SMART PHONE-BASED FUNDUS IMAGING AND TRANSFORMER

Abstract

ABSTRACT “SMART PHONE-BASED FUNDUS IMAGING AND TRANSFORMER” The present invention provides smart phone-based fundus imaging and transformer for cost-effective, early detection of retinal diseases in low-resource settings. The system integrates an attachable imaging device with a smartphone, featuring a dual LED illumination system (infrared and white light) and a replaceable lens for clear retinal images, even in low-light conditions. Powered by an independent battery, the device utilizes the AEResNet50 machine learning model for accurate disease diagnosis. This user-friendly solution is designed for non-professionals, offering accessible, portable, and affordable retinal disease detection, particularly for underserved populations and remote healthcare settings. Figure 1

Specification

Description:TECHNICAL FIELD
[0001] The present invention relates to the field of imaging and transformer systems, and more particularly, the present invention relates to the smart phone-based fundus imaging and transformer.
BACKGROUND ART
[0002] The following discussion of the background of the invention is intended to facilitate an understanding of the present invention. However, it should be appreciated that the discussion is not an acknowledgment or admission that any of the material referred to was published, known, or part of the common general knowledge in any jurisdiction as of the application's priority date. The details provided herein the background if belongs to any publication is taken only as a reference for describing the problems, in general terminologies or principles or both of science and technology in the associated prior art.
[0003] Problem in the existing product:
- Existing devices are bulky and less mobile while our device is compact and portable, ideal for remote areas.
- Current products are costly while our solution is cost-effective, improving accessibility in low-resource settings.
- Many devices require specialized training
- Traditional devices struggle in poor lighting
[0004] In light of the foregoing, there is a need for Smart phone-based fundus imaging and transformer that overcomes problems prevalent in the prior art associated with the traditionally available method or system, of the above-mentioned inventions that can be used with the presented disclosed technique with or without modification.
[0005] All publications herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies, and the definition of that term in the reference does not apply.
OBJECTS OF THE INVENTION
[0006] The principal object of the present invention is to overcome the disadvantages of the prior art by providing smart phone-based fundus imaging and transformer.
[0007] Another object of the present invention is to provide smart phone-based fundus imaging and transformer that provides a low-cost alternative using a smartphone-based system.
[0008] Another object of the present invention is to provide smart phone-based fundus imaging and transformer that can be used by minimally trained individuals, increasing reach in underserved areas.
[0009] Another object of the present invention is to provide smart phone-based fundus imaging and transformer that enables quicker retinal screening and faster results.
[0010] Another object of the present invention is to provide smart phone-based fundus imaging and transformer that can easily be deployed across remote and resource-limited regions.
[0011] The foregoing and other objects of the present invention will become readily apparent upon further review of the following detailed description of the embodiments as illustrated in the accompanying drawings.
SUMMARY OF THE INVENTION
[0012] The present invention relates to smart phone-based fundus imaging and transformer that addresses the pressing need for expedited and cost-effective detection of retinal diseases in low-resource settings, where current solutions frequently require costly equipment and professional expertise. This innovative solution integrates an attachable retinal imaging device with a smartphone, making it both economical and user-friendly. The device as shown the figure below offers a stable platform for obtaining clear retinal images in low-light environments, with enhanced focus capability through a replaceable lens system tailored to individual ocular needs. A dual LED illumination system, which includes both infrared (IR) and white light LEDs, ensures sufficient lighting without causing pupil constriction. Additionally, the device is powered by an independent battery system, eliminating the need to rely on the smartphone's power supply.
[0013] The detection process utilizes a smartphone-based machine learning model, AEResNet50, to diagnose retinal diseases with high accuracy. This model combines ResNet50's hierarchical feature extraction with the Transformer's self-attention mechanism, enabling it to capture both local and global retinal features. By analyzing key areas like the optic disc, blood vessels, and macular region from preprocessed fundus images, the model achieves exceptional performance, with 98.79% accuracy and a 99.53% AUC-ROC score. This efficient integration of advanced machine learning with smartphone technology provides a cost-effective, accessible solution for early detection of retinal diseases, particularly benefiting underserved regions.
[0014] In conclusion, our invention offers an accessible, cost-effective solution for early retinal disease detection, particularly in low-resource areas. By integrating smartphone technology with a portable imaging device and advanced machine learning, we enable efficient and accurate diagnosis, promoting timely medical intervention and improving overall healthcare accessibility.
[0015] While the invention has been described and shown with reference to the preferred embodiment, it will be apparent that variations might be possible that would fall within the scope of the present invention.
BRIEF DESCRIPTION OF DRAWINGS
