DESC:CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY
The present application does claim priority from its provisional application filed on 11th November 2022 under the application number of 202221064538.
TECHNICAL FIELD
The present disclosure relates to a system for a lateral fluorescence immunoassay analyser and a method thereof.
BACKGROUND
The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
Existing method used in fluorescence immunoassay analyser involves sophisticated image segmentation technique for locating Area of interest (AOI). Further, the calibration process can be performed before performing any tests. Further, existing techniques involves in setting the coordinates in advance for the calculation of volumetric parameters. Based on the above observation, it has been noted that there is no robust method for locating the AOI through image processing technique. Further, existing systems cannot provide effective quantitative and qualitative results without performing the calibration process prior to any test.
Therefore, there is long standing need of a system for a fluorescence immunoassay analyser and a method thereof which solves above mentioned limitations of the existing system.

Objectives of the present invention
Some of the objects of the present disclosure, which satisfies at least one embodiment are listed herein below.
One of the objects of the present disclosure is to provide a system and a method for a lateral fluorescence immunoassay analyzer.
Another objective of the present disclosure is to provide a lateral fluorescence immunoassay analyzer to provide quantitative and qualitative test results.
Yet another object of the present invention is to provide a system for the lateral fluorescence immunoassay analyser that is configured to use an integrated QR code and robust image processing technique to provide quantitative and qualitative test results.
Yet another objective of the present invention is to provide an analyzer that can detect areas of interest on lateral fluorescent immunoassay test strip for qualitative and quantitative analysis using image-based technique without performing time consuming calibration process.
Yet another objective of the present invention is to provide a system that is portable and standalone.
SUMMARY
The present disclosure overcomes one or more shortcomings of the prior art and provides additional advantages discussed throughout the present disclosure. Additional features and advantages are realized through the techniques of the present disclosure. Other embodiments and aspects of the disclosure are described in detail herein and are considered a part of the claimed disclosure. The present disclosure has been made in order to solve the above problems, and it is the object of the present disclosure to provide a system and a method for a lateral fluorescence immunoassay analyser.
In one non-limiting embodiment of the present disclosure, a system for a lateral fluorescence immunoassay analyzer has been disclosed. The system for the lateral fluorescence immunoassay analyser may be configured to use integrated QR code and robust image processing technique to provide quantitative and qualitative test results. An internal quality control (IQC) may be controlled through multiple vertical lines with various fluorescent intensities on the base pad. The values obtained, may be captured through the fluorescence detection module and processed through analysis algorithm to provide quantitative result of concentration of the analyte.
In another non-limiting embodiment of the present disclosure, a method for a lateral fluorescence immunoassay analyzer has been disclosed. The method may comprise capturing, via a camera module, at least a few images. The method may further comprise averaging, via a processor, on captured images. The method may further comprise applying convolution, via the processor, to find Area of Interest (AOI). The method may comprise profile plot, via the processor, to find the width of the illuminated area. The method may comprise obtaining, via the processor, volumetric parameters from base line corrected plot. The method may comprise taking, via the processor, ratio of test line volume to control line volume. The method may comprise checking, via the processor, whether ratio is less than one or not. When the ratio is less than one, the processor may be configured for selecting linear machine learning model. If the ratio is greater than one the processor may be configured for selecting polynomial machine learning model. The method may further comprise providing, via the processor, qualitative and quantitative results.
BRIEF DESCRIPTION OF DRAWINGS
The detailed description is described with reference to the accompanying Figures. In the Figures, the left-most digit(s) of a reference number identifies the Figure in which the reference number first appears. The same numbers are used throughout the drawings to refer like features and components.
Figure 1 illustrates a system level architecture (100) of a lateral fluorescence immunoassay analyser, in accordance with an embodiment of the present subject matter.
Figure 2 illustrates a fluorescence detection module architecture (200) of a lateral fluorescence immunoassay analyzer, in accordance with an embodiment of the present subject matter.
Figure 3 illustrates a step wise method (300) of lateral fluorescence immunoassay analyzer, in accordance with an embodiment of the present subject matter.
DETAILED DESCRIPTION
Reference throughout the specification to “various embodiments,” “some embodiments,” “one embodiment,” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, appearances of the phrases “in various embodiments,” “in some embodiments,” “in one embodiment,” or “in an embodiment” in places throughout the specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures or characteristics may be combined in any suitable manner in one or more embodiments.
Referring to Figure 1, a system level architecture (100) of a lateral fluorescence immunoassay analyser is illustrated in accordance with an embodiment of the present subject matter. The system level architecture (100) of the lateral fluorescence immunoassay analyser may comprise a plurality of peripherals comprising a thermal printer (101), a QR scanner module (102), a camera module (103), a multipurpose switch (104), an LED module (105), a touch screen (106), a flexible Wi-Fi antenna (107), a DC jack input (108), a Multi-pack Li-ion batteries (109), a power button (110) and a carrier board (111). The plurality of peripherals is electronically coupled with the carrier board (111).
In one embodiment, the lateral fluorescence immunoassay analyser is configured to use an integrated QR code analyzer and a robust image processing technique to provide quantitative and qualitative test results.
According to the embodiment of the present invention, the OR code module used herein could scan a QR code for the purpose of calibration of a system before the start of the analysis. The QR code reader is a 2D or 3D scan reader, wherein calibration data is embedded in the QR code which has information about the curve and its deviation from the standard curve. Herein, the QR code analyzer eliminates the need for a time-consuming calibration process.
