Description:DESCRIPTION OF INVENTION
FIELD OF THE INVENTION
The present invention relates to diagnostic device and method.
More specifically to a multi-antigen lateral flow immunoassay (LFIA) device for cancer screening. It integrates multiplexed biomarker detection, high-resolution imaging, artificial intelligence (AI), and automated reporting to enhance diagnostic accuracy, efficiency, and accessibility.
BACKGROUND OF THE INVENTION
Early detection of cancer is paramount in improving treatment outcomes and survival rates. When diagnosed at an early stage, many cancers can be treated effectively using curative interventions such as surgery, radiation therapy, chemotherapy, or targeted therapies. Early-stage detection not only enhances the likelihood of successful treatment but also minimizes the physical, emotional, and financial burden on patients by reducing the need for aggressive or invasive treatments. Furthermore, early diagnosis can significantly improve a patient’s quality of life by lowering the likelihood of metastasis and preserving organ function.
Despite these benefits, achieving early detection remains a challenge, particularly in low-resource settings where access to advanced diagnostic tools is limited. Even in well-equipped healthcare systems, existing screening methods may be expensive, invasive, or labor-intensive, making them unsuitable for widespread application. The lack of scalable, affordable, and accurate diagnostic tools hinders mass screening programs, often delaying diagnosis until the cancer has progressed to more advanced and harder-to-treat stages.
Given the global burden of cancer as a leading cause of mortality, there is a pressing need for innovative diagnostic solutions that enable rapid, accurate, and accessible cancer detection. These tools should be capable of integrating into diverse healthcare environments, from state-of-the-art hospitals to resource-limited clinics, and provide actionable results in real-time.
Lateral flow immunoassays (LFIAs) have gained widespread acceptance in point-of-care testing due to their affordability, simplicity, and rapid turnaround times. These devices are particularly suited to decentralized healthcare settings, such as rural clinics, mobile health units, or home testing, making them indispensable in resource-constrained environments. Their ease of use, requiring minimal technical training, has made LFIAs a popular choice for detecting infectious diseases, monitoring chronic conditions, and performing pregnancy tests.
However, when applied to complex diagnostic fields such as cancer detection, existing LFIA technologies exhibit significant limitations:
1. Subjective Interpretation:
The visual interpretation of LFIA results often depends on the user’s expertise and experience. Differences in perception, particularly in weak or borderline test results, can lead to diagnostic errors.
Variability in lighting conditions, test line intensity, and human judgment further exacerbate the inconsistency in result interpretation, undermining the reliability of LFIAs in critical diagnostic applications such as cancer screening.
2. Limited Sensitivity and Specificity:
Traditional LFIA devices may not achieve the high sensitivity required for detecting low concentrations of cancer biomarkers, particularly in early-stage cancers.
The inability to detect subtle changes in biomarker levels often results in missed diagnoses, reducing the utility of LFIA tests in early detection programs.
3. Lack of Data Integration:
Current LFIA tests operate as standalone tools, focusing solely on detecting a specific biomarker without considering additional patient-specific information.
Critical data such as demographic details, genetic predispositions, personal medical history, and omics data are not incorporated into the diagnostic process, leading to missed opportunities for comprehensive risk evaluation and personalized care.
4. Absence of Multiplexing Capabilities:
Many LFIA systems are designed for single-marker detection, requiring multiple tests to evaluate a panel of cancer biomarkers. This not only increases costs but also reduces efficiency and practicality in mass screening contexts.
5. Lack of Advanced Analysis:
Conventional LFIAs do not leverage modern data analysis tools, such as artificial intelligence (AI) or machine learning, to improve diagnostic accuracy and consistency.
The absence of automated analysis mechanisms increases the likelihood of human error and reduces the scalability of LFIA tests for population-wide screening.
The limitations of existing LFIA technologies highlight the critical need for an advanced, integrated diagnostic tool capable of addressing these challenges. The present invention responds to this need by introducing a multi-antigen LFIA device that incorporates cutting-edge technologies to overcome the drawbacks of traditional LFIAs and enhance their applicability in cancer diagnostics.
