtain the sample cavity spatial cross-sectional region. The area value of the sample cavity spatial cross-sectional region is obtained to obtain the cavity cross-sectional area value corresponding to the sample endoscope image.

[0093] The historical video stream coverage period is traversed using the motion screenshot time points, and the cavity cross-sectional area value of the endoscope image corresponding to each time point is obtained. The obtained cavity cross-sectional area values ​​are set as cavity cross-sectional area from M1 to Ma in the order of the corresponding acquisition time points.

[0094] It should be noted here that:

[0095] In this application, M1, M2, M3...Ma in the cavity cross-sectional area M1 to cavity cross-sectional area Ma are the marking symbols corresponding to the cavity cross-sectional area values.

[0096] The endoscopic depth of the endoscope lens when the cross-sectional area of ​​the cavity is M1 is obtained, and the endoscopic depth of the M1 lens is obtained. Similarly, the endoscopic depth of the endoscope lens when the cross-sectional area of ​​the cavity is M2 is obtained, and the endoscopic depth of the M2 lens is obtained.

[0097] Set the cross-sectional area of ​​the M1 cavity to the cross-sectional area of ​​the Ma cavity and the endoscopic depth of the M1 lens to the endoscopic depth of the Ma lens as the target endoscopic video analysis data;

[0098] The clinical symptoms of nasal diseases observed in the target endoscopic patients were obtained, resulting in multiple target nasal symptoms;

[0099] It should be noted here that:

[0100] In this application, the clinical symptoms of nasal diseases referred to herein include, but are not limited to, nasal congestion, increased nasal discharge, and nasal bleeding.

[0101] The history of patients who have completed nasal endoscopy is obtained, resulting in multiple historical endoscopic examination patients. One sample endoscopic examination patient is randomly selected from the multiple historical endoscopic examination patients.

[0102] Acquire the nasal clinical symptoms of patients undergoing endoscopic examination, compare the acquired nasal clinical symptoms with the target nasal symptoms for symptom consistency, set the nasal clinical symptoms with consistent symptom consistency as consistent symptoms, and calculate the ratio of the number of consistent symptoms to the number of target nasal symptoms to obtain the clinical symptom matching degree corresponding to the endoscopic examination of the sample.

[0103] Repeat the process of obtaining the clinical symptom matching degree corresponding to the sample endoscopy examination, obtain the clinical symptom matching degree corresponding to each historical endoscopy examination patient, and set a preset range of symptom matching degree. If the clinical symptom matching degree is within the preset range of symptom matching degree, the corresponding historical endoscopy examination patient is classified as a disease-matched patient. If the clinical symptom matching degree is not within the preset range of symptom matching degree, the corresponding historical endoscopy examination patient is classified as a non-disease-matched patient.

[0104] It should be noted here that:

[0105] The historical matching records of endoscopy are obtained. For multiple patients with historical diseases obtained from the historical matching records of endoscopy, the clinical symptom matching degree corresponding to each patient with historical diseases is obtained. The clinical symptom matching degree with the largest value is set as the upper limit of the preset range of symptom matching degree, and the clinical symptom matching degree with the smallest value is set as the lower limit of the preset range of symptom matching degree, so as to obtain the preset range of symptom matching degree.

[0106] The endoscopic video stream corresponding to each patient with a matching symptom is acquired to obtain multiple endoscopic video streams for matching patients.

[0107] It should be noted here that:

[0108] In this application, the duration of the endoscope working video stream is the same as that of the historical endoscope video stream.

[0109] Repeat the process of image analysis on historical endoscopic video streams to obtain endoscopic video analysis data corresponding to each segment of the patient's endoscopic video stream;

[0110] The endoscopic video analysis data corresponding to each segment of the matched patient endoscopic video stream and the target endoscopic video analysis data are defined as the initial matching data for the endoscopic patient.

[0111] The data analysis module performs cross-sectional area analysis and lens endoscopic depth analysis on the endoscopic video stream based on the initial matching data of endoscopic patients. Based on the analysis results, it obtains the endoscopic cavity similarity corresponding to the endoscopic video stream of the matched patients. Based on the endoscopic cavity similarity, it classifies the endoscopic video stream of the matched patients into types and obtains video stream matching data.

