Hair transplanting effect evaluation method based on multi-modal data fusion

Abstract

The invention discloses a hair transplantation effect evaluation method based on multi-modal data fusion, and the method comprises the steps: detecting the survival condition of transplanted hair follicles of a to-be-detected patient, and judging the influence of the survival condition of the transplanted hair follicles on a hair transplantation effect, thereby obtaining a survival rate evaluation coefficient; detecting hair transplantation parameters of a to-be-detected patient, comparing the hair transplantation parameters with hair transplantation requirements of the to-be-detected patient to obtain a patient requirement integrating degree coefficient, judging the satisfaction degree of the to-be-detected patient for hair transplantation to obtain a patient satisfaction degree evaluation coefficient, and obtaining a patient self-evaluation coefficient based on the patient requirement integrating degree coefficient and the patient satisfaction degree evaluation coefficient; detecting the hair condition of the area connected with the hair transplanting area to obtain connection detection parameter information, judging the connection natural degree of the to-be-detected patient based on the connection detection parameter information, and judging the influence on the hair transplanting effect of the to-be-detected patient to obtain a connection natural degree evaluation coefficient. The hair transplanting effect evaluation method has the effect of improving the hair transplanting effect evaluation efficiency based on multi-modal data fusion.

Description

Technical Field

[0001] This invention relates to the field of data fusion technology, and in particular to a method for evaluating the effectiveness of hair transplantation based on multimodal data fusion. Background Technology

[0002] Currently, hair transplantation is a surgical procedure that transplants healthy hair into areas of thinning hair. With the increasing sophistication of cosmetic surgery techniques, hair transplantation has become an effective solution for many patients with thinning hair or baldness. The success of a hair transplant can be comprehensively assessed by evaluating follicle survival rate, naturalness, density, growth direction, and post-operative recovery. Combining multimodal data to evaluate the transplant effect provides a more comprehensive and accurate assessment. Therefore, multimodal data fusion-based evaluation of hair transplant results is crucial.

[0003] Existing methods for evaluating hair transplant results based on multimodal data fusion assess the effectiveness of the transplant by examining hair growth and any abnormalities within the transplanted area on the patient's head. However, these methods fail to consider whether the transplanted hair meets aesthetic standards or whether the transition is natural, leading to unnatural hair growth in many patients. Consequently, the evaluation efficiency is low and needs improvement. Summary of the Invention

[0004] To improve the efficiency of hair transplant effect evaluation based on multimodal data fusion, this invention provides a method for hair transplant effect evaluation based on multimodal data fusion.

[0005] This invention provides a method for evaluating the effectiveness of hair transplantation based on multimodal data fusion, employing the following technical solution:

[0006] A method for evaluating hair transplant effectiveness based on multimodal data fusion includes the following steps:

[0007] Step S1: Detect the survival rate of transplanted hair follicles in the patient to be tested, and determine the impact of the survival rate of transplanted hair follicles on the hair transplant effect based on the hair follicle survival rate information to obtain the survival rate evaluation coefficient.

[0008] Step S2: Detect the hair transplant parameters of the patient to be tested and compare them with the patient's hair transplant needs to obtain the patient's needs matching coefficient. Statistically judge the patient's satisfaction with the hair transplant to obtain the patient satisfaction evaluation coefficient. Based on the patient's needs matching coefficient and the patient satisfaction evaluation coefficient, obtain the patient's self-evaluation coefficient.

[0009] Step S3: Detect the hair condition in the area where the patient's head connects with the transplant area to obtain connection detection parameter information. Compare the connection detection parameter information with the hair transplant detection parameter information to determine the naturalness of the connection between the transplant area and the connecting area on the patient's head, and then determine the impact on the hair transplant effect of the patient to obtain the naturalness of connection evaluation coefficient.

[0010] Step S4: Based on the comprehensive survival rate assessment coefficient, patient self-assessment coefficient, and seamlessness assessment coefficient, the hair transplant effect of the patient to be tested is determined to obtain the hair transplant effect assessment coefficient, and a hair transplant effect assessment report is generated. The hair transplant effect assessment coefficient report is sent to the user terminal and the background monitoring system and stored.

[0011] Preferably, the camera is used to capture images of the patient's head around the head of the patient being tested.

[0012] Based on the image information of the patient's head, it is determined whether there is scab in the hair transplant area of ​​the patient's head. If there is scab, the scab area is detected, the hair transplant area of ​​the patient's head is obtained, and the ratio of the scab area to the hair transplant area of ​​the patient's head is calculated to obtain the scab area proportion information.

[0013] The percentage of scab area is compared with the preset scab percentage threshold to determine whether there is a large area of ​​scab in the hair transplant area of ​​the patient's head. If the percentage of scab area is greater than the preset scab percentage threshold, then there is a large area of ​​scab on the patient's head, and the first abnormal appearance result is output.