OBJECTS OF THE INVENTION
The primary object of the invention is to detect multiple cancer and inflammatory condition biomarkers simultaneously, enabling comprehensive diagnostic insights from a single test;
Further object of the invention is to provide an advanced imaging systems, including fluorescence-based detection, improve the device’s ability to identify low concentrations of biomarkers, making it suitable for early-stage cancer detection;
Further object of the invention is to integrate AI modules to enable automated quantification of biomarker levels, reducing reliance on subjective visual interpretation;
Another object of the invention is to incorporate demographic data, omics data, medical history, and patient-reported symptoms into the diagnostic process for personalized diagnostic insights;
Another object of the present invention is to facilitate scalable diagnostic solutions for application in diverse healthcare settings, including low-resource environments, point-of-care testing, and home testing kits.
BRIEF DESCRIPTION OF DRAWINGS
The accompanying drawings constitute a part of this specification and illustrate one or more embodiments of the invention. Preferred embodiments of the invention are described in the following with reference to the drawings, which are for the purpose of illustrating the present preferred embodiments of the invention and not for the purpose of limiting the same.
For simplicity and clarity of illustration, the drawing figures illustrate the general manner of construction, and descriptions and details of well-known features and techniques may be omitted to avoid unnecessarily obscuring the invention. Additionally, elements in the drawing figures are not necessarily drawn to scale. For example, the dimensions of some of the elements in the figures may be exaggerated relative to other elements to help improve understanding of embodiments of the present invention. The same reference numerals in different figures denotes the same elements.
In the drawings:
Figure 1 illustrates LFIA (Lateral Flow Immunoassay) strip for the efficient and accurate detection of multiple cancer biomarkers from biological samples, such as blood, serum, plasma, cerebrospinal fluid (CSF), ascitic fluid, or pleural fluid. It features:
• Sample Pad: Ensures smooth and uniform migration of samples across the strip for effective testing.
• Control Line: Confirms the operational validity of the test, indicating proper reagent and sample migration.
• Test Lines: Dedicated to specific biomarkers, including PSA, CEA, CA-125, AFP, CA 19-9, HE4, CA15-3, CYFRA 21-1, NSE, and SCC antigens, with immobilized antibodies ensuring high specificity and sensitivity.
• Protective Housing: Encased in medical-grade polycarbonate with a transparent cover for sterility, durability, and easy imaging.
• Multiplexed Design: Enables simultaneous detection of multiple biomarkers, enhancing diagnostic efficiency and reducing the need for multiple tests.
Figure 2 provides a structural design and components of the LFIA Strip in accordance with the embodiments of the present invention.
SUMMARY OF THE INVENTION
Embodiments of the present disclosure present technological improvements as a solution to one or more of the above-mentioned technical problems recognized by the inventor in existing techniques.
The present disclosure introduces a multi-antigen lateral flow immunoassay device with ai-driven risk assessment for cancer and inflammatory conditions offering a rapid, efficient, and accurate method for detecting multiple cancer and inflammatory condition biomarkers from biological samples. The device integrates advanced technologies, including a multiplexed LFIA test strip, high-resolution imaging systems, artificial intelligence (AI) for data analysis, and automated reporting to provide a comprehensive diagnostic solution.
According to an aspect of the invention, an LFIA test strip is the central component of the device and is designed to detect various cancer biomarkers, including Prostate-Specific Antigen (PSA), Carcinoembryonic Antigen (CEA), Cancer Antigen 125 (CA-125), Alpha-Fetoprotein (AFP), Carbohydrate Antigen 19-9 (CA 19-9), Human Epididymis Protein 4 (HE-4), Cancer Antigen 15-3 (CA15.3), Cytokeratin-19 Fragment (CYFRA 21-1), Neuron-Specific Enolase (NSE), and Squamous Cell Carcinoma (SCC) Antigens. The strip consists of a sample pad that ensures smooth migration of the sample, test lines embedded with antibodies to bind specific biomarkers, a control line to validate test results, and an absorption pad to ensure proper sample flow. The multiplexed design of the test lines allows for simultaneous detection of multiple biomarkers from a single sample, enhancing diagnostic efficiency and reducing testing time. The listed markers claimed for generally more associated with cancer detection but some of these markers (e.g., CEA, CA-19-9, CA 125 and SCC) can be elevated in certain inflammatory diseases. SCC (Squamous Cell Carcinoma Antigen) - This can be elevated in conditions like squamous cell carcinomas and in some inflammatory diseases, though it is primarily used for cancer detection.
CEA (Carcinoembryonic Antigen) – Primarily used for monitoring cancers, especially colorectal cancer, though it can also be elevated in inflammatory conditions like Crohn's disease.
