3D Deep Learning for Non-Invasive IPF Diagnosis
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Solution Overview
Problem
Current diagnostic methods for interstitial lung diseases, particularly idiopathic pulmonary fibrosis (IPF), suffer from high variability and require invasive procedures like surgical biopsy, which are risky and have a significant mortality rate, while non-invasive methods lack accuracy and consistency.
Innovation Solution
A computer system utilizing a three-dimensional deep learning model trained with CT images and clinical factors to predict the likelihood of IPF, using surgical pathology outcomes for labeling and optimizing the model with weighted focal loss and stochastic gradient descent, enabling non-invasive diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If surgical biopsy is used for definitive diagnosis, then diagnostic accuracy is improved, but patient mortality risk increases
Solution Approach 1:
The patent uses CT images as non-invasive copies or proxies of the actual lung tissue pathology. Instead of requiring physical tissue samples through biopsy, the system analyzes detailed imaging data that captures the essential diagnostic features, thereby achieving accurate diagnosis without the harmful effects of surgical intervention
Solution Approach 2:
The patent replaces the mechanical surgical biopsy procedure with an automated machine learning analysis system. The deep learning model processes CT images and clinical data to provide diagnostic predictions, substituting the physical invasive procedure with a computational approach that eliminates surgical risks while maintaining diagnostic capability
2Object-affected harmful factors
If non-invasive work-up methods are used, then patient safety is improved, but diagnostic performance deteriorates
Solution Approach 1:
The patent transforms the diagnostic approach by changing the parameters analyzed in non-invasive methods. Instead of relying on simple imaging review, the system incorporates multiple parameters including detailed CT image features, pulmonary function test results, and clinical history, processed through machine learning to achieve diagnostic performance comparable to or exceeding biopsy accuracy
Solution Approach 2:
The patent creates a composite diagnostic system that integrates multiple data sources and modalities. By combining CT imaging data, clinical parameters, and machine learning algorithms into a unified diagnostic framework, the system achieves superior diagnostic performance while maintaining the safety benefits of non-invasive methodology
3Ease of manufacture
If standard work-up guidelines are followed, then diagnostic framework is improved, but inter-clinician variability increases
Solution Approach 1:
The patent implements a self-service diagnostic system where the machine learning model automatically analyzes patient data and generates diagnostic predictions without requiring human clinician interpretation. This automation eliminates inter-clinician variability by providing consistent, objective diagnostic assessments based on the same algorithms and criteria for all patients
Solution Approach 2:
The patent incorporates feedback mechanisms where the machine learning model learns from training data including surgical pathology outcomes and clinical assessments. This feedback loop allows the system to continuously improve its diagnostic accuracy while maintaining consistent performance, reducing variability by grounding decisions in learned patterns rather than individual clinician judgment
Data Source
AI summary
A method of automated diagnosis of disease database entities includes receiving a case processing request via an input application programming interface (API), extracting image data from the case processing request including at least one medical scan image of the patient, selecting at least a portion of the medical scan image(s) according to specified selection criteria, normalizing the selected at least a portion of the medical scan image(s), supplying the selected at least a portion of the medical scan image(s) to a machine learning model to generate a target medical condition prediction output, wherein the target medical condition prediction output is indicative of a likelihood that a patient will experience a future disease diagnosis event corresponding to the target medical condition, and automatically transmitting the target medical condition prediction output as an electronic transmission via an output API to a provider system associated with the patient.