Interactive Oral Cavity Photography for Accessible AI Cancer Screening
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Solution Overview
Problem
Current oral cancer screening methods face challenges due to the lack of public awareness and the diverse appearance of oral mucosal lesions, leading to delayed diagnoses, as most cases are only identified after they have advanced, despite the availability of specialized medical imaging techniques that are not suitable for widespread use.
Innovation Solution
An interactive oral cavity photography system combined with artificial intelligence image recognition, which guides users to capture images of specific oral cavity locations, applies pattern recognition algorithms to analyze these images, and provides risk level warnings through color lights, supporting comprehensive oral health monitoring and data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized medical imaging techniques (hyperspectral imaging, autofluorescence imaging, optical coherence tomography, endoscopic imaging, CT, or MR scans) are used for oral cancer detection, then measurement precision and reliability are improved, but device complexity and ease of operation deteriorate due to requiring specialized machinery not suitable for widespread use
Solution Approach 1:
The patent uses standard digital photography to create visual copies of oral cavity images, replacing the need for specialized medical imaging machinery. The system captures photographs using conventional cameras and processes them through AI algorithms to achieve cancer detection, effectively copying the diagnostic function from complex medical equipment to accessible photographic devices.
Solution Approach 2:
The patent replaces complex mechanical and optical imaging systems (hyperspectral imaging, optical coherence tomography, endoscopic imaging) with a digital photography-based system. By substituting specialized mechanical imaging equipment with standard digital cameras and AI software, the system achieves comparable diagnostic capability while dramatically improving accessibility and ease of use.
2Measurement precision
If clinical oral examination (COE) is performed by dental specialists, then measurement precision is improved, but productivity and ease of operation deteriorate due to long intervals between referrals and lack of public awareness
Solution Approach 1:
The patent enables patients to perform self-examinations using a smartphone application with AI-powered image analysis. The system allows individuals to capture their own oral cavity images and receive automated assessment, eliminating the need to wait for specialist appointments. This self-service capability dramatically increases screening frequency while maintaining detection accuracy through AI algorithms.
Solution Approach 2:
The patent introduces an AI-based image recognition system as an intermediary between patients and dental specialists. This intermediary process allows for preliminary screening and assessment of oral images, enabling more frequent checks without requiring constant specialist involvement. The AI mediator triages cases, identifying those needing specialist review while allowing low-risk cases to be monitored through self-examination.
3Productivity
If non-specialist healthcare providers and patients perform screenings, then productivity is improved, but measurement precision deteriorates due to inability to identify subtle visual signs of OSCC
Solution Approach 1:
The patent replaces human visual assessment capabilities with AI-based image recognition technology. Since non-specialists lack the trained ability to detect subtle oral lesions, the system substitutes their visual inspection function with machine learning algorithms that have been trained to identify cancerous patterns. This substitution maintains high detection accuracy while enabling widespread screening by non-specialists.
Solution Approach 2:
The patent creates a digital archive of oral cavity images with AI-generated annotations and assessments, copying the diagnostic function from specialist evaluation to automated image analysis. The system captures visual data and processes it through algorithms that replicate specialist-level detection capabilities, making accurate screening accessible to non-specialists through standardized image capture and analysis protocols.
Data Source
AI summary
An interactive oral cavity photography system and artificial intelligence image recognition oral cavity cancer screening method using the system is disclosed. The system involves a guiding module, configured to provide a reference schematic image of at least two different locations within an oral cavity; an image capture unit, communicatively connected to the guiding module and configured to capture and digitize at least two oral mucosal images of the patient based on a guide line corresponding to the reference schematic image; an artificial intelligence graphic recognition module, communicatively connected to the image capture unit and configured to receive the at least two oral mucosa images and generate a result through a graphic recognition algorithm; and a storage module, communicatively connected to the artificial intelligence graphic recognition module and configured to store the at least two oral mucosa images and the result.