CA-125 (Cancer Antigen 125) – Primarily used for ovarian cancer but can also be elevated in some inflammatory conditions (e.g., endometriosis, pelvic inflammatory disease).
CA 19-9 (Cancer Antigen 19-9) – A marker for pancreatic and gastrointestinal cancers, though elevated in some inflammatory conditions like pancreatitis.
According to further aspect of the present invention, a high-resolution imaging system captures images of the LFIA strip, including the visible test and control lines, which are essential for analyzing the presence and intensity of cancer biomarkers. The system employs fluorescence detection technology, which improves sensitivity and allows for the identification of low-concentration biomarkers, critical for early-stage cancer detection.
According to further aspect of the present invention, an AI module analyzes the captured images to quantify biomarker concentrations and compares them with predefined diagnostic thresholds to generate cancer risk scores. The AI module also integrates machine learning algorithms that refine its diagnostic accuracy by learning from cumulative data. Additionally, the AI module combines biomarker data with patient-specific information, including demographics, medical history, genetic profiles, and symptoms, enabling personalized risk assessment and diagnostic evaluation.
According to further aspect of the present invention, the system also features a symptom analysis module, which collects patient-reported data such as age, gender, lifestyle factors, and family history. This data is integrated with biomarker results to provide a more comprehensive and tailored diagnostic profile. The automated reporting system generates detailed diagnostic reports that include biomarker profiles, risk scores, and personalized clinical recommendations for follow-up tests or treatment, aiding healthcare providers in making informed decisions.
Incorporating a cloud-based AI system, the device ensures secure data storage and efficient data transfer for real-time analysis. The data, including the captured images and patient details, are securely transmitted to the cloud for processing, enabling seamless access to diagnostic results. The device’s modular design allows it to adapt to various healthcare environments, from clinical settings to remote and point-of-care applications.
The present invention is a powerful tool for early cancer detection, offering a reliable, scalable, and user-friendly solution for improving diagnostic accuracy and patient outcomes across diverse healthcare settings.
The objects and the advantages of the invention are achieved by the process elaborated in the present disclosure.
DETAILED ELEMENTS DESCRIPTION OF THE INVENTION
The following detailed description illustrates embodiments of the present disclosure and ways in which the disclosed embodiments can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognize that other embodiments for carrying out or practicing the present disclosure are also possible.
The present invention describes a multi-antigen lateral flow immunoassay device with AI-driven cancer risk assessment and personalized diagnostic reporting. It integrates advanced diagnostic technologies such as multiplexed biomarker detection, high-resolution imaging, artificial intelligence (AI), and automated reporting to provide a robust, comprehensive tool for early cancer detection.
The following description illustrates the workflow and components involved in the operation of the multi-antigen lateral flow immunoassay (LFIA) device designed for cancer screening and early detection. This detailed explanation highlights the integration of advanced technologies, including the LFIA test strip for multiplexed biomarker detection, high-resolution imaging for precise visualization, artificial intelligence (AI) for automated analysis, and an automated reporting system for actionable diagnostic insights.
Multi-Antigen LFIA Test Strip
According to an embodiment of the invention, lateral flow immunoassay (LFIA) strip is the core diagnostic component of the system, meticulously designed to detect multiple cancer biomarkers through immune complex (antigen-antibody interactions). The strip has a sample pad (300), which serves as the inlet for applying biological samples, such as blood, serum, plasma, cerebrospinal fluid (CSF), ascitic fluid, or pleural fluid. This pad ensures uniform sample distribution and smooth migration into the reaction zones while maintaining compatibility with diverse fluid types. Additionally, the sample pad (300) includes reagents necessary to facilitate the immune complex (antigen-antibody interactions) further along the strip.
The LFIA Strip Case (100) securely encases the entire LFIA strip within a protective housing made from medical-grade polycarbonate or an equivalent material, ensuring durability, sterility, and ease of handling. The housing includes a transparent cover, allowing clear visibility of the control and test lines during imaging or visual inspection. This case ensures sterility and durability during handling and testing, while also simplifying the integration with external imaging systems by providing an accessible design for inspection and analysis. The Paper Strip Housing is specifically engineered to hold the paper strip in place, ensuring proper alignment of the strip for consistent sample migration and accurate reaction visualization.