Now referring to figure 2, a fluorescence detection module architecture (200) of a lateral fluorescence immunoassay analyzer is illustrated in accordance with the embodiment of the present disclosure. The detection module architecture (200) may comprise a processor (201), an image sensor (202), a lens (203), a narrowband light source for label excitation (204), an optical bandpass filter (optical) (205), a test and control line of LFIA (206).
The processor (201) as disclosed in the present invention can be selected from Microcontroller, Microprocessor, Embedded Processor, DSP and Media Processor. In a particular embodiment of the present invention the processor used can be selected from Rasperry Pi CoM 4 or Jatson Nano (+ GPU) or a combination thereof.
The image sensor (202) as disclosed in the present invention is selected from but not limited to charge-coupled device (CCD) and the active-pixel sensor (CMOS sensor), fabricated in complementary MOS (CMOS) or N-type MOS (NMOS or Live MOS) technologies.
The lens (203) as disclosed in the present invention can be selected from but not limited to ultra/super wide angle, wide angle, standard, telephoto, super-telephoto, and macro.
The internal quality control (IQC) may be controlled through multiple vertical lines with various fluorescent intensities obtained on the base pad. The values obtained through may be captured through the fluorescence detection module and processed through analysis algorithm to provide quantitative result of concentration of the analyte.
The internal quality control is managed by the multiple vertical fluorescent lines at different level of intensity obtained on the base pad. This may help in checking the performance of optical hardware and even the other peripherals like LED. After every start-up, the image of the base pad may be taken, and its parameters are compared with the parameters of the standard image.
Now referring to the Figure 3, a step wise method for the lateral fluorescence immunoassay analyser is illustrated in accordance with an embodiment of the present disclosure. In one embodiment, the system for the lateral fluorescence immunoassay analyser may configured to use Artificial intelligence (AI) based algorithm for multiple assay tests which further enable the system for highly accurate quantitative and qualitative results.
At step (301), the method starts.
At step (302), the camera module (103) may be configured for capturing at least few images.
At step (303), the processor (201) may be configured for averaging on captured images.
At step (304), the processor (201) may be configured for applying convolution to find Area of Interest (AOI).
At step (305), the processor (201) may profile plot to find the width of the illuminated area.
At step (306), the processor (201) may be configured to obtain volumetric parameters from base line corrected plot.
At step (307), the processor (201) may be configured for taking ratio of test line volume to control line volume.
At step (308), the processor (201) may be configured to check whether ratio is less than one or not. When the ratio is less than one, the system goes to step (309) and may be configured for selecting linear machine learning model. If the ratio is greater than one, then system goes to step (310) and may be configured for selecting polynomial machine learning model.
At step (311), the processor (201) may be configured to provide qualitative and quantitative results.
In one exemplary embodiment, the analysis software may be configured to perform averaging and convolution on the acquired images to determine the Area of Interest (AOI). Further, volumetric parameters may be computed using the machine learning algorithm through the base line adjusted plot provides qualitative and quantitative outputs.
In one embodiment, the system may not require any calibration process. The system may comprise the calibration data embedded in the QR code which has information about the curve and its deviation from the standard curve.
In one embodiment, the system may be able to perform a number of other tests through software updates, without changing the system. Wherein the system is intended to be used for the quantitative and/or qualitative in-vitro determination of chemical and/or biological markers and/or antigen/antibody in the clinical specimen. The clinical specimen as disclosed in the present invention can be selected from but not limited to plasma, serum, tissue, swab, sputum, and blood.
In one embodiment, the system may be compact, lightweight and has built-in Wi-Fi, a barcode scanner, Bluetooth® and a thermal printer to print the results of the tests The weight of system as disclosed in the present invention lies from 500GM to 10 kilogram.
The system as disclosed in the present invention can be used for but not limited to central laboratories, medical facilities, patients, emergency laboratories, clinical departments, other medical services (such as community clinics and general clinics), physiological examination centers, retirement homes, as well as sports facilities, corporate health services, primary patient screening, and medical examination.
In one embodiment, the system as disclosed herein is portable, standalone, and sensitive. The system is designed to detect the fluorescence particle on the strip and intends to detect analytes like viruses, antibodies proteins, etc. It can be characterized by easy operation, fast test speed, and good repeatability. The optical sensitivity of the detector lies from 0.1 x 10-9 W. Furthermore, the system as disclosed herein provides results in less than 20 seconds.
The system as disclosed in the present invention wherein the test will start with a selection of strips for a particular disease selected but not limited to qualification or quantification of markers related to heart, diabetes, COVID, cancer, autoimmune diseases, and for identification of hormones, amino acids, vitamins, antigen, and antibodies.
In one embodiment, the system may comprise rechargeable Li-ion batteries with longer battery times. The rechargeable Li-ion batteries may comprise a high-performance processor configured to handle multiple process.
The embodiments, and alternatives of the preceding paragraphs, the description, including any of their various aspects or respective individual features, may be taken independently or in any combination. Features described in connection with one embodiment are applicable to all embodiments unless such features are incompatible.
According to an embodiment of the present invention, the fluoresce-based lateral immunofluorescent test cartridge is used to detect analytes of interest. After performing the lateral immunofluorescent test on the cartridge, it is inserted into the lateral fluorescence immunoassay analyzer as disclosed in the present invention. The system captures one or more images of the test cartridge and applies convolution, via the processor, to find the Area of Interest (AOI). Further, volumetric parameters may be computed using the machine learning algorithm through the baseline-adjusted plot to provide qualitative and quantitative results. Although the implementations for the lateral fluorescence immunoassay analyser have been described in language specific to structural features and/or methods, it is to be understood that the appended claims are not necessarily limited to the specific features or methods described.
Dated this 11th Day of November 2023.
,CLAIMS:We Claim:

