Description:TECHNICAL FIELD

[0001] The present disclosure generally relates to a field of detecting a medical condition, and more particularly relates to a system and a method for detecting a vaginal Potential of Hydrogen (pH) of a user.
BACKGROUND

[0002] Generally, a large number of female population suffer from common vaginal problems. However, these problems often go unaddressed due to social stigmas and a lack of accessible healthcare. The vaginal related problems can be reduced through regular monitoring of the vaginal pH. Monitoring the vaginal pH levels is an essential aspect of maintaining the vaginal health. By determining the vaginal pH, one can provide valuable insights into various factors affecting the vaginal well-being, including diet, hygiene practices, sexual activity, and hormonal fluctuations. However, traditional methods for monitoring the vaginal pH, such as clinical visits, are often inconvenient, costly, and inaccessible, particularly for the females residing in less urbanized areas.
[0003] Further, existing self-testing kits also pose challenges. The existing self-testing kits for measuring the vaginal pH use a pH chart or a colour chart provided with the testing kit to detect an approximate range of the vaginal pH. Comparing a vaginal sample with the colour chart may include a scope of human error and may lead to inaccurate results. Also, some testing kits require a test strip to be inserted into the vagina directly which may cause other problems related to skin. The test strip is inserted with the help of a test probe and is required to be correctly positioned in the vagina. In some cases, the test strip may fall off or get lost during insertion or removal of the test probe within the vagina. Additionally, direct insertion of the test probe or the test strip into the vagina can lead to contamination with other bodily fluids further leading to incorrect results. Moreover, the existing pH monitoring kits may be financially burdensome for females as the kits are designed for single use and require repeated purchases.
[0004] Therefore, in view of the above-mentioned problems, it is advantageous to provide an improved system and method for detecting the vaginal pH that can overcome the above-mentioned problems and limitations.

SUMMARY

[0005] This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention. This summary is neither intended to identify key or essential inventive concepts of the invention nor is it intended for determining the scope of the invention.
[0006] According to an embodiment of the present disclosure, disclosed herein is a method for detecting the vaginal Potential of Hydrogen (pH) of a user. The method includes receiving at least one image of a test kit having the vaginal fluid of the user. The method further includes creating a histogram for the received at least one image. The method also includes identifying one or more characteristics of the created histogram by using the created histogram and a pH detection-based Machine Learning (ML) model. The method further includes detecting the vaginal pH value of the user by using the pH detection-based ML model based on the identified one or more characteristics. The method also includes displaying the detected vaginal pH value to the user.
[0007] According to another embodiment of the present disclosure, also disclosed herein is a system for detecting a vaginal Potential of Hydrogen (pH) of a user. The system includes a memory and one or more processors communicably coupled to the memory. The one or more processors are configured to receive at least one image of a test kit having a vaginal fluid of the user. The one or more processors are also configured to create a histogram for the received at least one image. The one or more processors are also configured to identify one or more characteristics of the created histogram by using the created histogram and a pH detection-based Machine Learning (ML) model. The one or more processors are also configured to detect the vaginal pH value of the user by using the pH detection-based ML model based on the identified one or more characteristics. The one or more processors are also configured to display the detected vaginal pH value to the user.
[0008] To further clarify the advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which are illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

[0009] These and other features, aspects, and advantages of the present invention will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:
[0010] Figure 1 illustrates an environment depicting a user device employed to obtain a Potential of Hydrogen (pH) readings from a vaginal fluid sample, according to an embodiment of the present disclosure;
[0011] Figure 2 illustrates a schematic view of a system for detecting the vaginal pH of a user, according to an embodiment of the present disclosure;
[0012] Figure 3 illustrates a process flow chart depicting a method for detecting the vaginal pH of the user, according to an embodiment of the present disclosure;
[0013] Figure 4 illustrates a graphical representation showcasing a plurality of histograms depicting a distribution of a plurality of vaginal pH values obtained from a set of test samples, according to an embodiment of the present disclosure; and
[0014] Figure 5 illustrates a graphical representation showcasing the plurality of histograms depicting a comparison of the plurality of vaginal pH values obtained from the set of test samples, according to an embodiment of the present disclosure.
[0015] Further, skilled artisans will appreciate that those elements in the drawings are illustrated for simplicity and may not have necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help to improve understanding of aspects of the present invention. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.

