Tissue Classification via Transient Optical Imaging
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Solution Overview
Problem
Current methods for classifying biological tissue, such as cervical lesions, using colposcopy and artificial intelligence, face limitations in sensitivity and specificity, particularly in identifying precancerous or cancerous regions, which can lead to inaccurate biopsy placement and treatment decisions.
Innovation Solution
A computing system that captures multiple images of biological tissue during the transient optical effects caused by a pathology differentiating agent like acetic acid, processes these images using a machine learning algorithm, including deep neural networks, to provide precise classification and segmentation of tissue areas, enhancing sensitivity and specificity for cervical dysplasia and neoplasia identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple images are captured during transient optical effects and processed using machine learning algorithms, then measurement precision and reliability of tissue classification are improved, but device complexity and loss of time increase
Solution Approach 1:
The system segments the tissue classification problem into multiple discrete classification categories (e.g., normal, dysplastic, cancerous) and processes different image sequences through specialized machine learning models trained for specific tissue types or lesion characteristics, improving precision while managing complexity through modular architecture
Solution Approach 2:
The system performs preliminary actions by capturing multiple images during the transient optical effects of acetic acid application before final classification. This preliminary capture of temporal data allows the machine learning algorithm to analyze dynamic changes in tissue optical properties, enhancing classification accuracy without requiring additional complex hardware
2Measurement precision
If multiple images are captured during transient optical effects and processed using machine learning algorithms, then measurement precision and reliability of tissue classification are improved, but loss of time increases
Solution Approach 1:
The system employs periodic action by capturing images at specific time intervals during the transient optical effects of acetic acid application. This periodic sampling captures the dynamic optical changes without continuous imaging, improving classification precision while minimizing time loss through optimized sampling frequency
Solution Approach 2:
The system uses copying by processing digital image copies through machine learning algorithms rather than requiring physical tissue manipulation or repeated examinations. Multiple image copies are analyzed computationally to extract temporal features, improving measurement precision without additional time cost to the patient
3Reliability
If machine learning algorithms are used for tissue classification, then reliability of diagnosis is improved, but ease of operation decreases
Solution Approach 1:
The system implements self-service by enabling the machine learning algorithm to automatically perform tissue classification without requiring manual intervention for feature extraction or interpretation. The algorithm autonomously analyzes image sequences, extracts temporal features, and generates diagnostic classifications, improving reliability while maintaining ease of operation through automated workflows
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system significantly improves the accuracy of cervical tissue classification, allowing for more precise identification of precancerous or cancerous regions, potentially reducing the need for biopsies and improving treatment outcomes by providing detailed risk assessments.
Implementation Method 1
This causes a transient optical effect, specifically a whitening of the tissue, which can be viewed directly and also in a captured image
Implementation Method 2
transient and/or spectral analysis of the one or more captured images, particularly measurement of diffuse reflectance
Data Source
AI summary
A biological tissue is classified using a computing system. Image data comprising a plurality of images of an examination area of a biological tissue is received at the computing system. Each of the plurality of images is captured at different times during a period in which topical application of a pathology differentiating agent to the examination area of the tissue causes transient optical effects. The received image data is provided as an input to a machine learning algorithm operative on the computing system. The machine learning algorithm is configured to allocate one of a plurality of classifications to each of a plurality of segments of the tissue.