[0112] Specifically as follows:

[0113] Acquire initial matching data for endoscopic patients, and based on the initial matching data for endoscopic patients, acquire target endoscopic video analysis data and endoscopic video analysis data corresponding to each segment of matched patient endoscopic video stream;

[0114] Based on the target endoscopic video analysis data, the cross-sectional area of ​​cavity M1 to cavity Ma and the endoscopic depth of lens M1 to lens Ma are obtained respectively. In the Cartesian coordinate system, the coordinate point with the endoscopic depth of lens M1 and the cross-sectional area of ​​cavity M1 is set as the target coordinate point H1. The coordinate point with the endoscopic depth of lens M2 and the cross-sectional area of ​​cavity M2 is set as the target coordinate point H2. And so on, the coordinate point with the endoscopic depth of lens Ma and the cross-sectional area of ​​cavity Ma is set as the target coordinate point Ha.

[0115] It should be noted here that:

[0116] In this application, H1, H2, H3...Ha in the target coordinates from H1 to Ha are the marking symbols corresponding to the target coordinates.

[0117] Connect the target coordinates H1 to Ha sequentially to obtain the target endoscopic cross-section change polyline. Set the Cartesian coordinate system where the target endoscopic cross-section change polyline is located as the endoscopic cross-section coordinate system.

[0118] In the acquired multi-segment matched patient endoscopic video streams, arbitrarily select a sample matched video stream, obtain the endoscopic cross-sectional change polyline corresponding to the sample matched video stream based on the endoscopic video analysis data, and mark it in the endoscopic cross-sectional coordinate system to obtain the sample endoscopic cross-sectional change polyline;

[0119] Geometric feature analysis was performed on the sample endoscope cross-section polygon and the target endoscope cross-section change polygon, and the endoscope cavity similarity corresponding to the sample matching video stream was obtained based on the analysis results;

[0120] Specifically as follows:

[0121] The abscissa values ​​of the left edge coordinate points corresponding to the broken line of the sample endoscopic section are obtained to obtain the first left edge abscissa value. The abscissa values ​​of the left edge coordinate points corresponding to the broken line of the target endoscopic section are obtained to obtain the second left edge abscissa value. If the first left edge abscissa value is less than or equal to the second left edge abscissa value, the left edge coordinate points corresponding to the broken line of the sample endoscopic section are set as the first edge feature points. If the first left edge abscissa value is greater than the second left edge abscissa value, the left edge coordinate points corresponding to the broken line of the target endoscopic section are set as the first edge feature points.

[0122] The abscissa values ​​of the right edge coordinate points corresponding to the polyline of the sample endoscopic section are obtained to obtain the first right edge abscissa value. The abscissa values ​​of the right edge coordinate points corresponding to the polyline of the target endoscopic section are obtained to obtain the second right edge abscissa value. If the first right edge abscissa value is less than or equal to the second right edge abscissa value, the left edge coordinate point corresponding to the polyline of the target endoscopic section is set as the second edge feature point. If the first right edge abscissa value is greater than the second right edge abscissa value, the left edge coordinate point corresponding to the polyline of the sample endoscopic section is set as the second edge feature point.

[0123] In the endoscopic cross-section coordinate system, a straight line perpendicular to the x-axis is drawn through the first edge feature point to obtain the first edge feature line. A straight line perpendicular to the x-axis is drawn through the second edge feature point to obtain the second edge feature line. The closed area enclosed by the first edge feature line, the second edge feature line, the sample endoscopic cross-section change line, and the target endoscopic cross-section change line is obtained to obtain the change line deviation area.

[0124] The area of ​​the variable polyline deviation region is obtained, and the area value of the polyline deviation region is obtained. The abscissa deviation between the second edge feature point and the first edge feature point is calculated to obtain the length value of the polyline deviation time period. The average ordinate of the variable polyline of the target endoscopic section is calculated to obtain the average longitudinal ordinate of the target polyline.