The strip includes test lines (202–211), each specifically embedded with immobilized antibodies that bind to individual cancer biomarkers, including Prostate-Specific Antigen (PSA), Carcinoembryonic Antigen (CEA), Cancer Antigen 125 (CA-125), Alpha-Fetoprotein (AFP), Carbohydrate Antigen 19-9 (CA 19-9), Human Epididymis Protein 4 (HE-4), Cancer Antigen 15-3 (CA15.3), Cytokeratin-19 Fragment (CYFRA 21-1), Neuron-Specific Enolase (NSE), and Squamous Cell Carcinoma (SCC) Antigens. The test lines generate visible markers upon immune complex (antigen-antibody interaction), indicating the presence and concentration of the biomarkers. The Conjugate Pad releases labeled antibodies that react with the target antigens, while the Test Line indicates the presence of specific cancer biomarkers. A critical element of the LFIA strip, the control line (201) ensures the operational validity of the test. It appears as a distinct marker when the strip functions correctly, confirming that the reagents and sample migration process are appropriate.
At the end of the strip, an absorption pad collects residual fluid to ensure continuous sample flow and prevent backflow that might compromise the results. The sequential arrangement of test lines enables multiplexed analysis, allowing simultaneous detection of multiple biomarkers from a single sample, significantly enhancing diagnostic efficiency by reducing the need for multiple separate tests. The integration of this multiplexed design with high-resolution imaging and AI analysis further improves the diagnostic capabilities of the LFIA device, enabling efficient and accurate cancer screening in clinical and point-of-care settings.
Integrated Data Capturing and High-Resolution Imaging Module
According to further embodiment of the present invention, the data capturing module integrates modern digital technologies to facilitate efficient data management and analysis across diverse healthcare environments. The system supports multiple platforms, including mobile devices, tablets, desktop computers, and other compatible digital interfaces, making it adaptable for use in clinical, point-of-care, and remote testing settings. These devices act as the primary medium for acquiring diagnostic and demographic data.
The system allows for a variety of data acquisition methods, such as manual data entry, pre-filled forms, and integrated scanning tools, ensuring that the device can handle various data sources, including handwritten notes and digital records. It further supports input versatility, accepting a wide range of formats, including voice inputs (via voice commands or recordings), text inputs (manual or pre-existing text files), scan inputs (scanning physical records or lab reports), and visual inputs (high-resolution images and screenshots of medical records). This enables comprehensive data capture for a more complete diagnostic analysis.
Additionally, the system is designed to seamlessly integrate pre-existing medical and demographic data, allowing for a more holistic view of the patient’s condition. The combination of historical data with current diagnostic results improves the overall accuracy and depth of the analysis, providing valuable context for interpreting biomarkers.
The captured data, including high-resolution images of the LFIA strip, is securely transmitted to the Cloud AI system for storage, enabling centralized data management. This cloud-based system facilitates real-time access to diagnostic information, ensuring efficient retrieval and reporting. By leveraging machine learning models, the system enhances diagnostic accuracy and improves the speed of the analysis, making it an essential tool for early-stage cancer detection.
According to a further embodiment of the invention, the high-resolution imaging unit plays a critical role in the overall diagnostic process. It captures precise visuals of the LFIA strip, utilizing advanced optical components to detect and analyze test line intensities. The imaging unit is specifically designed to capture faint signals that might represent low-concentration biomarkers, ensuring heightened sensitivity. The inclusion of fluorescence detection technology improves the ability to identify subtle variations in biomarker levels, which is crucial for early-stage cancer diagnosis.
The high-resolution imaging system supports both qualitative and quantitative methods, employing fluoroscopy, mobile, or digital cameras, making it adaptable to various healthcare environments. This flexibility allows the device to be used in diverse clinical settings, from hospitals to remote testing locations. The captured image data, along with relevant patient information, is then securely transmitted to the cloud, where it is stored and analyzed for real-time access.
The system also integrates patient information, such as demographics, medical history, and reported symptoms, providing a comprehensive and personalized diagnostic analysis. By linking the image data with the patient’s health profile, the system ensures that diagnostic reports are tailored to the individual’s specific medical background, improving the accuracy and relevance of the final results. This integrated approach ensures that the diagnostic process is not only efficient but also personalized, enabling healthcare providers to make informed, data-driven decisions for optimal patient care.