[0014] After receiving the first abnormal survival result, the difference between the scab area ratio information and the preset scab ratio threshold information is calculated to obtain the scab ratio difference value, and the first abnormal survival coefficient is obtained based on the scab ratio difference value.

[0015] Based on the image information of the patient's head, it is determined whether there is redness and swelling in the hair transplant area of ​​the patient's head. If there is redness and swelling, the area of ​​redness and swelling is detected, and the ratio of the redness and swelling area to the hair transplant area of ​​the patient's head is calculated to obtain the proportion of redness and swelling area.

[0016] The proportion of red and swollen oozing area is compared with the preset red and swollen oozing proportion threshold to determine whether there is abnormal red and swollen oozing in the hair transplant area of ​​the test patient's head. If the proportion of red and swollen oozing area is greater than the preset red and swollen oozing proportion threshold, then there is abnormal red and swollen oozing in the test patient's head, and the second appearance survival abnormal result is output.

[0017] After receiving the second appearance survival abnormality result, the difference between the redness and swelling exudate area ratio information and the preset redness and swelling exudate ratio threshold information is calculated to obtain the redness and swelling exudate ratio difference value. The second appearance survival abnormality coefficient is obtained based on the redness and swelling exudate ratio difference value.

[0018] The survival coefficient of appearance detection is obtained by analyzing the first appearance survival anomaly coefficient and the second appearance survival anomaly coefficient.

[0019] Preferably, the uniformity of hair thickness is obtained by acquiring and statistically analyzing the thickness of the transplanted hair in the hair transplant area of ​​the patient under test using a magnification device.

[0020] Based on the information on the uniformity of hair thickness in the transplanted area, the influence of the uniformity of hair thickness in the transplanted area on the survival of transplanted hair follicles in the head of the test patient is determined, and the uniformity of hair thickness survival coefficient is obtained.

[0021] Gently pull the hair in the transplant area of ​​the patient to be tested to test the stability of the hair in the transplant area and obtain the degree of hair transplant stability. Based on the degree of hair transplant stability, judge the survival of the transplanted hair follicles on the head of the patient to be tested and obtain the hair transplant stability survival coefficient.

[0022] Based on the survival coefficient of appearance inspection, the survival coefficient of uniform thickness, and the survival coefficient of hair transplant stability, the survival of transplanted hair follicles on the head of the test patient is judged to obtain hair follicle survival rate information.

[0023] Based on the hair follicle survival rate information, the survival rate of transplanted hair follicles is assessed to determine the impact of the survival rate on the hair transplant effect, resulting in a survival rate evaluation coefficient.

[0024] Preferably, the hair transplant needs information of the patients to be tested is obtained, and the hair transplant area on the head of the patients to be tested is regularly tested to obtain the hair transplant test parameters of the patients to be tested; the hair transplant test parameters include hairline parameters and hair density parameters.

[0025] The hair transplant test parameters of the patient to be tested are compared with the hair transplant needs of the patient to be tested to obtain the hair transplant needs comparison results. Based on the hair transplant needs comparison results, the degree of fit between the hair transplant situation of the patient to be tested and the actual needs of the patient to be tested is judged to obtain the patient needs fit coefficient.

[0026] A characteristic questionnaire was developed based on the basic personal information of the patients to be tested. The characteristic questionnaire included the patients' satisfaction with various hair transplant parameters and their suggestions.

[0027] The characteristic questionnaire is sent to the user terminal of the patient to be tested on a regular basis for the patient to fill out. After the patient has completed the questionnaire, the characteristic questionnaire is stored and recorded.

[0028] The satisfaction scores of each hair transplant parameter in the feature questionnaire were averaged to obtain the satisfaction coefficient of the first type of patients.

[0029] Based on the patient suggestions in the feature questionnaire, the tone and emotion bias of the test patient is identified to obtain the patient tone bias value. Based on the patient tone bias value, the satisfaction level of the test patient with hair transplantation is judged to obtain the second type of patient satisfaction coefficient.

[0030] The patient satisfaction evaluation coefficient is obtained by superimposing the satisfaction coefficients of the first type of patients and the second type of patients.

[0031] The patient self-assessment coefficient is obtained by superimposing the patient needs matching coefficient and the patient satisfaction assessment coefficient.

[0032] Preferably, the hair transplant detection parameter information also includes information on the direction of hair transplantation and the layer information of hair transplantation;

[0033] The area where the patient's head connects to the hair transplant area is marked as the connection zone. The hair condition in the connection zone is detected to obtain connection detection parameter information, which includes connection density parameter information, connection hair direction information, and connection hair layer information.

[0034] The connection detection parameter information is compared with the hair transplant detection parameter information periodically to obtain the hair transplant connection comparison result. Based on the hair transplant connection comparison result, the degree of naturalness of the connection between the hair transplant area and the connection area on the head of the test patient is judged to obtain the hair transplant connection naturalness information of the test patient.

[0035] Based on the naturalness of hair transplant integration information of the test patients, the impact of the naturalness of integration on the hair transplant effect is determined, and the naturalness of integration evaluation coefficient is obtained.

