Multimodal Medical Data Fusion for Rectal Cancer Remission Evaluation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current clinical evaluation methods for neoadjuvant therapy in rectal cancer rely heavily on expert experience, leading to inconsistent decision-making and judgment errors due to human factors, and there is a lack of tools for objective and consistent evaluation of treatment efficacy using multimodal medical data.
Innovation Solution
An evaluation method and device for multimodal medical data fusion using artificial intelligence to extract and fuse feature vectors from multiple data modalities, including rectal cancer images, clinical data, and tumor marker information, through a pre-trained multimodal fusion evaluation model to predict disease remission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If expert experience-based evaluation is used, then clinical judgment can be made, but judgment accuracy and consistency deteriorate due to human factors
Solution Approach 1:
The patent replaces the mechanical system of human expert evaluation with an AI-based multimodal fusion evaluation system. The system automatically processes multiple data modalities (imaging, pathology, clinical data) through neural networks and fusion algorithms to generate objective evaluation results, eliminating human factors that cause judgment inconsistency and accuracy variation.
Solution Approach 2:
The evaluation system is designed to universally process multiple data modalities including imaging data, pathology data, and clinical data through a unified multimodal fusion framework. This universal approach allows consistent evaluation across different data types and clinical scenarios, improving both reliability and precision simultaneously.
2Productivity
If manual evaluation methods are used, then clinical decision-making can proceed, but medical risk increases due to evaluation inaccuracies
Solution Approach 1:
The patent replaces manual evaluation with an automated AI system that processes multimodal data through neural networks and fusion algorithms. This substitution maintains high evaluation efficiency while significantly improving medical safety by reducing evaluation errors and providing more reliable clinical decision support.
3Loss of information
If multiple data modalities are integrated, then evaluation comprehensiveness improves, but system complexity increases
Solution Approach 1:
The patent segments the complex multimodal evaluation system into distinct processing modules for different data modalities (imaging module, pathology module, clinical data module). Each module processes its specific data type independently, and results are then fused through a dedicated fusion mechanism. This segmentation manages system complexity while maintaining comprehensive data integration.
Solution Approach 2:
The patent introduces a multimodal fusion mechanism as an intermediary that integrates results from different data modalities. This fusion layer acts as a mediator that combines imaging features, pathology features, and clinical features into a unified evaluation result, managing the complexity of integrating multiple data sources while preserving comprehensive information.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
The present application relates to the technical field of medical treatment, and discloses a multi-modal medical data fusion evaluation method and apparatus, a device, and a storage medium. The method comprises: obtaining medical data to be evaluated of a plurality of modes of a target object; performing feature extraction on said medical data of each mode to obtain a plurality of feature vectors, and fusing the plurality of feature vectors to obtain a fused feature vector; and inputting the fused feature vector into a trained multi-modal fusion evaluation model to acquire an evaluation result output by the model. According to the present application, feature extraction and feature fusion are performed on the multi-modal medical data on the basis of artificial intelligence to obtain a fused feature vector, and a multi-modal fusion evaluation model is used to predict and evaluate the disease alleviation situation of the target object on the basis of the fused feature vector. The method can assist in accurately evaluating the disease alleviation situation under a pathological level, thereby improving the determination accuracy and reducing the medical risk. The present application further discloses multi-modal medical data fusion evaluation.