Artificial Intelligence (AI) and Symptom Analysis Module
According to a further embodiment of the present invention, the AI module plays a pivotal role in automating the analysis of test results, significantly enhancing diagnostic precision and consistency. Sophisticated image processing algorithms within the AI module analyze the visuals captured by the imaging system, quantifying the intensity of test lines and calculating biomarker concentrations with high precision. The module is configured to compare quantified Antigen / Antibodies / Proteins concentrations against predefined diagnostic cut-off values to assess cancer risk and generate an objective cancer risk profile that indicate potential cancer progression. This feature ensures that the system provides accurate cancer risk assessments based on the latest clinical standards.
Furthermore, the AI module is integrated with machine learning capabilities, which continuously improve the system’s analytical performance. By adapting to historical datasets and refining its models over time, the AI module ensures the system remains accurate and reliable as it encounters new data patterns. This machine learning integration enables the device to stay up-to-date with emerging trends in cancer biomarker identification, thereby refining diagnostic accuracy with every new data set.
In addition to analyzing biomarker data, the AI module incorporates a non-linear regression model to refine Antigen / Antibodies / Proteins concentration measurements and risk scoring based on patient-specific demographic, genetic, and omics data. This integration allows for a comprehensive, personalized risk evaluation, which not only assesses cancer biomarkers but also factors in the patient’s unique health profile. By considering the full spectrum of available data, the system enhances the diagnostic process and enables more tailored decision-making.
According to a further embodiment, the symptom analysis module is designed to collect and integrate patient-specific information, adding a personalized dimension to the diagnostic process. This module allows patients to input relevant details, including demographics (such as age and gender), family and medical history, current symptoms, lifestyle factors, and genetic profiles. By correlating this detailed patient data with the biomarker results, the symptom analysis module refines the overall diagnostic accuracy and provides a nuanced risk assessment tailored to the individual’s specific health context.
Report Generation
According to further embodiment of the present invention, the automated reporting system consolidates all diagnostic findings into a detailed and actionable report for healthcare professionals. The report integrates results from the LFIA strip, AI-driven analysis, and patient-reported data to present a comprehensive diagnostic profile. It features graphical and tabular visualizations to improve interpretability, alongside actionable clinical recommendations based on established diagnostic thresholds. These recommendations guide further diagnostic tests or interventions, ensuring timely and accurate management of the patient’s condition.
The diagnostic system generates reports in multiple formats to meet the diverse needs of both clinical and patient scenarios. These reports provide detailed insights, including biomarker detection, where quantitative and qualitative analyses of cancer biomarkers are presented. Additionally, the reports feature risk scoring, which includes both numerical and graphical representations of cancer risk based on the biomarkers and patient data. The system also includes comparative analysis, which benchmarks patient results against standard and historical datasets to enhance the accuracy of the diagnostic evaluation.
The final report compiles all relevant information in a concise and actionable format. This final report includes an overall risk score, which is a cumulative assessment combining biomarker levels, medical history, and demographic data. Furthermore, it presents detailed parameters, offering scores and analyses for factors such as occupational exposure, smoking, hormone therapy, family history, and more. To assist healthcare professionals, the report also includes personalized recommendations, providing tailored clinical advice for further testing or preventive measures.
To enhance the interpretability of the report, visual and tabular representations are integrated, including graphs and percentile-based scoring. These features enable healthcare providers to quickly assess the patient’s condition, offering a clear and comprehensive overview that aids in informed decision-making. The report evaluates a range of scoring parameters, such as medical history (including chronic illnesses, previous treatments, and surgeries), genetic reports (including insights from omics data and genetic testing), lifestyle factors (such as smoking, alcohol consumption, dietary habits, and physical activity), environmental and occupational exposures (including risks from radiation, pollutants, or carcinogens), and symptoms and observations (correlating current health complaints with biomarker results).
This robust reporting system is designed to provide a holistic view of the patient’s diagnostic profile, empowering healthcare professionals to deliver more targeted, personalized, and effective care. It emphasizes precision, clarity, and usability, ensuring that complex diagnostic data is presented in a format that is easily interpretable for clinical use.
The said system’s workflow is meticulously designed to ensure seamless and efficient cancer screening. The process begins with sample collection and application, where a biological fluid is applied to the sample pad (300) and stabilized with a buffer solution. As the sample migrates along the LFIA strip, Immune complex (antigen-antibody interactions) occur at the test lines (202–211), generating visible markers indicative of biomarker presence. These results are captured by the high-resolution imaging system, which produces detailed visuals of the test and control lines.