[0016] So that the manner in which the above-recited features of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may have been referred by embodiments, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only typical embodiments of this invention and are therefore not to be considered limiting of its scope, for the invention may admit to other equally effective embodiments.
[0017] These and other features, benefits, and advantages of the present invention will become apparent by reference to the following text figure, with like reference numbers referring to like structures across the views, wherein:
[0018] Figure-1: The retinal imaging device used in our study.
[0019] Figure-2: Flowchart representation of the model.
DETAILED DESCRIPTION OF THE INVENTION
[0020] While the present invention is described herein by way of example using embodiments and illustrative drawings, those skilled in the art will recognize that the invention is not limited to the embodiments of drawing or drawings described and are not intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and the detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the scope of the present invention as defined by the appended claim.
[0021] As used throughout this description, the word "may" is used in a permissive sense (i.e. meaning having the potential to), rather than the mandatory sense, (i.e. meaning must). Further, the words "a" or "an" mean "at least one" and the word "plurality" means "one or more" unless otherwise mentioned. Furthermore, the terminology and phraseology used herein are solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and additional subject matter not recited, and is not intended to exclude other additives, components, integers, or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents, acts, materials, devices, articles, and the like are included in the specification solely for the purpose of providing a context for the present invention. It is not suggested or represented that any or all these matters form part of the prior art base or were common general knowledge in the field relevant to the present invention.
[0022] In this disclosure, whenever a composition or an element or a group of elements is preceded with the transitional phrase "comprising", it is understood that we also contemplate the same composition, element, or group of elements with transitional phrases "consisting of", "consisting", "selected from the group of consisting of, "including", or "is" preceding the recitation of the composition, element or group of elements and vice versa.
[0023] The present invention is described hereinafter by various embodiments with reference to the accompanying drawing, wherein reference numerals used in the accompanying drawing correspond to the like elements throughout the description. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiment set forth herein. Rather, the embodiment is provided so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those skilled in the art. In the following detailed description, numeric values and ranges are provided for various aspects of the implementations described. These values and ranges are to be treated as examples only and are not intended to limit the scope of the claims. In addition, several materials are identified as suitable for various facets of the implementations. These materials are to be treated as exemplary and are not intended to limit the scope of the invention.
[0024] The present invention relates to smart phone-based fundus imaging and transformer that addresses the pressing need for expedited and cost-effective detection of retinal diseases in low-resource settings, where current solutions frequently require costly equipment and professional expertise. This innovative solution integrates an attachable retinal imaging device with a smartphone, making it both economical and user-friendly. The device as shown the figure below offers a stable platform for obtaining clear retinal images in low-light environments, with enhanced focus capability through a replaceable lens system tailored to individual ocular needs. A dual LED illumination system, which includes both infrared (IR) and white light LEDs, ensures sufficient lighting without causing pupil constriction. Additionally, the device is powered by an independent battery system, eliminating the need to rely on the smartphone's power supply.
[0025] The detection process utilizes a smartphone-based machine learning model, AEResNet50, to diagnose retinal diseases with high accuracy. This model combines ResNet50's hierarchical feature extraction with the Transformer's self-attention mechanism, enabling it to capture both local and global retinal features. By analyzing key areas like the optic disc, blood vessels, and macular region from preprocessed fundus images, the model achieves exceptional performance, with 98.79% accuracy and a 99.53% AUC-ROC score. This efficient integration of advanced machine learning with smartphone technology provides a cost-effective, accessible solution for early detection of retinal diseases, particularly benefiting underserved regions.
[0026] In conclusion, our invention offers an accessible, cost-effective solution for early retinal disease detection, particularly in low-resource areas. By integrating smartphone technology with a portable imaging device and advanced machine learning, we enable efficient and accurate diagnosis, promoting timely medical intervention and improving overall healthcare accessibility.
[0027] Unique features of our solution include the following:
- Integrates an attachable retinal imaging device with a smartphone, significantly reducing costs and making it accessible in low-resource settings.
- Combines a smartphone with a specially designed imaging device, making it easy to use for non-professionals and enhancing accessibility.
- Features a stable platform for clear retinal images in low-light environments, supported by a replaceable lens system tailored to individual ocular needs.
- Utilizes the AEResNet50 model, combining ResNet50's hierarchical feature extraction with the Transformer's self-attention mechanism, achieving high accuracy (98.79%) and a high AUC-ROC score (99.53%).
