Description:FIELD OF THE INVENTION
This invention relates to system and method for ai-driven early detection of oral diseases using image and symptom-based analysis.
BACKGROUND OF THE INVENTION
Oral disease infects almost 3.5 billion individuals worldwide and nearly all are diagnosed too late due to deficiency of dentists in rural areas, high price of diagnosis, and unavailability of time. Low dental ratio to patient ratio in rural India is a main issue where initial diagnosis is not an easily attainable goal. The conventional approach is based on clinic visits, long diagnostics, and waiting for expert opinion, resulting in delay and poor outcome. The patients also end up ignoring early disease signs due to ignorance. The innovation aims to counter these by making the possibility of empowering users as an AI-based system that facilitates early screening and early professional intervention. It shortens diagnosis time, cost, and accessibility hurdles as well as enhances public oral health outcomes by active surveillance.
US20250218560: The present invention provides a system for enhanced oral health and disease prevention. The system comprises a computing device configured to execute modules, such as a tooth-brushing data module to assess brushing frequency and duration, an oral microbiome analysis module for analyzing saliva samples, a plaque assessment module to evaluate plaque levels through images or videos, a breath analysis module for assessing oral odor of the user, a data triangulation module to generate an oral health scorecard, and a preventive care recommendation module to create personalized oral care plans that include tailored schedules for professional cleaning appointments. The system includes a smart contract module to enforce compliance with the personalized oral care plans, thereby offering incentives or cost adjustments. The system promotes improved oral hygiene practices, early detection of oral diseases, and proactive dental care management.
US20250226109: Exemplary embodiments of the present disclosure are directed towards artificial intelligence-based system for automated preventative health screening and data-driven health risk analysis. The system integrates Generative AI with the CDC's Syndemic Model to enable anonymous, stigma-free health screening and care linkage. The system leverages oral health as neutral entry point to assess interconnected risks across sexual, mental, and behavioral health domains. Using Large Language Models trained on validated sources and AI-based image analysis of oral and skin abnormalities, system computes “Chance screening Scores” to classify risks for communicable (e.g., HIV, STIs, Mpox) and non-communicable diseases (e.g., diabetes, hypertension). Accessible through mobile apps/QR codes without requiring login, system generates Unique IDs for test kits, telehealth services, and rewards. Features include actionable recommendations, gamified Stigma Meter, geographic insights, language translation, and data control options. This innovative solution fosters informed decision-making, reduces stigma, and empowers users to manage their health confidentially and effectively.
Oral diseases such as gingivitis, dental caries, and oral cancer affect billions globally, with most cases detected late due to lack of dentists in rural areas, high diagnostic costs, and limited awareness. Existing systems rely heavily on in-clinic visits, radiographs, or high-end equipment, making them inaccessible to underserved populations. Moreover, current AI solutions are image-dependent and lack integration with symptom-based data, limiting diagnostic accuracy. The present invention solves these problems by providing an AI-powered system that combines intraoral image analysis with patient symptom reporting through mobile or web-based platforms. It enables early, affordable, explainable, and accessible oral disease detection, reducing dependency on experts while empowering individuals to self-screen and receive timely professional intervention.
SUMMARY OF THE INVENTION
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 and nor is it intended for determining the scope of the invention.
The invention discloses a system and method for AI-driven early detection of oral diseases using a combination of image-based analysis and symptom-based assessment. The system integrates smartphone or intraoral camera images with structured symptom inputs to detect conditions such as dental caries, gingivitis, and oral cancer at high accuracy levels.
A hybrid AI model comprising convolutional neural networks (CNNs) for image feature extraction and decision tree-based classifiers for symptom interpretation is employed. The outputs are fused using ensemble learning, generating a comprehensive diagnostic result. The invention also incorporates attention mechanisms and explainable AI (XAI) modules, which provide transparency in predictions and highlight the features contributing to diagnostic outcomes.
The system is deployed on mobile or web platforms with multilingual and voice-input support, enabling usability across diverse populations. Cloud infrastructure ensures secure storage, periodic model retraining, and integration with healthcare systems through APIs.