[0036] Preferably, a sample parameter database is obtained, which includes threshold values ​​for the difference in density between different regions of the head of a sample user with normal hair, threshold values ​​for the difference in direction of connecting hair, and threshold values ​​for the difference in layer of connecting hair.

[0037] The difference between the connection density parameter information and the hair transplant density parameter information is calculated to obtain the connection density difference value;

[0038] Based on the sample parameter database, the threshold range of the difference in connection density between the connection area and the hair transplant area of ​​the patient under test is obtained and marked as the standard range of connection density difference.

[0039] Calculate the difference between the bonding density and the standard range of bonding density. If the difference between the bonding density and the standard range of bonding density is not within the standard range of bonding density, calculate the minimum difference between the difference between the bonding density and the standard range of bonding density to obtain the first bonding naturalness coefficient. If the difference between the bonding density and the standard range of bonding density is within the standard range of bonding density, output the first bonding naturalness coefficient.

[0040] The difference between the direction information of the connecting hair and the direction parameter information of the transplanted hair is calculated to obtain the deviation value of the connecting hair direction.

[0041] Based on the sample parameter database, the threshold range of the difference in the direction of the connecting hair between the location of the connecting area and the location of the hair transplant area of ​​the patient under test is obtained and marked as the standard range of the difference in the direction of the connecting hair.

[0042] Calculate the deviation value of the connecting hair direction from the standard range of the connecting hair direction. If the deviation value of the connecting hair direction is within the standard range, calculate the minimum difference between the deviation value of the connecting hair direction and the standard range of the connecting hair direction to obtain the second connecting naturalness coefficient. If the deviation value of the connecting hair direction is within the standard range, output the second connecting naturalness coefficient.

[0043] Preferably, the difference between the hair layer information and the hair layer parameter information of the transplanted hair is calculated to obtain the hair layer deviation value.

[0044] Based on the sample parameter database, the threshold range of the difference in hair layer between the connecting area and the hair transplant area of ​​the patient under test is obtained and marked as the standard range of the difference in hair layer.

[0045] Calculate the deviation value of the connecting hair layer from the standard range of the connecting hair layer. If the deviation value of the connecting hair layer is within the standard range, calculate the minimum difference between the deviation value of the connecting hair layer and the standard range of the connecting hair layer to obtain the second connecting naturalness coefficient. If the deviation value of the connecting hair layer is within the standard range, output the third connecting naturalness coefficient.

[0046] The hair transplant naturalness information of the test patient is obtained by superimposing the first naturalness coefficient, the second naturalness coefficient, and the third naturalness coefficient.

[0047] Preferably, the survival rate assessment coefficient, the patient's self-assessment coefficient, and the naturalness of the transition assessment coefficient are superimposed and calculated to determine the hair transplant effect of the patient to be tested and obtain the hair transplant effect assessment coefficient.

[0048] Based on the survival rate assessment coefficient, patient self-assessment coefficient, naturalness of integration assessment coefficient, and hair transplant effect assessment coefficient, a hair transplant effect assessment report is generated for the patient to be tested.

[0049] Acquire a wireless communication module and establish a signal connection link between the wireless communication module and the patient to be tested;

[0050] The wireless communication module is used to send the hair transplant effect evaluation report of the patient to be tested to the user terminal and the background monitoring system.

[0051] The hair transplant effect evaluation report of the patients to be tested will be uploaded to the blockchain system for storage.

[0052] In summary, the present invention has at least one of the following beneficial technical effects:

[0053] The survival rate of transplanted hair follicles in the test patients was detected, and the impact of the survival rate on the hair transplant effect was determined to obtain a survival rate evaluation coefficient. The hair transplant parameters of the test patients were detected and compared with the hair transplant needs of the test patients to obtain a patient demand fit coefficient. The patient satisfaction with the hair transplant was statistically assessed to obtain a patient satisfaction evaluation coefficient. Based on the patient demand fit coefficient and the patient satisfaction evaluation coefficient, a patient self-evaluation coefficient was obtained. The connection of the hair in the test patients was detected to obtain connection detection parameter information. The connection detection parameter information was compared with the hair transplant detection parameter information to determine the naturalness of the connection between the hair in the transplant area and the connection area of ​​the test patients' heads, and then the impact on the hair transplant effect was determined to obtain a connection naturalness evaluation coefficient. The three aspects of the survival rate evaluation coefficient, patient self-evaluation coefficient, and connection naturalness evaluation coefficient were analyzed and evaluated, and a hair transplant effect evaluation report was generated, which improved the efficiency of hair transplant effect evaluation based on multimodal data fusion. Attached Figure Description

[0054] Figure 1 This embodiment is a flowchart illustrating the hair transplant effect evaluation method based on multimodal data fusion. Detailed Implementation

[0055] The present invention will be further described in detail below with reference to the accompanying drawings.

[0056] This invention discloses a method for evaluating the effectiveness of hair transplantation based on multimodal data fusion.