The captured data is then processed by the AI module, which quantifies biomarker concentrations, calculates risk scores, and integrates this information with patient-reported data for a comprehensive evaluation. Finally, the automated reporting system generates a diagnostic report summarizing the findings and providing clinical recommendations.
This multi-antigen lateral flow immunoassay system for cancer screening and early detection leverages advanced multiplexing, high-resolution imaging, AI analysis, and automated reporting to provide a transformative cancer screening solution. Its modular design and scalability make it suitable for diverse healthcare applications, including mass screening programs, clinical diagnostics, and home testing kits. By addressing the limitations of existing technologies, this invention provides a transformative solution for early cancer detection, ensuring improved accessibility, efficiency, and diagnostic accuracy.
, Claims:We Claim:
1. A multi-antigen lateral flow immunoassay device with AI-driven risk assessment for cancer and inflammatory conditions, the said device comprising:
a) a multi-antigen LFIA test strip configured to detect cancer and inflammatory condition biomarkers from a biological sample, the test strip consisting of:
- a sample pad (300) for applying the biological sample;
- test lines (202-211), each immobilized with antibodies specific to a cancer biomarker, the biomarkers including Prostate-Specific Antigen (PSA), Carcinoembryonic Antigen (CEA), Cancer Antigen 125 (CA-125), Alpha-Fetoprotein (AFP), Carbohydrate Antigen 19-9 (CA 19-9), Human Epididymis Protein 4 (HE4), Cancer Antigen 15-3 (CA15.3), Cytokeratin-19 Fragment (CYFRA 21-1), Neuron-Specific Enolase (NSE), and Squamous Cell Carcinoma (SCC) Antigen;
- a control line (201) to validate the functionality of the test strip; and
- an absorption pad to facilitate fluid migration;
b) a protective housing made of medical-grade material to enclose the test strip, the housing having a transparent cover;
c) an imaging unit configured to capture images of the LFIA strip, the imaging unit consisting of a fluorescence detection module; and an optical system for image clarity;
d) an artificial intelligence (AI) module operatively connected to the imaging unit, the AI module configured to: process captured images to quantify the intensity of test and control lines; compare quantified biomarker concentrations against predefined diagnostic thresholds; and generate a risk assessment based on the biomarker concentrations;
e) a symptom analysis module configured to collect and integrate patient data, including demographic details, medical history, family history, symptoms, and omics data;
f) an automated reporting module configured to generate a diagnostic report consisting of biomarker concentrations; cancer risk scores; and actionable recommendations for further diagnostic tests or treatment.
2. The multi-antigen lateral flow immunoassay (LFIA) device as claimed in Claim 1, wherein the sample pad is configured to accommodate various biological samples, including blood, serum, plasma, cerebrospinal fluid, ascitic fluid, and pleural fluid.
3. The multi-antigen lateral flow immunoassay (LFIA) device as claimed in Claim 1, wherein the imaging unit supports fluorescence-based detection for enhanced sensitivity and the detection of low biomarker concentrations.
4. The multi-antigen lateral flow immunoassay (LFIA) device as claimed in Claim 1, wherein the AI module further incorporates machine learning algorithms that adapt to historical data to improve diagnostic accuracy over time.
5. The multi-antigen lateral flow immunoassay (LFIA) device as claimed in Claim 1, wherein the automated reporting module provides graphical and tabular representations of biomarker concentrations and cancer risk scores.
6. The multi-antigen lateral flow immunoassay (LFIA) device as claimed in Claim 1, wherein the automated reporting module recommends follow-up diagnostic tests based on internationally accepted thresholds for cancer biomarkers.
7. The multi-antigen lateral flow immunoassay (LFIA) device as claimed in Claim 1, wherein the test strip housing includes a disposable design for single-use applications, ensuring sterility.
8. The multi-antigen lateral flow immunoassay (LFIA) device as claimed in Claim 1, wherein the AI module integrates symptom data, demographic data, and omics data with biomarker results to provide a personalized risk profile.
9. The multi-antigen lateral flow immunoassay (LFIA) device as claimed in Claim 1, wherein the generated report includes comparisons of patient results against community datasets and scientific literature for enhanced diagnostic insights.