DETAILED DESCRIPTION

[0016] For the purpose of promoting an understanding of the principles of the invention, reference will now be made to the various embodiments and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the invention is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the invention as illustrated therein being contemplated as would normally occur to one skilled in the art to which the invention relates.
[0017] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are explanatory of the invention and are not intended to be restrictive thereof.
[0018] Reference throughout this specification to “an aspect”, “another aspect” or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, appearances of the phrase “in an embodiment”, “in another embodiment” and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.
[0019] The terms “comprises”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such process or method. Similarly, one or more devices or sub-systems or elements or structures or components preceded by “comprises... a” does not, without more constraints, preclude the existence of other devices or other sub-systems or other elements or other structures or other components or additional devices or additional sub-systems or additional elements or additional structures or additional components.
[0020] The present disclosure includes a test kit designed to monitor vaginal pH levels. The present disclosure offers a user-friendly, accurate, and affordable solution for women to manage their reproductive health from home. The present disclosure further integrates usage of technology thereby enabling users to perform tests easily and get immediate results without a need for expensive equipment or frequent visits to healthcare providers.
[0021] Figure 1 illustrates an environment 100 depicting a user device 102 employed to obtain a Potential of Hydrogen (pH) reading from a vaginal fluid sample, according to an embodiment of the present disclosure. As depicted in Figure 1, the user device 102 may be a smart phone. The user device may further include, but is not limited to, computers, laptops, tablets, or any device having an image capturing/processing feature.
[0022] The vaginal fluid sample may be collected manually by gently swabbing a vaginal area with a cotton swab. A tip of the cotton swab may be placed on a test pad which is further connected to a test strip 104. The test strip 104 may be a paper test strip having an elevated test pad. The test pad may be infused with a testing reagent or an indicator dye that reacts with the vaginal fluid sample to indicate the pH levels. Due to the presence of the indicator dye, the test pad changes colour when the test pad may come in contact with the vaginal fluid sample.
[0023] In an exemplary embodiment, the colour of the test pad may be yellow. When the test pad comes in contact with the vaginal fluid sample, the colour of the test pad may change from yellow to green or blue depending upon the pH of the vaginal fluid sample determining an alkalinity of pH or an infection. The colour change is indicative of a pH level. In an example, different colours correspond to different pH levels and may vary depending on a type of reagent or the indicator dye used. The present disclosure is not limited to any specific dye or colour scheme. The choice of dye and the associated colour changes are subject to variation based on a chemical formulation and design preferences of a manufacturer. Therefore, the scope of the present invention is not limited to a specific indicator dye or the testing reagent that can demonstrate the colour change in response to the different pH levels, as described herein.
[0024] In an embodiment, at least one image 108 of the test strip 104 may be taken from the user device 102 once the colour change is evident. The test strip 104 may be placed on any background including white for capturing the at least one image 108 through the user device 102. More specifically, the at least one image 108 may be captured in any open environment thereby reducing significant costs on specialized equipment, simplifying, and speeding up the testing process. Further, the test strip 104 may include a QR code 106 that may be scanned from the user device 102 to detect an authenticity of the test strip 104. In another embodiment, the at least one image 108 of the test strip 104 may be directly received at the user device 102, from another device (not shown) via a network.
[0025] The user device 102 may have an application installed within the user device 102 and the application may be equipped with image processing capabilities to analyze the colour change accurately. The application may use, but is not limited to, calorimetric image processing and machine learning algorithms to give the test reading. The software of the application may be trained on a large number of data sets to give accurate results. The application may process the at least one image 108 to determine the vaginal pH level based on the colour analysis. A result 110 may then be displayed on a user interface of the user device 102, thereby providing the user with immediate feedback on the vaginal health.
[0026] In an embodiment, the application may be equipped with an optimized image capturing feature. The optimized image capturing features may include, but is not limited to, autofocus and image stabilization, to ensure high-quality image capture and facilitate feature extraction. Further, the user device 102 may include a gyroscope to capture the at least one image 108 correctly.
[0027] Figure 2 illustrates a schematic view of a system 200 for detecting the vaginal pH of the user, according to an embodiment of the present disclosure. The system 200 may include one or more processors 202 configured to perform one or more operations to detect the vaginal pH of the user.