[0057] A method for evaluating hair transplant effectiveness based on multimodal data fusion includes the following steps:

[0058] Reference Figure 1 Step S1 involves detecting the survival rate of transplanted hair follicles in the patient to be tested. Based on the hair follicle survival rate information, the impact of transplanted hair follicle survival on the hair transplant effect is determined, and a survival rate evaluation coefficient is obtained. Step S1 specifically includes the following sub-steps:

[0059] Step A1: Obtain the camera and capture images of the patient's head around the camera to obtain image information of the patient's head.

[0060] Step A2: Based on the image information of the patient's head, determine whether there is scab formation in the hair transplant area of ​​the patient's head. If scab formation is present, detect the scab area, obtain the hair transplant area of ​​the patient's head, and calculate the ratio of the scab area to the hair transplant area of ​​the patient's head to obtain the scab area proportion information.

[0061] Step A3: Compare the scab area percentage information with the preset scab percentage threshold information to determine whether there is a large area of ​​scabs in the hair transplant area of ​​the test patient's head. If the scab area percentage information is greater than the preset scab percentage threshold information, then there is a large area of ​​scabs on the test patient's head, and the first abnormal appearance survival result is output.

[0062] Step A4: Upon receiving the first abnormal survival result in appearance, calculate the difference between the scab area percentage information and the preset scab percentage threshold information to obtain the scab percentage difference value. Based on the scab percentage difference value, obtain the first abnormal survival coefficient in appearance. The larger the scab percentage difference value, the higher the first abnormal survival coefficient in appearance.

[0063] Step A5: Based on the image information of the patient's head, determine whether there is redness and swelling with oozing in the hair transplant area of ​​the patient's head. If there is redness and swelling with oozing, detect the area of ​​redness and swelling with oozing and calculate the ratio of the area of ​​redness and swelling with oozing to the area of ​​hair transplantation on the patient's head to obtain the proportion of the redness and swelling with oozing area.

[0064] Step A6: Compare the percentage information of the red and swollen exudate area with the preset red and swollen exudate percentage threshold information to determine whether there is abnormal red and swollen exudate in the hair transplant area of ​​the test patient's head. If the percentage information of the red and swollen exudate area is greater than the preset red and swollen exudate percentage threshold information, then there is abnormal red and swollen exudate in the test patient's head, and the second appearance survival abnormal result is output.

[0065] Step A7: Upon receiving the second appearance survival anomaly result, calculate the difference between the redness and swelling exudate area percentage information and the preset redness and swelling exudate percentage threshold information to obtain the redness and swelling exudate percentage difference value. Based on the redness and swelling exudate percentage difference value, obtain the second appearance survival anomaly coefficient. The larger the redness and swelling exudate percentage difference value, the higher the second appearance survival anomaly coefficient.

[0066] Step A8: The survival coefficient for appearance inspection is obtained based on the analysis of the first and second appearance survival anomaly coefficients. Specifically, a higher first appearance survival anomaly coefficient results in a lower appearance inspection survival coefficient, and vice versa.

[0067] Step S1 also includes the following sub-steps:

[0068] Step B1: Obtain and statistically analyze the thickness uniformity of the transplanted hair in the hair transplant area of ​​the patient under test using a magnification device to obtain hair transplant thickness uniformity information.

[0069] Step B2 involves determining the impact of the uniformity of hair thickness in the transplanted area on the survival of transplanted hair follicles in the patient's scalp, based on the hair thickness uniformity information, to obtain the hair thickness uniformity survival coefficient. A lower hair thickness uniformity information value results in a lower hair thickness uniformity survival coefficient.

[0070] Step B3: Gently pull the hair in the transplant area of ​​the patient to test the stability of the hair and obtain the hair transplant stability degree. Based on the hair transplant stability degree, determine the survival rate of the transplanted hair follicles on the patient's head and obtain the hair transplant stability survival coefficient. The higher the hair transplant stability degree, the higher the hair transplant stability survival coefficient.

[0071] Step B4: Based on the appearance detection survival coefficient, thickness uniformity survival coefficient, and hair transplant stability survival coefficient, the survival rate of transplanted hair follicles on the patient's head is determined to obtain hair follicle survival rate information. Specifically, a higher appearance detection survival coefficient, a higher thickness uniformity survival coefficient, and a higher hair transplant stability survival coefficient all indicate a higher hair follicle survival rate.

[0072] Step B5 involves determining the impact of transplanted hair follicle survival rate on the hair transplant outcome based on the hair follicle survival rate information, thus obtaining a survival rate evaluation coefficient. A higher hair follicle survival rate results in a higher survival rate evaluation coefficient.

