Spatio-Temporal Medical Image Analysis for Faster Accurate Detection
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Solution Overview
Problem
Existing computer-implemented systems for medical image analysis, such as endoscopy and capsule endoscopy, struggle with accurately detecting features of interest and efficiently analyzing image sequences, leading to inefficient and potentially erroneous medical diagnoses.
Innovation Solution
A computer-implemented system using spatio-temporal image analysis with local and global processing modules, combined with recurrent neural networks and temporal convolution networks, to detect and refine features of interest in medical images, providing a report on their likelihood and generating actionable insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual examination of captured images is performed, then diagnostic accuracy can be maintained through human expertise, but the examination time becomes excessively long and inefficiency increases
Solution Approach 1:
The patent replaces the mechanical system of manual image examination with an automated computer-implemented system using machine learning algorithms. The system processes captured images through trained models that automatically detect features of interest, classify abnormalities, and generate diagnostic reports, eliminating the time-consuming manual review process while maintaining diagnostic accuracy through sophisticated algorithmic analysis
Solution Approach 2:
The patent creates a virtual copy of the diagnostic process through machine learning models that have been trained on large datasets of medical images. These models replicate human expert decision-making capabilities by learning from annotated training data, enabling automated analysis that mimics human diagnostic reasoning without the time constraints of manual examination
2Productivity
If automated image analysis systems are implemented, then processing efficiency increases and examination time decreases, but the accuracy of detecting features of interest and recognizing characteristics deteriorates
Solution Approach 1:
The patent applies preliminary action by extensively training machine learning models on large datasets of annotated medical images before deployment. The models undergo multiple training phases including data preprocessing, feature extraction, model training, and validation on test datasets. This preliminary training ensures the automated system achieves high detection accuracy before it begins processing actual patient images
Solution Approach 2:
The patent implements feedback mechanisms where the automated system's detection results are continuously evaluated and used to refine model performance. The system provides feedback on detected features, allows for correction and verification, and uses this information to improve future detections. This feedback loop ensures maintaining high accuracy while processing images efficiently
3Extent of automation
If extant computer-implemented systems are used, then some automation is achieved, but the systems lack the capability to intelligently analyze image sequences and recognize temporal patterns of events
Solution Approach 1:
The patent applies dynamics by implementing machine learning models that can adapt to different types of medical procedures, imaging modalities, and diagnostic tasks. The system dynamically adjusts its analysis based on the specific characteristics of the input images and can handle various scenarios including real-time video endoscopy and capsule endoscopy with different frame rates and image qualities
Solution Approach 2:
The patent creates a universal automated analysis system that can handle multiple types of medical imaging procedures through a single platform. The machine learning models are designed to process different image types, detect various features of interest, and adapt to different diagnostic requirements, providing versatile intelligent analysis capabilities across multiple applications
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.