[0028] In an embodiment, the one or more processors 202 may include, but is not limited to, a memory 204, one or more modules 206, and a database 208. The one or more processors 202 may be a single processing unit or a number of units, all of which may include multiple computing units. The one or more processors 202 may be implemented as one or more microprocessing units, microcomputers, microcontrollers, digital signal processing units, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the one or more processors 202 may be configured to fetch and execute computer-readable instructions and data stored in the memory 204.
[0029] The memory 204 may include any non-transitory computer-readable medium known in the art including, for example, volatile memory, such as static random-access memory (SRAM) and dynamic random-access memory (DRAM), and/or non-volatile memory, such as read-only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes, but is not limited to the same.
[0030] The one or more modules 206, amongst other things, include routines, programs, objects, components, data structures, etc., which perform particular tasks or implement data types, but is not limited to the same. The one or more modules 206 may also be implemented as, signal processing unit(s), state machine(s), logic circuitries, and/or any other device or component that manipulates signals based on operational instructions.
[0031] Further, the one or more modules 206 may be implemented in hardware, instructions may be executed by the one or more processors 202. The one or more processors 202 may comprise a computer, a state machine, a logic array and/or any other suitable devices capable of processing instructions. The one or more processors 202 may be general-purpose processing unit(s) which execute instructions to cause the general-purpose processing unit to perform operations or, may be dedicated to performing the required functions. In some example embodiments, the one or more modules 206 may be machine-readable instructions (software, such as web-application, mobile application, program, etc.) which, when executed by the one or more processors 202 perform any of the described functionalities/methodology, as discussed throughout the present disclosure.
[0032] In an implementation, the modules 206 may include a receiving module 210, a histogram module 212, an identifying module 214, and a detecting module 216. The receiving module 210, the histogram module 212, the identifying module 214, and the detecting module 216 may be in communication with each other. The database 208 may serve, amongst other things, as a repository for storing data processed, received, and generated by one or more of the modules 206.
[0033] In an embodiment, the one or more processors 202 may be configured to receive the at least one image 108 of the test kit having the vaginal fluid of the user through the receiving module 210. The test kit may include the test strip 104 and the test pad that changes colour based on the pH of the fluid. The one or more processors 202 may further be adapted to create a histogram for the received at least one image 108 via the histogram module 212. The one or more processors 202 may identify one or more characteristics of the created histogram by using the created histogram and a pH detection-based Machine Learning (ML) model through the identifying module 214. The one or more processors 202 may then detect the vaginal pH value of the user by using the pH detection-based ML model based on the identified one or more characteristics through the detection module 216 and display the detected vaginal pH value to the user.
[0034] In an embodiment of the present disclosure, the one or more modules 206 may be implemented as part of the one or more processors 202. In another embodiment of the present disclosure, the one or more modules 206 may be external to the one or more processors 202. In yet another embodiment of the present disclosure, the one or more modules 206 may be part of the memory 204. In another embodiment of the present disclosure, the one or more modules 206 may be part of hardware, separate from the one or more processors 202.
[0035] Figure 3 illustrates a process flow chart depicting a method 300 for detecting the vaginal pH of the user, according to an embodiment of the present disclosure.
[0036] At step 302, the method 300 includes receiving the at least one image 108 of the test kit that may have the vaginal fluid sample of the user. The at least one image 108 may then be further analyzed based on the change in colour.
[0037] At step 304, the method 300 includes creating a histogram for the received at least one image 108. A histogram may be a graphical representation of a distribution of a plurality of data points or colour values within the at least one image 108. The histogram may aid in quantifying a variation in colour that occurs on the test strip 104 after the reaction takes place between the vaginal fluid sample and the indicator dye.
[0038] Further, the histogram represents a pixel intensity with respect to a frequency of RGB (red, green, and blue). The pixel intensity may indicate a brightness or strength of the light that each pixel in the at least one image 108 emits or reflects. The pixel intensity may further provide a colour depth and saturation on the test strip 104 where the pH reaction occurs. The varying pH levels may cause distinct colour changes on the test strip 104 due to a chemical reaction with the vaginal fluid. In an exemplary embodiment, a certain pH might intensify a red component, decrease a blue component, or alter a green component. The histogram may indicate the changes in colour by displaying peaks or shifts in the frequency of certain RGB values.