- Specifically designed to meet the needs of underserved communities, providing a cost-effective and accessible option for early detection of retinal diseases where resources are limited.
[0028] Advantages:
- Cost efficiency due to the integration of a smartphone with an attachable retinal imaging device, significantly lowering the overall cost and making it more accessible for users in low-resource settings.
- Portability, allowing for easy transportation and use in various locations, facilitating on-the-go diagnostics.
- User-friendly operation, enabling non-professionals to effectively use the device, reducing the need for extensive training.
- Enhanced imaging quality, providing clear retinal images in low-light environments through a stable imaging platform and tailored lens system.
- Advanced diagnostic accuracy, with the AEResNet50 machine learning model enabling high precision in detecting retinal diseases.
- Versatile illumination from a dual LED system, ensuring optimal lighting conditions without causing pupil constriction
[0029] Innovative features:
- Smartphone integration for compatibility with smartphones, enhancing portability and accessibility.
- Dual LED illumination system that combines infrared (IR) and white light to provide optimal lighting conditions without causing pupil constriction.
- AI-powered diagnosis using the AEResNet50 model, which integrates ResNet50 and Transformer technologies for highly accurate detection of retinal diseases directly on the device.
- User-friendly interface designed for non-professionals to operate the device effectively.
- Cost-effective alternative to traditional retinal imaging devices, making it accessible to underserved populations.
- Enhanced image processing with advanced algorithms for image preprocessing and analysis, ensuring high-quality output for accurate diagnostics.
[0030] Applications of the invention:
- Early Detection of Retinal Diseases: Facilitates the timely diagnosis of conditions such as diabetic retinopathy, glaucoma, and age-related macular degeneration.
- Remote Healthcare: Enables healthcare providers to assess patients in low-resource or remote settings, expanding access to eye care services.
- Telemedicine Applications: Supports virtual consultations by allowing healthcare professionals to analyze retinal images from patients' homes.
- Research and Data Collection: Provides a source of retinal images for academic and clinical research, contributing to studies on eye health and disease progression.
- Training and Education: Serves as a practical tool for training medical students and healthcare professionals in retinal imaging and diagnostics.
- Routine Eye Exams: Can be used for regular eye check-ups, promoting proactive eye health management.
- Monitoring Disease Progression: Assists in tracking the progression of retinal diseases over time, helping clinicians adjust treatment plans accordingly.
- Community Health Programs: Can be integrated into public health initiatives aimed at improving eye health awareness and screening in underserved populations.
- Data Sharing for Collaborative Studies: Facilitates collaboration between institutions by enabling sharing of anonymized retinal data for joint research projects.
- Cost-Effective Eye Care Solutions: Offers a more affordable option for eye screening, making eye care services accessible to a broader population.
[0031] Various modifications to these embodiments are apparent to those skilled in the art from the description and the accompanying drawings. The principles associated with the various embodiments described herein may be applied to other embodiments. Therefore, the description is not intended to be limited to the 5 embodiments shown along with the accompanying drawings but is to be providing the broadest scope consistent with the principles and the novel and inventive features disclosed or suggested herein. Accordingly, the invention is anticipated to hold on to all other such alternatives, modifications, and variations that fall within the scope of the present invention and appended claims. , Claims:CLAIMS
We Claim:
1) A smartphone-based retinal imaging system for the early detection of retinal diseases, the system comprising:
- An attachable imaging device configured to be attached to a smartphone for capturing fundus images;
- A replaceable lens system configured to enhance focus and accommodate individual ocular needs;
- A dual LED illumination system with infrared (IR) and white light LEDs to ensure optimal lighting conditions without causing pupil constriction;
- A power supply unit independent from the smartphone to power the imaging device.
2) The system as claimed in claim 1, wherein the system further comprising a smartphone-based machine learning model, AEResNet50, designed for retinal disease diagnosis, integrating ResNet50 hierarchical feature extraction with the Transformer's self-attention mechanism for enhanced diagnostic accuracy.
3) The system as claimed in claim 1, wherein the device is designed for use by non-professionals, providing an easy-to-use interface that enables effective diagnosis without requiring extensive training.
4) The system as claimed in claim 1, wherein the imaging device and machine learning model are designed to work in low-resource settings, providing a cost-effective solution for early detection of retinal diseases in underserved populations.
5) The system as claimed in claim 1, wherein the attachable imaging device is powered by an independent battery, which eliminates the need for the smartphone's power supply.
6) A method for diagnosing retinal diseases, the method comprising the steps of:
- Attaching the retinal imaging device to a smartphone;
- Capturing fundus images using the dual LED illumination system and the replaceable lens system;
- Preprocessing the captured images for input into the AEResNet50 machine learning model;
- Analyzing the images using the AEResNet50 model to detect retinal diseases with high diagnostic accuracy.

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