Automated Veterinary Radiography AI Analysis System
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Solution Overview
Problem
Current AI systems for analyzing radiographic images in veterinary radiology are inefficient due to the need for manual identification of body regions, incorrect region identification leading to false results, and the exponential increase in report templates with multiple AI models, making it difficult to create comprehensive diagnostic reports.
Innovation Solution
A system that automatically processes radiographic images to classify, crop, and label body regions, directing sub-images to specific AI processors for evaluation, and synthesizes results into a cohesive report using clustering techniques and Natural Language Processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification and cropping of body regions is performed prior to AI evaluation, then processing accuracy is improved, but processing time and operational complexity increase
Solution Approach 1:
The system performs automatic body region identification and cropping without requiring manual user input. The AI processor autonomously detects body regions in radiographic images, crops them appropriately, and directs them to relevant specialized AI processors, eliminating the need for manual identification while maintaining high accuracy.
Solution Approach 2:
The system performs preliminary automatic body region identification and cropping before AI evaluation. By pre-processing images to identify and crop body regions automatically, the system prepares images in advance for targeted AI processor evaluation, reducing overall processing time while maintaining accuracy.
2Adaptability or versatility
If multiple AI processors are used to evaluate different body regions and orientations, then diagnostic comprehensiveness is improved, but system complexity and processing overhead increase
Solution Approach 1:
The system divides the diagnostic task into multiple specialized AI processors, each trained to evaluate specific body regions (thorax, abdomen, pelvis, limbs) and orientations (lateral, dorsal, ventral). This segmentation allows comprehensive diagnostic coverage while managing complexity through modular, specialized components rather than a single monolithic system.
Solution Approach 2:
The system employs a universal workflow architecture that can handle multiple body regions, orientations, and AI processors through a common framework. The automatic body region identification and routing mechanism provides multi-functionality, allowing the same system structure to evaluate various anatomical regions and orientations without requiring separate dedicated systems for each.
3Reliability
If unidentified images are sent to multiple AI processors, then comprehensive evaluation is attempted, but processing efficiency decreases and false results increase
Solution Approach 1:
The system performs preliminary automatic body region identification before directing images to AI processors. By identifying body regions in advance, the system routes images only to relevant specialized AI processors, avoiding unnecessary processing by unrelated processors and eliminating false results from mismatched evaluations.
Solution Approach 2:
The system introduces an intermediary automatic body region identification step between image input and AI processor evaluation. This intermediary component analyzes images to determine body regions and orientations, then routes images to appropriate AI processors, ensuring that only relevant processors evaluate each image, thereby improving both accuracy and efficiency.
Data Source
AI summary
A system, an image analyzer and a method for diagnosing a presence of a disease or a condition in an image of a subject, for example, a veterinary patient, are provided including: classifying the image to a body region, and obtaining a classified, labeled, cropped, and oriented sub-image; directing the sub-image to artificial intelligence processor for obtaining an evaluation result, and comparing the evaluation result to a database library of evaluation results and matched written templates or a dataset cluster to obtain at least one cluster result; measuring the distance between the cluster result and the evaluation result to obtain at least one cluster diagnosis; and assembling the cluster diagnosis and the matched written templates to obtain a report to display the report to a radiologist. These system, analyzer and method are achieved in greatly reduced lengths of time and are useful for cost and time savings.