[0039] At step 306, the method 300 includes identifying one or more characteristics of the created histogram by using the created histogram and a pH detection-based Machine Learning (ML) model. The pH detection-based ML model may be trained to interpret the histograms created from the at least one image 108. The pH detection-based ML model analyzes the histograms to identify one or more characteristics that are indicative of certain pH levels. The one or more characteristics may indicate a distribution gap between Red Green Blue (RGB) values and peak frequency in the created histogram. The one or more characteristics may further include, but are not limited to, peaks, colour intensity, spread, and other statistical features to determine the pH of the vaginal fluid sample placed on the test strip 104.
[0040] At step 308, the method 300 further includes detecting the vaginal pH value of the user by using the pH detection-based ML model based on the identified one or more characteristics. The pH detection-based ML model may match the identified one or more characteristics against a trained dataset to accurately predict the pH level represented by the colour changes on the test strip 104. In an embodiment, the method 300 further includes comparing the identified one or more characteristics with a plurality of predefined characteristics associated with a predefined set of the vaginal pH values by using the pH detection-based ML model and detecting the vaginal pH value of the user based on the result of a comparison.
[0041] At step 310, the method 300 includes displaying the detected vaginal pH value on the user device 102. The detected pH value is displayed to the user through a user interface on the user device 102 and provides immediate feedback to the user regarding the vaginal pH level.
[0042] In an embodiment, the method 300 also includes determining, if the detected vaginal pH value is within a predefined range. The predefined range may be a normal pH range of a healthy vagina. In an exemplary embodiment, the predefined range may be between 3.8 and 4.5. Upon determining that the detected vaginal pH value is outside the predefined range, the method 300 further includes generating one or more recommendations (i.e., the feedback) to normalize the detected vaginal pH value by correlating the detected vaginal pH value and a predefined set of recommendations. In an embodiment, the predefined set of recommendations may be based on best medical practices, research data, or clinical guidelines tailored to address specific pH imbalances. In an exemplary embodiment, if the pH is too acidic (lower than the predefined range), the predefined set of recommendations might include hydration tips, dietary adjustments, or suggestions to reduce acidic substances intake. When the pH is too alkaline (higher than the predefined range), the predefined set of recommendations include changes in hygiene practices, reviewing the use of certain soaps or lubricants, or medical consultations for possible infections. In an embodiment, the predefined set of recommendations may be displayed to the user through the user interface of the user device 102 to help the user take informed steps to normalize their vaginal pH.
[0043] Figure 4 illustrates a graphical representation 400 showcasing a plurality of histograms depicting a distribution of a plurality of the vaginal pH values obtained from a set of test samples, according to an embodiment of the present disclosure. The histogram provides a visual representation of data to understand trends and patterns in the pH levels.
[0044] Further, the histograms aid in analyzing the distribution of the plurality of vaginal pH values across the set of test samples to identify common pH ranges as well as outliers within a population that is studied.
[0045] Figure 5 illustrates a graphical representation 500 showcasing the plurality of histograms depicting a comparison of the plurality of vaginal pH values obtained from the set of test samples, according to an embodiment of the present disclosure. Figure 5 further depicts the user may compare the pH levels across different age groups, health conditions, or after using different types of products that may influence the vaginal pH. The histogram provides the visual representation that aids in assessing the reliability and precision of the pH testing method under varied circumstances and may provide valuable data for improving the product and targeting specific user needs. The histogram may also track changes in the vaginal pH over time, showing any deviation may be a result of hormonal changes, menstrual cycles, or life stages such as pregnancy or menopause.
[0046] The present disclosure provides a solution to monitor the vaginal health by making the vaginal pH monitoring accessible and user-friendly The present disclosure not only reduces a need for expensive lab tests and frequent doctor visits but also provides immediate results. The integration of image processing techniques and the pH detection-based ML model ensures that the pH readings are both accurate and reliable thereby addressing common issues associated with traditional colorimetric methods that may be subjective and prone to errors.
[0047] Moreover, the system 200 and method 300 disclosed herein, provides real-time feedback on the user’s vaginal pH levels to facilitate immediate and informed health decisions. The system 200 and method 300 enables the users to track changes on the vaginal pH thereby enabling an early detection of any infections or other health issues before it becomes more severe. The test kit disclosed in the present disclosure is designed to be lightweight, budget-friendly, easy to use, and accessible at user’s convenience.
[0048] While specific language has been used to describe the present subject matter, any limitations arising on account thereto, are not intended. As would be apparent to a person in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein. The drawings and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment.