[0073] Reference Figure 1 Step S2 involves detecting the hair transplant parameters of the patient to be tested and comparing them with the patient's hair transplant needs to obtain a patient-needs fit coefficient. A characteristic questionnaire is then sent to the patient. Based on the characteristic questionnaire, the patient's satisfaction with the hair transplant is statistically assessed to obtain a patient satisfaction evaluation coefficient. Finally, based on the patient-needs fit coefficient and the patient satisfaction evaluation coefficient, a patient self-evaluation coefficient is obtained. Step S2 specifically includes the following sub-steps:

[0074] Step S21: Obtain the hair transplant needs information of the patient to be tested, including hairline requirements and hair density requirements. Regularly monitor the hair transplant area on the patient's head to obtain hair transplant testing parameters, including hairline parameters and hair density parameters.

[0075] Step S22: Compare the hair transplant test parameters of the patient to be tested with the hair transplant needs information of the patient to be tested to obtain the hair transplant needs comparison results. Based on the hair transplant needs comparison results, determine the degree of fit between the hair transplant situation of the patient to be tested and the actual needs of the patient to obtain the patient needs fit coefficient.

[0076] Specifically, the hairline parameter information is compared with the hairline requirement information. If the comparison is inconsistent, the degree of overlap between the hairline parameter information and the hairline requirement information is judged to obtain the hairline comparison matching coefficient. If the comparison is consistent, the hairline comparison matching coefficient with the highest value of 1 is output.

[0077] The hair transplant density parameter information is compared with the hair transplant density requirement information. If the comparison is inconsistent, the difference between the hair transplant density parameter information and the hair transplant density requirement information is calculated to obtain the hair transplant density difference value. Based on the hair transplant density difference value, the density comparison matching coefficient is obtained.

[0078] The patient's needs fit coefficient is obtained by superimposing the hairline matching coefficient and the density matching coefficient.

[0079] Step S23: Develop a characteristic questionnaire based on the patient's basic personal information. The characteristic questionnaire includes the patient's satisfaction with various hair transplant parameters and the patient's suggestions.

[0080] Step S24: The characteristic questionnaire is periodically sent to the user terminal of the patient to be tested for completion. After completion, the characteristic questionnaire is stored and recorded.

[0081] Step S25: The satisfaction scores of each hair transplant parameter in the feature questionnaire are averaged to obtain the satisfaction coefficient of the first type of patients.

[0082] Step S26: Based on the patient suggestions in the feature questionnaire, identify the tone and emotion bias of the test patient to obtain the patient tone bias value, and judge the test patient's satisfaction with hair transplantation based on the patient tone bias value to obtain the second type of patient satisfaction coefficient.

[0083] Step S27: The patient satisfaction evaluation coefficient is obtained by superimposing the satisfaction coefficients of the first type of patients and the second type of patients.

[0084] Step S28: The patient's self-assessment coefficient is obtained by superimposing the patient's needs matching coefficient and the patient's satisfaction assessment coefficient.

[0085] Reference Figure 1 Step S3 involves detecting the hair condition in the area where the patient's scalp meets the transplanted area to obtain connection detection parameters. These parameters are then compared with the hair transplant detection parameters to determine the naturalness of the connection between the transplanted area and the connecting area. This assessment determines the impact on the overall hair transplant outcome and yields a naturalness evaluation coefficient. Step S3 specifically includes the following sub-steps:

[0086] Step S31, the hair transplant detection parameter information also includes the direction information of the transplanted hair and the layer information of the transplanted hair.

[0087] Step S32: The area connecting the patient's head and the hair transplant area is marked as the connection area. The hair condition in the connection area is detected to obtain connection detection parameter information, which includes connection density parameter information, connection hair direction information, and connection hair layer information.

[0088] Step S33 involves periodically comparing the connection detection parameters with the hair transplant detection parameters to obtain the hair transplant connection comparison results. Based on these results, the degree of naturalness of the connection between the transplanted hair and the connection area on the patient's head is assessed, thus obtaining the naturalness information of the hair transplant connection for the patient. Step S33 specifically includes the following sub-steps:

[0089] Step C1: Obtain the sample parameter database. The sample parameter database includes the threshold range of the difference in density values ​​between different regions of the head of the sample user with normal hair, the threshold range of the difference in the direction of the connecting hair, and the threshold range of the difference in the layer of the connecting hair.

[0090] Step C1: Calculate the difference between the connection density parameter information and the hair transplant density parameter information to obtain the connection density difference value.

[0091] Step C2: Based on the sample parameter database, obtain the threshold range of the difference in connection density between the connection area and the hair transplant area of ​​the patient to be tested, and mark it as the standard range of the difference in connection density.

[0092] Step C3: Calculate the difference in bonding density between the standard range and the threshold density difference. If the difference in bonding density is not within the standard range, calculate the minimum difference between the difference in bonding density and the standard range to obtain the first bonding naturalness coefficient. The smaller the minimum difference between the difference in bonding density and the standard range, the higher the first bonding naturalness coefficient. If the difference in bonding density is within the standard range, output the first bonding naturalness coefficient. In this case, the first bonding naturalness coefficient is the highest, with a value of 1.