By empowering individuals with accessible, low-cost screening tools, the invention enhances oral health outcomes, reduces diagnostic delays, and improves healthcare accessibility in underserved areas.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is 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.
The invention provides an enormous system based on AI with early oral disease detection and image data (intraoral images) and received patient-symptom reports identification. The invention involves the utilization of smart algorithms and deep neural models for diseases such as gingivitis, dental caries, oral cancer, and other usual disorders at high percentage accuracies. Such a system is provided via web or mobile-based platform, and it enables individuals to screen themselves at home. The hybrid input mechanism improves diagnostic accuracy and reduces dependence on expensive and time-consuming specialist consultations, promoting oral care affordability and accessibility, especially among the underprivileged group.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Oral health problems represent a major global health burden, with billions of individuals suffering from preventable or treatable conditions. Conventional diagnostic approaches rely on professional expertise, radiographs, and lab-based testing, which are costly, time-consuming, and inaccessible to rural communities.
The present invention introduces an AI-driven platform that integrates both intraoral imaging and symptom-based reporting to achieve accurate early detection of oral diseases.
The image acquisition module allows users to capture oral cavity images using a smartphone or intraoral camera. Images are preprocessed through de-noising and normalization to remove artifacts and standardize quality.
The symptom input interface enables patients to enter symptoms via text, checklist forms, or voice input. The interface supports multilingual communication, ensuring inclusivity across diverse demographics.
A hybrid AI engine processes the multimodal input. Convolutional Neural Networks extract features from oral cavity images, identifying visual indicators such as lesions, discoloration, and cavities. A symptom-based classifier analyzes patient-reported information.
A fusion module combines outputs from both the image and symptom classifiers using ensemble learning techniques. This generates a final diagnostic prediction along with a confidence score.
To enhance interpretability, an explainable AI layer highlights critical image features and symptom markers that influenced the diagnosis. This builds trust among users and clinicians.
A decision support module provides diagnostic outcomes along with next-step recommendations such as self-care, dental consultation, or urgent referral.
The invention incorporates a feedback loop, collecting user and expert feedback to continuously retrain models. Federated learning ensures that updates can occur without compromising data privacy.
The communication protocols include Wi-Fi, 4G/5G, or Bluetooth to synchronize data between user devices and cloud servers.
A backend infrastructure ensures secure cloud storage, periodic model updates, and API integration with electronic health records (EHR) and telemedicine platforms.
The system supports alternative embodiments, such as wearable intraoral cameras, intelligent toothbrushes, or integration into telemedicine platforms. It can also function offline in rural areas with poor connectivity.
The invention offers multiple benefits: early detection of oral diseases, cost reduction, scalability across populations, and improved public oral health outcomes.
Unlike prior systems limited to image-only analysis, the present invention leverages multimodal fusion for improved accuracy and inclusivity.
Its scalability ensures deployment in individual households, community health centers, and large-scale telehealth programs, supporting preventive healthcare and mass screening initiatives.
The invention provides an enormous system based on AI with early oral disease detection and image data (intraoral images) and received patient-symptom reports identification. The invention involves the utilization of smart algorithms and deep neural models for diseases such as gingivitis, dental caries, oral cancer, and other usual disorders at high percentage accuracies. Such a system is provided via web or mobile-based platform, and it enables individuals to screen themselves at home. The hybrid input mechanism improves diagnostic accuracy and reduces dependence on expensive and time-consuming specialist consultations, promoting oral care affordability and accessibility, especially among the underprivileged group.
Best Method of Working
The best method of working involves deploying the invention as a mobile-first application. Users capture intraoral images with smartphone cameras, enter symptoms via text or voice, and upload data through a secure interface. The AI engine extracts features from images and symptoms, processes them through the fusion model, and generates diagnostic outputs with confidence scores. Results are displayed with explanatory visualizations and suggested actions. Data is synchronized with a cloud backend for long-term monitoring, model retraining, and integration with healthcare systems. This configuration ensures wide accessibility, low-cost deployment, and real-time feedback.