1. A system for a lateral fluorescence immunoassay analyzer, comprising of:
a) a system-level architecture (100) comprising of:
a plurality of peripherals, wherein the plurality of peripherals comprising of a thermal printer (101) configured to print out the result of the analysis; a QR scanner module (102) configured to scan a QR code; a camera module (103) configured to capture an image of a test cartridge; a multipurpose switch (104) configured to on/off the system; an LED module (105); a touch screen (106); a flexible Wi-Fi antenna (107); a DC jack input (108); a multi-pack Li-ion batteries (109); a power button (110); a carrier board (111);
b) a fluorescence detection module architecture (200) on a base pad comprising of: a processor (201); an image sensor (202); a lens (203); a narrowband light source for label excitation (204); an optical bandpass filter (205); a test and control line of LFIA (206);
c) a QR code card configured to read by the QR scanner module (102);
wherein the system for a lateral fluorescence immunoassay analyzer is configured to use an integrated QR code card and robust image processing technique to provide a quantitative and a qualitative test results.

2) The system for the lateral fluorescence immunoassay analyzer as claimed in claim 1, wherein the plurality of peripherals is electronically coupled with the carrier board (111).

3) The system for the lateral fluorescence immunoassay analyzer as claimed in claim 1, wherein the base pad is configured to comprises multiple vertical lines with various fluorescent intensities.

4) The system for the lateral fluorescence immunoassay analyzer as claimed in claim 1, wherein the system performs a test through a software update, without changing the system, wherein the test is for the quantitative and/or qualitative in-vitro determination of chemical and/or biological markers and/or an antigen/antibody in a clinical specimen.

5) The system for the lateral fluorescence immunoassay analyzer as claimed in claim 1, wherein the said system is compact, portable, and lightweight.

6) A method for a lateral fluorescence immunoassay analyzer comprises the steps of:
a) capturing (302), a few images, by the camera module (103);
b) averaging (303), on captured images, by one or more processors (201);
c) finding an area of interest, by applying convolution, using one or more processors (201);
d) finding (305), a width of the illuminated area, using a profile plot by one or more processors (201);
e) obtaining (306), volumetric parameters from baseline corrected plot obtained by using one or more processor (201);
f) taking a ratio of test line volume to control line volume, by configuring a one or more processor (201);
g) checking (308), the specific ratio using the processor (201), and when the ratio is less than one, the system goes to step (309) and is configured for selecting a linear machine learning model, and if the ratio is greater than one then system goes to step (310) and is configured for selecting a polynomial machine learning model; and
h) providing (311), the qualitative and quantitative results of the test by using a one or more processor (201).