[0093] Step C4: Calculate the difference between the hair direction information of the connecting hair and the hair direction parameter information of the transplanted hair to obtain the hair direction deviation value of the connecting hair.

[0094] Step C5: Based on the sample parameter database, obtain the threshold range of the difference in the direction of the connecting hair between the location of the connecting area and the location of the hair transplant area of ​​the patient to be tested, and mark it as the standard range of the difference in the direction of the connecting hair.

[0095] Step C6: Calculate the standard range between the deviation value of the connecting hair direction and the standard range of the connecting hair direction. If the deviation value is within the standard range, calculate the minimum difference between the deviation value and the standard range to obtain the second naturalness coefficient. The smaller the minimum difference between the deviation value and the standard range, the higher the second naturalness coefficient. If the deviation value is within the standard range, output the second naturalness coefficient. In this case, the second naturalness coefficient is at its highest, set to 1.

[0096] Step S33 also includes the following sub-steps:

[0097] Step D1: Calculate the difference between the hair layer information and the hair layer parameter information of the transplanted hair to obtain the hair layer deviation value.

[0098] Step D2: Based on the sample parameter database, obtain the threshold range of the difference in hair layer between the connecting area and the hair transplant area of ​​the patient to be tested, and mark it as the standard range of the difference in hair layer.

[0099] Step D3: Calculate the deviation value of the connecting hair layer from the standard range of the connecting hair layer. If the deviation value is within the standard range, calculate the minimum difference between the deviation value and the standard range to obtain the second naturalness coefficient. The smaller the minimum difference, the higher the second naturalness coefficient. If the deviation value is within the standard range, output the third naturalness coefficient. In this case, the third naturalness coefficient is the highest, with a value of 1.

[0100] Step D4: The first, second, and third naturalness coefficients of hair transplantation are superimposed to calculate the naturalness information of the hair transplanted patient.

[0101] Step S34: Based on the naturalness information of the hair transplant connection of the test patient, determine the impact of the naturalness of the connection on the hair transplant effect and obtain the naturalness evaluation coefficient. The higher the naturalness information of the hair transplant connection of the test patient, the higher the naturalness evaluation coefficient.

[0102] Reference Figure 1 Step S4 involves combining the survival rate assessment coefficient, patient self-assessment coefficient, and naturalness of hair transplantation assessment coefficient to determine the hair transplant effect of the patient, obtaining a hair transplant effect assessment coefficient, and generating a hair transplant effect assessment report. This report is then sent to the user terminal and the backend monitoring system for storage. Step S4 specifically includes the following sub-steps:

[0103] Step S41: The survival rate assessment coefficient, the patient's self-assessment coefficient, and the naturalness of the transition assessment coefficient are superimposed and calculated to determine the hair transplant effect of the patient to be tested and obtain the hair transplant effect assessment coefficient.

[0104] Step S42: Based on the survival rate assessment coefficient, patient self-assessment coefficient, naturalness of connection assessment coefficient, and hair transplant effect assessment coefficient, generate a hair transplant effect assessment report for the patient to be tested.

[0105] Step S43: Obtain the wireless communication module and establish a signal connection link between the wireless communication module and the patient to be tested.

[0106] Step S44: Based on the wireless communication module, the hair transplant effect evaluation report of the patient to be tested is sent to the user terminal and the background monitoring system.

[0107] Step S45: Upload the hair transplant effect evaluation report of the patient to be tested to the blockchain system for storage.

[0108] The above are all preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made in accordance with the structure, shape and principle of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for evaluating the effectiveness of hair transplantation based on multimodal data fusion, characterized in that, Includes the following steps: Step S1: Detect the survival rate of transplanted hair follicles in the patient to be tested, and determine the impact of the survival rate of transplanted hair follicles on the hair transplant effect based on the hair follicle survival rate information to obtain the survival rate evaluation coefficient. Step S2: Detect the hair transplant parameters of the patient to be tested and compare them with the patient's hair transplant needs to obtain the patient's needs matching coefficient. Statistically judge the patient's satisfaction with the hair transplant to obtain the patient satisfaction evaluation coefficient. Based on the patient's needs matching coefficient and the patient satisfaction evaluation coefficient, obtain the patient's self-evaluation coefficient. Step S3: Detect the hair condition in the area where the hair transplant area connects to the head of the patient to obtain connection detection parameter information. Based on the connection detection parameter information, determine the degree of naturalness of the connection between the hair transplant area and the connection area of ​​the head of the patient to obtain the naturalness evaluation coefficient of the connection. Step S4: Based on the comprehensive survival rate assessment coefficient, patient self-assessment coefficient, and seamlessness assessment coefficient, the hair transplant effect of the patient to be tested is determined to obtain the hair transplant effect assessment coefficient, and a hair transplant effect assessment report is generated. The hair transplant effect assessment coefficient report is sent to the user terminal and the background monitoring system and stored.

