AI X-Ray Image Labeling for Flexible Veterinary Shot Order
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Solution Overview
Problem
Existing radiology instruments for veterinarians label x-ray images based on a predetermined shot order, which can lead to improper labeling when images are taken out of order, affecting further image processing.
Innovation Solution
Implementing a machine learning algorithm to automatically identify and label x-ray images based on their actual content, allowing for a free-form shot order and correct orientation, independent of the capture sequence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a predetermined shot order protocol is used for labeling x-ray images, then the labeling process is simple and automatic, but the labeling accuracy deteriorates when images are captured out of order
Solution Approach 1:
The patent replaces the mechanical ordering system (where images must be captured in a fixed sequence to receive correct labels) with an AI-based content recognition system. The machine learning model analyzes the actual anatomical content of each image to assign labels, substituting the rigid mechanical protocol with an intelligent, content-aware labeling mechanism that works regardless of capture sequence.
Solution Approach 2:
The patent changes the fundamental parameter of labeling from being order-dependent to being content-dependent. Instead of assigning labels based on the sequence number in the capture protocol, the system transforms the labeling parameter to be based on AI-analyzed anatomical features, allowing flexible capture ordering while maintaining accurate labeling.
2Adaptability or versatility
If images are captured in a free-form order to accommodate patient movement and veterinary needs, then workflow flexibility improves, but automatic labeling based on predetermined order fails
Solution Approach 1:
The patent implements self-service labeling where the system automatically identifies and labels images based on their own content without requiring external intervention to reorder or manually tag them. The AI model enables each image to be self-labeled according to its anatomical content, making the system adaptable to any capture sequence while maintaining reliability.
3Measurement precision
If manual renaming and reclassification of images is performed to correct labeling errors, then labeling accuracy improves, but time consumption and workflow efficiency deteriorate
Solution Approach 1:
The patent performs preliminary action by implementing accurate AI-based labeling at the moment of image capture, eliminating the need for subsequent manual renaming and reclassification. The correct labels are assigned upfront based on content analysis, preventing the accumulation of mislabeled images that would require time-consuming correction later.
Data Source
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AI summary
An example method includes capturing, via an x-ray machine, a plurality of x-ray images of a patient covering a number of different anatomy of the patient in any order, using a machine learning algorithm to process the plurality of x-ray images for identification of an anatomy in respective x-ray images of the plurality of x-ray images, associating a label with each of the plurality of x-ray images based on the identification of the anatomy, positioning each of the plurality of x-ray images upright based on a preset coordinate scheme for the anatomy, arranging the plurality of x-ray images into a predetermined order based on the species of the patient, and generating and outputting a data file including the plurality of x- ray images in the predetermined order, positioned upright, and labeled.