[0125] The similarity of the endoscopic cavity corresponding to the sample endoscopic section is obtained by calculating the area value of the broken line deviation region, the length value of the broken line deviation time period, and the longitudinal average value of the target broken line.

[0126] The similarity of the endoscopic cavities corresponding to the sample-matched video streams is calculated using the following formula:

[0127] ;

[0128] Where Jmp is the similarity of the endoscopic cavity corresponding to the sample matching video stream, Szp is the area of ​​the polygonal deviation region, Tzp is the length of the polygonal deviation time period, and Zpz is the longitudinal average value of the target polygonal line.

[0129] Repeat the process of obtaining the endoscopic cavity similarity corresponding to the sample matching video stream, and obtain the endoscopic cavity similarity corresponding to each segment of the patient's endoscopic video stream;

[0130] Set an endoscope cavity similarity matching interval. If the endoscope cavity similarity is within the endoscope cavity similarity matching interval, then set the corresponding matched patient endoscope video stream as a matched video stream. If the endoscope cavity similarity is not within the endoscope cavity similarity matching interval, then set the corresponding matched patient endoscope video stream as a non-matched video stream to obtain video stream matching data.

[0131] It should be noted here that:

[0132] The historical matching video streams in the historical matching records of endoscopes are obtained. The similarity of the endoscope cavity corresponding to each historical matching video stream is obtained. The numerical values ​​of the obtained multiple endoscope cavity similarities are compared. The endoscope cavity similarity with the largest value is set as the upper limit of the endoscope similarity matching interval, and the endoscope cavity similarity with the smallest value is set as the lower limit of the endoscope similarity matching interval, thus obtaining the endoscope similarity matching interval.

[0133] The image transmission module performs endoscopic near-field triggering settings for the target endoscopic patient and wireless transmission of endoscopic images based on video stream matching data.

[0134] Specifically as follows:

[0135] Acquire video stream matching data, acquire matching video streams based on the video stream matching data, and mark the endoscope used to examine the target endoscopic patient as the target endoscope;

[0136] Obtain the initial adjustment focal length corresponding to each matching video stream, and calculate the average of the multiple initial adjustment focal lengths to obtain the initial endoscopic focal length. Obtain the initial endoscopic adjustment brightness corresponding to each matching video stream, and calculate the average of the multiple initial endoscopic adjustment brightnesses to obtain the initial endoscopic brightness.

[0137] It should be noted here that:

[0138] In this application, the initial focus adjustment referred to herein specifically refers to the endoscope focus after the endoscope operator performs the initial focus adjustment in a matched video stream, and the initial brightness adjustment referred to herein specifically refers to the endoscope brightness after the endoscope operator performs the initial brightness adjustment in a matched video stream.

[0139] The initial endoscopic focus and initial endoscopic brightness are synchronized to the NFC near-field trigger terminal. The initial endoscopic focus and initial endoscopic brightness settings of the target endoscope are completed by directly connecting the NFC terminal built into the target endoscope to the NFC near-field trigger terminal.

[0140] The specific setup process is as follows:

[0141] During the initial parameter configuration of the target endoscope, the initial endoscopic focal length and brightness values ​​are simultaneously transmitted to a trigger terminal device with NFC near-field communication functionality. This ensures that the terminal stores accurate and configurable parameter information. The operator then brings the NFC communication module of the target endoscope into close proximity with the NFC near-field trigger terminal, which stores the initial parameters. Through the near-field sensing and data interaction mechanism unique to NFC technology, a wireless direct communication link is established the moment they touch. In this direct communication state, the NFC near-field trigger terminal automatically transmits the stored initial endoscopic focal length and brightness setting commands to the target endoscope's internal control system. Upon receiving these setting commands, the target endoscope's internal adjustment module automatically operates the focal length and brightness adjustment units according to the command requirements, thereby completing the initial endoscopic focal length and brightness settings, enabling the target endoscope to be used with the preset appropriate parameters.

[0142] Once the setup is complete, the target endoscope acquires real-time endoscopic images and transmits them wirelessly.

