Spatio-Temporal Medical Image Analysis for Faster Lesion Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing computer-implemented systems for medical image analysis, such as endoscopy and capsule endoscopy, struggle to accurately detect and analyze features of interest in large volumes of images, leading to inefficiencies and potential misdiagnosis due to manual review processes.
Innovation Solution
A computer-implemented system utilizing spatio-temporal image analysis with local and global processing modules, including recurrent neural networks and temporal convolution networks, to intelligently process images from medical procedures, detecting features of interest and generating reports with numerical values and recommended actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of images is performed, then diagnostic accuracy can be maintained, but time consumption increases significantly
Solution Approach 1:
The patent replaces the manual mechanical review process with an automated computer-implemented system that uses machine learning models and image processing algorithms to detect features of interest, thereby maintaining diagnostic accuracy while significantly reducing time consumption
Solution Approach 2:
The patent introduces an intermediary automated analysis system that acts as a bridge between the captured images and final diagnosis, using spatio-temporal analysis and machine learning to assist physicians without completely replacing human judgment, thus maintaining accuracy while reducing time burden
2Measurement precision
If all images are analyzed individually, then detection accuracy improves, but processing efficiency decreases
Solution Approach 1:
The patent merges individual image analysis with spatio-temporal context by processing sequences of images together and utilizing information from multiple frames and spatial relationships, thereby maintaining high detection accuracy while improving processing efficiency through combined analysis
Solution Approach 2:
The patent segments the analysis process into local spatio-temporal processing of individual images or small groups, followed by global processing that integrates information across the entire sequence, allowing efficient processing while maintaining comprehensive detection accuracy
3Measurement precision
If spatio-temporal analysis is implemented, then feature detection accuracy improves, but computational complexity increases
Solution Approach 1:
The patent divides spatio-temporal analysis into separate local and global processing stages, where local processing handles individual image frames and global processing integrates temporal information, reducing computational complexity while maintaining detection accuracy
Solution Approach 2:
The patent performs preliminary local spatio-temporal processing on individual images or small groups before conducting global analysis, pre-processing and filtering information to reduce the computational burden of subsequent comprehensive analysis while preserving detection accuracy
Data Source
AI summary
A computer-implemented method is provided for detecting at least one feature of interest in images captured with an imaging device. The method includes receiving an ordered set of images from the captured images, the ordered set of images being temporally ordered and analyzing one or more subsets of the ordered set of images using a local spatio-temporal processing module, the local spatio-temporal processing module being configured to determine the presence of characteristics related to the at least one feature of interest in each image of each subset of images and to annotate the subset of images based on the determined characteristics in each image of each subset of images. The method further includes processing a set of feature vectors of the ordered set of images using a global spatio-temporal processing module, the global spatio-temporal processing module being configured to refine the determined characteristics associated with each subset of images, and calculating one or more values for each image using a timeseries analysis module, the numerical value being representative of the at least one feature of interest and calculated using the refined characteristics associated each subset of images and spatio-temporal information. Still further, the method may include generating a report, a data or electronic file, integration into another reporting system or electronic medical records, and/or generating an electronic display on the at least one feature of interest using the multiple values associated with each image of each subset of the ordered set of images.