Claims:

1. A method (300) for detecting a vaginal Potential of Hydrogen (pH) of a user, the method (300) comprising:
receiving (302) at least one image (108) of a test kit having a vaginal fluid of the user;
creating (304) a histogram for the received at least one image (108);
identifying (306) one or more characteristics of the created histogram by using the created histogram and a pH detection-based Machine Learning (ML) model;
detecting (308) the vaginal pH value of the user by using the pH detection-based ML model based on the identified one or more characteristics; and
displaying (310) the detected vaginal pH value to the user.

2. The method (300) as claimed in claim 1, wherein the one or more characteristics comprise a distribution gap between Red Green Blue (RGB) values and a peak frequency in the created histogram.

3. The method (300) as claimed in claim 1, further comprising:
determining if the detected vaginal pH value is within a predefined range; and
generating, upon determining that the detected vaginal pH value is outside the predefined range, one or more recommendations to normalize the detected vaginal pH value by correlating the detected vaginal pH value and a predefined set of recommendations.

4. The method (300) as claimed in claim 1, wherein the histogram represents a pixel intensity with respect to a frequency of RGB.

5. The method (300) as claimed in claim 1, wherein detecting the vaginal pH value of the user comprises:

comparing the identified one or more characteristics with a plurality of predefined characteristics associated with a predefined set of the vaginal pH values by using the pH detection-based ML model; and
detecting the vaginal pH value of the user based on the result of a comparison.

6. A system (200) for detecting a vaginal Potential of Hydrogen (pH) of a user, the system comprising:
a memory (204); and
one or more processors (202) communicably coupled to the memory (204), the one or more processors (202) are configured to:
receive at least one image (108) of a test kit having a vaginal fluid of the user;
create a histogram for the received at least one image (108);
identify one or more characteristics of the created histogram by using the created histogram and a pH detection-based Machine Learning (ML) model;
detect the vaginal pH value of the user by using the pH detection-based ML model based on the identified one or more characteristics; and
display the detected vaginal pH value to the user.

7. The system (200) as claimed in claim 6, wherein the one or more characteristics comprise a distribution gap between Red Green Blue (RGB) values and a peak frequency in the created histogram.

8. The system (200) as claimed in claim 6, wherein the one or more processors (202) are configured to:
determine if the detected vaginal pH value is within a predefined range; and
generate, upon determining that the detected vaginal pH value is outside the predefined range, one or more recommendations to normalize the detected vaginal pH value by correlating the detected vaginal pH value and a predefined set of recommendations.

9. The system (200) as claimed in claim 6, wherein the histogram represents a pixel intensity with respect to a frequency of RGB.

10. The system (200) as claimed in claim 8, wherein, in detecting the vaginal pH value of the user, the one or more processors (202) are configured to:
compare the identified one or more characteristics with a plurality of predefined characteristics associated with a predefined set of the vaginal pH values by using the pH detection-based ML model; and
detect the vaginal pH value of the user based on the result of a comparison.