[0143] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A wireless image transmission system for ENT endoscopes based on NFC near-field triggering, characterized in that, include: Data acquisition module: acquires the historical endoscopic video stream corresponding to the target endoscopic patient and performs frame-by-frame image capture. Combines the endoscopic depth with cavity cross-sectional area analysis of the captured images to obtain target endoscopic video analysis data. Based on the clinical symptoms of the target endoscopic patient, it performs symptom matching patient screening and obtains endoscopic video analysis data corresponding to the symptom matching patients. The endoscopic video analysis data and the target endoscopic video analysis data are defined as the initial matching data for endoscopic patients. Data Analysis Module: Based on the initial matching data of endoscopic patients, the module performs cross-sectional area analysis and lens endoscopic depth analysis on the endoscopic video stream. Based on the analysis results, it obtains the endoscopic cavity similarity corresponding to the endoscopic video stream of the matched patients. Based on the endoscopic cavity similarity, the module classifies the endoscopic video stream of the matched patients into different types to obtain video stream matching data. Image transmission module: Sets up near-field triggering for the endoscope and wirelessly transmits endoscopic images to the target endoscopic patient based on video stream matching data; The specific settings for endoscope near-field triggering and wireless image transmission are as follows: Acquire video stream matching data, acquire matching video streams based on the video stream matching data, and mark the endoscope used to examine the target endoscopic patient as the target endoscope; Obtain the initial adjustment focal length corresponding to each matching video stream, calculate the average of the multiple initial adjustment focal lengths to obtain the initial endoscopic focal length, obtain the initial endoscopic adjustment brightness corresponding to each matching video stream, and calculate the average of the multiple initial endoscopic adjustment brightnesses to obtain the initial endoscopic brightness. The initial endoscopic focus and initial endoscopic brightness are synchronized to the NFC near-field trigger terminal. The initial endoscopic focus and initial endoscopic brightness of the target endoscope are set by directly connecting the NFC terminal built into the target endoscope to the NFC near-field trigger terminal. Once the setup is complete, the target endoscope acquires real-time endoscopic images and transmits them wirelessly.

2. The NFC-based near-field triggered wireless image transmission system for ENT endoscopes according to claim 1, characterized in that, The initial matching data for endoscopic patients was obtained as follows: Acquire patients for endoscopic examinations, and randomly select a target endoscopic patient from among them; Video streams of historical endoscopic examinations corresponding to the target endoscopic patient are acquired to obtain historical endoscopic video streams. Image analysis is performed on the historical endoscopic video streams, and target endoscopic video analysis data is obtained based on the analysis results. The clinical symptoms of nasal diseases in the target endoscopic patients were obtained, resulting in multiple target nasal symptoms. The target nasal symptoms were compared and analyzed with those of historical patients, and multiple patients matching the symptoms were obtained based on the analysis results. For each patient matched with a symptom, the corresponding endoscopic video stream is acquired, resulting in multiple endoscopic video streams for matched patients. The endoscopic video analysis data corresponding to each segment of the matched patient's endoscopic video stream is then obtained.

3. The NFC-based near-field triggered wireless image transmission system for ENT endoscopes according to claim 2, characterized in that, The data obtained from the target endoscopic video analysis is as follows: The historical endoscopic video stream is captured within a time period covered by the historical video stream. A motion screenshot time point is set within the historical video stream coverage period to capture images from the historical endoscopic video stream, resulting in a sample endoscopic image. Perform cavity region circumferential analysis on the endoscopic images of the sample, and obtain the cross-sectional area value of the cavity corresponding to the endoscopic images of the sample based on the analysis results; By using motion capture time points to traverse the historical video stream coverage period, the cross-sectional area value of the cavity in the endoscope image captured at each time point is obtained. The cross-sectional area value of the cavity is then obtained according to the endoscopic depth at the corresponding acquisition time point. Multiple lens endoscopic depths are used to obtain target endoscopic video analysis data.