SIX STEPWISE WORKING FUNCTIONALITY
1.Data Collection: Patient takes oral cavity photo on smart phone and inputs symptoms in guided fashion.
2.Preparation: Software de-noises, images is solved, and data is normalized for justice.
3.Feature Generation: CNN extracts informative visual features whereas symptom-based model extracts indicative textual/sensory features.
4.Multi-Modal Processing: Information from images and symptoms is merged by ensemble models to produce an exhaustive diagnosis.
5. Diagnosis
The system shows the diagnosed state, degree of confidence, and suggested next step (e.g., visit a dentist).
6. Learning and Feedback: User and expert feedback are collected to update and sharpen the model to fine-tune it perpetually.
ADVANTAGES OF THE INVENTION
• Facilitates preventive treatment and clinical load reduction.
• Reduces healthcare costs and oral diagnostics makes it economical.
• Saves on redundant travel and carbon footprints through remote screening.
• Empowers poor and rural with cheap diagnostics.
• Improves public health facilities with early detection and reporting of diseases.
The proposed invention is significant in how it blends multi-modal input—merging real-time image data with user-entered measurements of symptoms—to maximally lead to diagnostic accuracy. Compared to other devices that are visually inspection dependent or require expert intervention, the system here integrates self-diagnosis and a machine learning environment trained on a large data set. The model makes use of attention mechanisms for feature importance and explainable AI methods to render transparency. System flexibility allows it to get better over time using federated learning. Also, mobile-first technology has mass reachability, and its diagnostic suggestions adhere to standard dental practice. End-to-end integration and flexibility make the solution innovative and feasible.
, Claims:1. A system for AI-driven early detection of oral diseases, comprising:
a) an image acquisition module using a smartphone or intraoral camera;
b) a symptom input interface supporting text, checklist, or voice input;
c) a preprocessing unit for de-noising and normalizing image and symptom data;
d) a hybrid AI engine comprising convolutional neural networks for image analysis and decision tree-based classifiers for symptom analysis;
e) a fusion module for combining outputs of the AI engine;
f) an explainable AI layer to highlight diagnostic features;
g) a decision support module for generating diagnostic outcomes and recommendations;
h) a feedback loop for periodic model updates using federated learning;
i) a backend infrastructure including secure cloud storage and APIs for healthcare integration; and
j) a communication module employing Wi-Fi, 4G/5G, or Bluetooth.
2. A method for AI-driven early detection of oral diseases using the system as claimed in claim 1, comprising:
a) capturing intraoral images and collecting patient symptoms;
b) preprocessing the data to standardize quality;
c) analyzing images using convolutional neural networks and symptoms using classifiers;
d) fusing the analysis outputs into a unified diagnostic prediction;
e) generating explainable diagnostic outputs;
f) providing decision support recommendations; and
g) updating models periodically through feedback and federated learning.
3. The system as claimed in claim 1 or the method as claimed in claim 2, wherein the image acquisition module supports smartphone or wearable intraoral cameras.
4. The system as claimed in claim 1 or the method as claimed in claim 2, wherein the symptom input interface supports multilingual and voice-based inputs.
5. The system as claimed in claim 1 or the method as claimed in claim 2, wherein the explainable AI layer provides visual heatmaps and symptom importance rankings.
6. The system as claimed in claim 1 or the method as claimed in claim 2, wherein the decision support module provides geo-tagged alerts and dentist referral suggestions.
7. The system as claimed in claim 1 or the method as claimed in claim 2, wherein the feedback loop updates diagnostic models using clinician validation and user feedback.
8. The system as claimed in claim 1 or the method as claimed in claim 2, wherein the backend infrastructure integrates with electronic health records and telemedicine platforms.
9. The system as claimed in claim 1 or the method as claimed in claim 2, wherein the communication module enables offline functionality with later synchronization.
10. The system as claimed in claim 1 or the method as claimed in claim 2, wherein the platform reduces diagnostic delays and increases accessibility in rural populations.