3D Deep Learning Diagnosis of Interstitial Lung Disease
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Solution Overview
Problem
Current diagnostic methods for interstitial lung diseases, particularly idiopathic pulmonary fibrosis (IPF), suffer from high variability and suboptimal performance, often requiring invasive procedures like surgical biopsy, which carry significant mortality risk, and lack accurate non-invasive alternatives.
Innovation Solution
A computer system utilizing a three-dimensional deep learning model trained on CT images and clinical factors to predict disease diagnoses, employing a weighted focal loss and stochastic gradient descent with momentum, and calibrated using scaling methods, without manual image segmentation.
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 creates a virtual copy of the biopsy process through machine learning models that simulate and predict tissue pathology outcomes based on non-invasive imaging data (CT scans, PET scans). The model learns from training data containing paired imaging and biopsy results, then generates predictive diagnoses without requiring actual tissue sampling, thereby eliminating the harmful effects of invasive procedures while preserving diagnostic accuracy.
Solution Approach 2:
The patent replaces the mechanical invasive procedure of surgical biopsy with an automated computational system. The machine learning model processes imaging data through algorithms and neural networks to generate diagnostic predictions, substituting the physical needle insertion and tissue extraction process with a digital information processing system that eliminates patient exposure to procedural risks.
2Adaptability or versatility
If standard work-up procedures are used, then diagnostic framework is provided, but inter-clinician variability increases
Solution Approach 1:
The patent implements a self-service diagnostic system where the machine learning model autonomously analyzes imaging data and generates diagnostic predictions without requiring human clinician interpretation. The model consistently applies the same algorithmic criteria to all cases, eliminating inter-clinician variability while maintaining the adaptability of comprehensive diagnostic evaluation through its ability to process multiple imaging modalities and clinical parameters.
Solution Approach 2:
The patent incorporates feedback mechanisms where the model is trained on labeled data containing ground truth diagnoses, allowing it to learn from correct diagnostic outcomes. The system continuously refines its predictions by comparing model outputs against known diagnoses in the training set, thereby reducing variability and improving reliability through iterative learning from feedback signals.
3Measurement precision
If manual image segmentation is performed, then image analysis precision is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary action by pre-processing and normalizing imaging data during the training phase, where the model learns to automatically identify and segment relevant anatomical structures and pathological features. This preliminary learning of segmentation patterns allows the model to rapidly process new cases without requiring time-consuming manual segmentation steps during actual diagnostic evaluation.
Solution Approach 2:
The patent replaces the manual mechanical process of image segmentation with automated computational algorithms. The machine learning model uses computer vision techniques to automatically identify, segment, and analyze relevant imaging features, substituting the time-consuming manual contouring and region-of-interest definition with rapid automated image processing that maintains or improves segmentation precision.
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.