4. The NFC-based near-field triggered wireless image transmission system for ENT endoscopes according to claim 3, characterized in that, The cavity region was analyzed by circumferential analysis of the endoscopic images of the sample, as follows: The nasal cavity region in the endoscopic image is marked, and a pixel is randomly selected in the marked nasal cavity region to obtain the cavity feature pixel. A parallel circle line perpendicular to the long axis of the nasal cavity is drawn through the cavity feature pixel to obtain the cavity feature circle line. Draw a straight line perpendicular to the long axis of the nasal cavity through the feature pixel to obtain the cavity feature line, and obtain the distal extension direction and proximal extension direction for the cavity feature line respectively. The image pixels covered by the cavity feature circles are obtained, resulting in multiple circle-covered pixels.

5. The NFC-based near-field triggered wireless image transmission system for ENT endoscopes according to claim 4, characterized in that, The cavity region was analyzed by circumferential analysis of the endoscopic images of the sample, as detailed below: If all the pixels covered by the obtained circle are in the nasal cavity area, proceed to step 1; if there are pixels covered by the obtained circle that are not in the nasal cavity area, proceed to step 3. Step 1: Select a new cavity feature pixel in the proximal extension direction of the cavity feature line to obtain the cavity update pixel. Draw a parallel circle line perpendicular to the long axis of the nasal cavity through the cavity update pixel to obtain the cavity update circle line. Obtain the image pixels covered by the cavity feature circle line to obtain multiple circle line covered pixels. If there are pixels in the circle line covered pixels that are not in the nasal cavity area, set the cavity feature circle line as the largest cavity circle line corresponding to the sample endoscopic cropped image. Step 2: If all the pixels covered by the obtained circle are in the nasal cavity area, repeat Step 1 until there are pixels covered by the obtained circle that are not in the nasal cavity area, then proceed to Step 5. Step 3: Select a new cavity feature pixel in the direction of the far end of the cavity feature line to obtain the cavity update pixel. Draw a parallel circle line perpendicular to the long axis of the nasal cavity through the cavity update pixel to obtain the cavity update circle line. Obtain the image pixels covered by the cavity feature circle line to obtain multiple circle line covered pixels. If all the obtained circle line covered pixels are in the nasal cavity area, then set the cavity feature circle line as the largest cavity circle line corresponding to the sample endoscopic image. Step 4: If there are pixels covered by the obtained circle line that are not in the nasal cavity area, repeat step 3 until all pixels covered by the obtained circle line are in the nasal cavity area, then proceed to step 5. Step 5: Obtain the image region marked by the maximum cavity circle line in the sample endoscope image to obtain the sample cavity spatial cross-sectional region. Obtain the area value of the sample cavity spatial cross-sectional region to obtain the cavity cross-sectional area value corresponding to the sample endoscope image.

6. The NFC-based near-field triggered wireless image transmission system for otolaryngology endoscopes according to claim 1, characterized in that, The process of acquiring patients based on disease matching is as follows: The history of patients who have completed nasal endoscopy is obtained, resulting in multiple historical endoscopic examination patients. One sample endoscopic examination patient is randomly selected from the multiple historical endoscopic examination patients. Acquire the nasal clinical symptoms of patients undergoing endoscopic examination, compare the acquired nasal clinical symptoms with the target nasal symptoms for symptom consistency, set the nasal clinical symptoms with consistent symptom consistency as consistent symptoms, and calculate the ratio of the number of consistent symptoms to the number of target nasal symptoms to obtain the clinical symptom matching degree corresponding to the endoscopic examination of the sample. For each patient undergoing a historical endoscopic examination, the clinical symptom matching degree is obtained. A preset range for symptom matching degree is set. If the clinical symptom matching degree is within the preset range, the corresponding patient undergoing a historical endoscopic examination is classified as a patient with a matching symptom. If the clinical symptom matching degree is not within the preset range, the corresponding patient undergoing a historical endoscopic examination is classified as a patient without a matching symptom.