2. The hair transplant effect evaluation method based on multimodal data fusion according to claim 1, characterized in that, Step S1 specifically includes: The camera is used to capture images of the patient's head around the head of the test subject. Based on the image information of the patient's head, it is determined whether there is scab in the hair transplant area of ​​the patient's head. If there is scab, the scab area is detected, the hair transplant area of ​​the patient's head is obtained, and the ratio of the scab area to the hair transplant area of ​​the patient's head is calculated to obtain the scab area proportion information. The percentage of scab area is compared with the preset scab percentage threshold to determine whether there is a large area of ​​scab in the hair transplant area of ​​the patient's head. If the percentage of scab area is greater than the preset scab percentage threshold, then there is a large area of ​​scab on the patient's head, and the first abnormal appearance result is output. After receiving the first abnormal survival result, the difference between the scab area ratio information and the preset scab ratio threshold information is calculated to obtain the scab ratio difference value, and the first abnormal survival coefficient is obtained based on the scab ratio difference value. Based on the image information of the patient's head, it is determined whether there is redness and swelling in the hair transplant area of ​​the patient's head. If there is redness and swelling, the area of ​​redness and swelling is detected, and the ratio of the redness and swelling area to the hair transplant area of ​​the patient's head is calculated to obtain the proportion of redness and swelling area. The proportion of red and swollen oozing area is compared with the preset red and swollen oozing proportion threshold to determine whether there is abnormal red and swollen oozing in the hair transplant area of ​​the test patient's head. If the proportion of red and swollen oozing area is greater than the preset red and swollen oozing proportion threshold, then there is abnormal red and swollen oozing in the test patient's head, and the second appearance survival abnormal result is output. After receiving the second appearance survival abnormality result, the difference between the redness and swelling exudate area ratio information and the preset redness and swelling exudate ratio threshold information is calculated to obtain the redness and swelling exudate ratio difference value. The second appearance survival abnormality coefficient is obtained based on the redness and swelling exudate ratio difference value. The survival coefficient of appearance detection is obtained by analyzing the first appearance survival anomaly coefficient and the second appearance survival anomaly coefficient.

3. The hair transplant effect evaluation method based on multimodal data fusion according to claim 2, characterized in that, Step S1 also includes: The hair transplant area of ​​the patient to be tested was acquired and observed using a magnifying device, and the uniformity of the thickness of the transplanted hair was statistically analyzed to obtain information on the uniformity of hair thickness. Based on the information on the uniformity of hair thickness in the transplanted area, the influence of the uniformity of hair thickness in the transplanted area on the survival of transplanted hair follicles on the head of the patient is determined, and the uniformity survival coefficient is obtained. Gently pull the hair in the hair transplant area of ​​the patient to be tested to test the stability of the hair in the hair transplant area and obtain the degree of hair transplant stability. Based on the degree of hair transplant stability, judge the survival of the transplanted hair follicles on the head of the patient to be tested and obtain the hair transplant stability survival coefficient. Based on the survival coefficient of appearance inspection, the survival coefficient of uniform thickness, and the survival coefficient of hair transplant stability, the survival status of transplanted hair follicles on the head of the test patient is judged, and the hair follicle survival rate information is obtained. Based on the hair follicle survival rate information, the survival rate of transplanted hair follicles is assessed to determine the impact of the survival rate on the hair transplant effect, resulting in a survival rate evaluation coefficient.

4. The hair transplant effect evaluation method based on multimodal data fusion according to claim 3, characterized in that, Step S2 specifically includes: The process involves obtaining hair transplant needs information from patients to be tested, and periodically monitoring the hair transplant area on the patient's head to obtain hair transplant testing parameters, including hairline parameters and hair density parameters. The hair transplant test parameters of the patient to be tested are compared with the hair transplant needs of the patient to be tested to obtain the hair transplant needs comparison results. Based on the hair transplant needs comparison results, the degree of fit between the hair transplant situation of the patient to be tested and the actual needs of the patient to be tested is judged to obtain the patient needs fit coefficient. A characteristic questionnaire was developed based on the basic personal information of the patients to be tested. The characteristic questionnaire included the patients' satisfaction with various hair transplant parameters and their suggestions. The characteristic questionnaire is sent to the user terminal of the patient to be tested on a regular basis for the patient to fill out. After the patient has completed the questionnaire, the characteristic questionnaire is stored and recorded. The satisfaction scores of each hair transplant parameter in the feature questionnaire were averaged to obtain the satisfaction coefficient of the first type of patients. Based on the patient suggestions in the feature questionnaire, the tone and emotion bias of the test patient is identified to obtain the patient tone bias value. Based on the patient tone bias value, the satisfaction level of the test patient with hair transplantation is judged to obtain the second type of patient satisfaction coefficient. The patient satisfaction evaluation coefficient is obtained by superimposing the satisfaction coefficients of the first type of patients and the second type of patients. The patient self-assessment coefficient is obtained by superimposing the patient needs matching coefficient and the patient satisfaction assessment coefficient.

