Deep Learning CT Triage for Indeterminate Stroke Mimics
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Solution Overview
Problem
Existing machine learning algorithms for identifying ischemic stroke in medical images struggle with indeterminate imaging findings, failing to accurately distinguish between ischemic stroke and its mimics, which are brain diseases that present similar symptoms.
Innovation Solution
A deep learning-based system that analyzes medical images, specifically CT scans, using convolutional neural networks (DCNNs) to predict the presence of intracranial abnormalities and classify them as probable, not probable, or indeterminate ischemic stroke mimics, supported by an explainable model and a radiology atlas to enhance diagnostic accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If dichotomous classification algorithms are used to identify abnormalities in medical images, then the algorithm can determine whether an abnormality is present or not, but it cannot handle indeterminate imaging findings
Solution Approach 1:
The patent changes the output parameter from binary (present/absent) to continuous probability scores (0-1 range), enabling the system to express diagnostic uncertainty. This allows indeterminate cases to be identified and handled appropriately, resolving the contradiction between automation and reliability for ambiguous imaging findings.
Solution Approach 2:
The system transitions from static binary classification to dynamic probability-based classification, where the output can vary continuously based on the strength of evidence. This dynamic approach allows the algorithm to adapt to different levels of diagnostic confidence and handle indeterminate cases by flagging them for further review.
2Productivity
If machine learning algorithms are used to identify ischemic stroke, then detection speed is improved, but false positives and false negatives increase due to inability to distinguish mimics
Solution Approach 1:
The patent introduces an intermediary layer of probability scoring between image input and final diagnosis. This intermediary allows the system to express uncertainty and differentiate between high-confidence detections and ambiguous cases that may be mimics, thereby reducing false positives and negatives while maintaining automated detection speed.
Solution Approach 2:
The system provides feedback in the form of probability scores and confidence levels to clinicians, enabling them to adjust their interpretation based on the algorithm's certainty. This feedback mechanism helps reduce false positives by allowing clinicians to review cases with lower confidence scores, improving overall measurement precision.
3Measurement precision
If traditional imaging interpretation methods are used, then diagnostic thoroughness is maintained, but time consumption increases in emergency settings
Solution Approach 1:
The patent performs preliminary automated analysis of medical images, generating probability scores and highlighting potential abnormalities before clinician review. This preliminary action filters out obvious cases and prepares structured information for clinicians, reducing their workload and time consumption while maintaining diagnostic thoroughness through the preserved probability-based assessment.
Data Source
AI summary
An ischemic stroke mimic is detected, or otherwise predicted, based on medical images acquired from a subject. Medical image data, which include medical images acquired from a head of the subject, are accessed with a computer system. A machine learning model (e.g., one or more deep convolutional neural networks) is trained on training data to estimate a probability of an acute intracranial abnormality being depicted in a medical image. Intracranial abnormality prediction data are generated by inputting the medical image data to the machine learning model. The intracranial abnormality prediction data include an intracranial abnormality probability score for each of the medical images in the medical image data. An ischemic stroke mimic classification for the medical image data is generated based on the intracranial abnormality prediction data, and may be displayed to a user with the computer system.