7. The NFC-based near-field triggered wireless image transmission system for otolaryngology endoscopes according to claim 6, characterized in that, The video stream matching data is obtained as follows: Acquire initial matching data for endoscopic patients, and based on the initial matching data for endoscopic patients, acquire target endoscopic video analysis data and endoscopic video analysis data corresponding to each segment of matched patient endoscopic video stream; Based on the target endoscopic video analysis data, the cavity cross-sectional area and lens endoscopic depth are obtained. The coordinates of the points with the lens endoscopic depth on the horizontal axis and the cavity cross-sectional area on the vertical axis are connected sequentially to obtain the target endoscopic cross-section change polygon. The plane rectangular coordinate system where the target endoscopic cross-section change polygon is located is set as the endoscopic cross-section coordinate system. In the acquired multi-segment matched patient endoscopic video streams, arbitrarily select a sample matched video stream, obtain the endoscopic cross-sectional change polyline corresponding to the sample matched video stream based on the endoscopic video analysis data, and mark it in the endoscopic cross-sectional coordinate system to obtain the sample endoscopic cross-sectional change polyline; Geometric feature analysis was performed on the sample endoscope cross-section polygon and the target endoscope cross-section change polygon. Based on the analysis results, the sample matching video stream and the endoscope cavity similarity corresponding to each segment of the matching patient endoscope video stream were obtained. Set an endoscope cavity similarity matching interval. If the endoscope cavity similarity is within the endoscope cavity similarity matching interval, then set the corresponding matched patient endoscope video stream as a matched video stream. If the endoscope cavity similarity is not within the endoscope cavity similarity matching interval, then set the corresponding matched patient endoscope video stream as a non-matched video stream, thus obtaining video stream matching data.

8. The NFC-based near-field triggered wireless image transmission system for ENT endoscopes according to claim 7, characterized in that, Geometric feature analysis was performed on the polygonal line representing the change in the endoscopic cross section, as detailed below: The abscissa values ​​of the left edge coordinate points corresponding to the broken line of the sample endoscopic cross section are obtained to obtain the first left edge abscissa value. The abscissa values ​​of the left edge coordinate points corresponding to the broken line of the target endoscopic cross section are obtained to obtain the second left edge abscissa value. If the first left edge abscissa value is less than or equal to the second left edge abscissa value, the left edge coordinate points corresponding to the broken line of the sample endoscopic cross section are set as the first edge feature points. If the first left edge abscissa value is greater than the second left edge abscissa value, the left edge coordinate points corresponding to the broken line of the target endoscopic cross section are set as the first edge feature points. The abscissa values ​​of the right edge coordinate points corresponding to the broken line of the sample endoscopic cross section are obtained to obtain the first right edge abscissa value. The abscissa values ​​of the right edge coordinate points corresponding to the broken line of the target endoscopic cross section are obtained to obtain the second right edge abscissa value. If the first right edge abscissa value is less than or equal to the second right edge abscissa value, the left edge coordinate point corresponding to the broken line of the target endoscopic cross section is set as the second edge feature point. If the first right edge abscissa value is greater than the second right edge abscissa value, the left edge coordinate point corresponding to the broken line of the sample endoscopic cross section is set as the second edge feature point.

9. The NFC-based near-field triggered wireless image transmission system for otolaryngology endoscopes according to claim 8, characterized in that, Geometric feature analysis was performed on the polygonal line representing the change in the endoscopic cross section, as detailed below: In the endoscopic cross-section coordinate system, a straight line perpendicular to the x-axis is drawn through the first edge feature point to obtain the first edge feature line. A straight line perpendicular to the x-axis is drawn through the second edge feature point to obtain the second edge feature line. The closed area enclosed by the first edge feature line, the second edge feature line, the sample endoscopic cross-section change line, and the target endoscopic cross-section change line is obtained to obtain the change line deviation area. The area of ​​the variable polyline deviation region is obtained, and the area value of the polyline deviation region is obtained. The abscissa deviation between the second edge feature point and the first edge feature point is calculated to obtain the length value of the polyline deviation time period. The average ordinate of the variable polyline of the target endoscopic section is calculated to obtain the average longitudinal ordinate of the target polyline. The similarity of the endoscopic cavity corresponding to the sample endoscopic cross section is obtained by calculating the area value of the broken line deviation region, the length value of the broken line deviation time period, and the longitudinal average value of the target broken line.

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