5. The hair transplant effect evaluation method based on multimodal data fusion according to claim 4, characterized in that, Step S3 specifically includes: The hair transplant detection parameters also include information on the direction of hair transplantation and the layer of hair transplantation. The area where the patient's head connects to the hair transplant area is marked as the connection zone. The hair condition in the connection zone is detected to obtain connection detection parameter information, which includes connection density parameter information, connection hair direction information, and connection hair layer information. The connection detection parameter information is compared with the hair transplant detection parameter information periodically to obtain the hair transplant connection comparison result. Based on the hair transplant connection comparison result, the degree of naturalness of the connection between the hair transplant area and the connection area on the head of the test patient is judged to obtain the hair transplant connection naturalness information of the test patient. Based on the naturalness of hair transplant integration information of the test patients, the impact of the naturalness of integration on the hair transplant effect is determined, and the naturalness of integration evaluation coefficient is obtained.

6. The hair transplant effect evaluation method based on multimodal data fusion according to claim 5, characterized in that, The step of periodically comparing the connection detection parameter information with the hair transplant detection parameter information to obtain the hair transplant connection comparison result, and judging the naturalness of the connection between the hair transplant area and the connection area on the head of the test patient based on the hair transplant connection comparison result to obtain the naturalness information of the hair transplant connection of the test patient, specifically includes: Obtain a sample parameter database, which includes threshold values ​​for the difference in density between different regions of the head of a sample user with normal hair, threshold values ​​for the difference in direction of connecting hair, and threshold values ​​for the difference in layer of connecting hair. The difference between the connection density parameter information and the hair transplant density parameter information is calculated to obtain the connection density difference value; Based on the sample parameter database, the threshold range of the difference in connection density between the connection area and the hair transplant area of ​​the patient under test is obtained and marked as the standard range of connection density difference. Calculate the difference between the bonding density and the standard range of bonding density. If the difference between the bonding density and the standard range of bonding density is not within the standard range of bonding density, calculate the minimum difference between the difference between the bonding density and the standard range of bonding density to obtain the first bonding naturalness coefficient. If the difference between the bonding density and the standard range of bonding density is within the standard range of bonding density, output the first bonding naturalness coefficient. The difference between the direction information of the connecting hair and the direction parameter information of the transplanted hair is calculated to obtain the deviation value of the connecting hair direction. Based on the sample parameter database, the threshold range of the difference in the direction of the connecting hair between the location of the connecting area and the location of the hair transplant area of ​​the patient under test is obtained and marked as the standard range of the difference in the direction of the connecting hair. Calculate the deviation value of the connecting hair direction from the standard range of the connecting hair direction. If the deviation value of the connecting hair direction is within the standard range, calculate the minimum difference between the deviation value of the connecting hair direction and the standard range of the connecting hair direction to obtain the second connecting naturalness coefficient. If the deviation value of the connecting hair direction is within the standard range, output the second connecting naturalness coefficient.

7. The hair transplant effect evaluation method based on multimodal data fusion according to claim 6, characterized in that, The step of periodically comparing the connection detection parameter information with the hair transplant detection parameter information to obtain a hair transplant connection comparison result, and judging the naturalness of the connection between the hair transplant area and the connection area on the test patient's head based on the hair transplant connection comparison result to obtain the hair transplant connection naturalness information of the test patient, further includes: The difference between the hair layer information of the connecting hair layer and the hair layer parameter information of the transplanted hair layer is calculated to obtain the hair layer deviation value of the connecting hair layer. Based on the sample parameter database, the threshold range of the difference in hair layer between the connecting area and the hair transplant area of ​​the patient under test is obtained and marked as the standard range of the difference in hair layer. Calculate the deviation value of the connecting hair layer from the standard range of the connecting hair layer. If the deviation value of the connecting hair layer is within the standard range, calculate the minimum difference between the deviation value of the connecting hair layer and the standard range of the connecting hair layer to obtain the second connecting naturalness coefficient. If the deviation value of the connecting hair layer is within the standard range, output the third connecting naturalness coefficient. The hair transplant naturalness information of the test patient is obtained by superimposing the first naturalness coefficient, the second naturalness coefficient, and the third naturalness coefficient.

8. The hair transplant effect evaluation method based on multimodal data fusion according to claim 7, characterized in that, Step S4 specifically includes: The hair transplant effect evaluation coefficient is obtained by superimposing the survival rate evaluation coefficient, the patient self-evaluation coefficient, and the naturalness of the connection evaluation coefficient to determine the hair transplant effect of the tested patient. Based on the survival rate assessment coefficient, patient self-assessment coefficient, naturalness of integration assessment coefficient, and hair transplant effect assessment coefficient, a hair transplant effect assessment report is generated for the patient to be tested. Acquire a wireless communication module and establish a signal connection link between the wireless communication module and the patient to be tested; The wireless communication module is used to send the hair transplant effect evaluation report of the patient to be tested to the user terminal and the background monitoring system. The hair transplant effect evaluation report of the patients to be tested will be uploaded to the blockchain system for storage.

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