Deep Learning Medical Diagnostic Model Integrating Clinical Data
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Solution Overview
Problem
Conventional medical image analysis is labor-intensive, prone to human error, and lacks integration of clinical data, leading to reduced accuracy and efficiency in diagnosing medical conditions.
Innovation Solution
A deep learning-based medical diagnostic method that uses pre-trained models to extract regions of interest from 3D medical images and generate diagnostic results by combining latent space data with clinical data, reducing manual bias and enhancing model robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If statistical techniques are used for medical image analysis, then the analysis process is simplified and can be performed with basic tools, but systematic errors occur and information loss reduces accuracy
Solution Approach 1:
The patent replaces statistical techniques (mechanical/mathematical processing) with deep learning models (neural network-based intelligent processing). Specifically, convolutional neural networks (CNNs) are used to extract features from medical images, substituting the manual feature engineering and statistical analysis with automated, adaptive learning systems that reduce systematic errors and information loss while maintaining implementation feasibility through pre-trained models.
2Productivity
If only medical images are used for analysis, then the analysis process is simpler and faster, but the accuracy is reduced due to lack of clinical context
Solution Approach 1:
The patent merges medical images and clinical data into a unified analysis framework. The deep learning model simultaneously processes both image data (through CNNs) and clinical data (through fully connected layers), combining them in the latent space to generate comprehensive diagnostic results. This integration maintains analysis speed through automated processing while improving accuracy by incorporating multidimensional patient information.
3Measurement precision
If deep learning models are trained with both medical images and clinical data, then diagnostic accuracy is improved, but the model complexity and training difficulty increase
Solution Approach 1:
The patent applies preliminary action by using pre-trained deep learning models, specifically pre-trained CNNs for image feature extraction. These pre-trained models have already learned general image features from large datasets, so they can be directly applied to medical images without extensive retraining. This reduces the overall training complexity and computational burden while maintaining high diagnostic accuracy when combined with clinical data.
4Ease of operation
If manual feature extraction is used, then the interpretability of the analysis process is maintained, but labor intensity increases and human errors occur
Solution Approach 1:
The patent implements self-service by enabling the deep learning model to automatically extract features and generate diagnostic results without manual intervention. The CNN-based architecture automatically learns relevant features from medical images, and the integrated model automatically combines image and clinical data to produce diagnoses. This eliminates labor-intensive manual feature extraction and reduces human errors while maintaining interpretability through the model's structured processing of input data.
Data Source
AI summary
A medical diagnostic method according to an embodiment includes receiving a plurality of slide images of a three-dimensional (3D) medical image obtained by capturing an object and clinical data for the object, extracting one or more slide images including a lesion among the plurality of slide images, extracting a region of interest (ROI) including the lesion from each of the extracted one or more slide images, generating latent space data including a feature of an image of the extracted ROI using a pre-trained deep learning-based ROI abstraction model, and generating a diagnostic result for the object from the latent space data and the clinical data using a pre-trained deep learning-based medical diagnostic model.