Pet Radiology AI Reporting for Faster Abnormality Detection
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Solution Overview
Problem
The limited number of veterinary trained radiologists and the time-consuming nature of existing image-based diagnostic techniques in veterinary medicine, particularly in pet radiology, where incorrectly oriented images and missing or incorrect laterality markers hinder effective diagnosis by veterinarians, including both radiology-trained and non-radiology-trained professionals.
Innovation Solution
A joint text-image-model is trained to automate the processing and interpretation of pet radiographic images, using a hybrid CNN-Transformer architecture, enabling the detection of abnormalities and generation of detailed diagnostic reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review of radiographic images is performed by veterinarians, then diagnostic accuracy can be maintained, but time consumption and labor requirements increase significantly
Solution Approach 1:
A machine learning model serves as an intermediary between the radiographic images and the veterinarian. The model automatically analyzes images, detects abnormalities, and generates preliminary reports, thereby reducing the time veterinarians need to spend on manual review while maintaining diagnostic accuracy through expert validation.
Solution Approach 2:
The system enables self-service by allowing the machine learning model to autonomously perform image analysis, abnormality detection, and report generation. This automation reduces the burden on veterinarians and enables faster diagnostic processing without requiring constant human intervention for routine analyses.
2Reliability
If veterinary trained radiologists review medical images, then diagnostic quality improves, but the limited number of trained professionals creates accessibility issues
Solution Approach 1:
The machine learning model creates a digital copy of radiologist expertise, capturing their diagnostic capabilities through trained algorithms. This virtual replica can analyze images continuously without fatigue, making high-quality diagnostic services accessible to more veterinarians and patients regardless of geographic location or availability of trained radiologists.
Solution Approach 2:
The system provides universal diagnostic capability by deploying the machine learning model across multiple veterinary clinics and settings. The model can serve various types of animals and detect different abnormalities, making diagnostic services universally accessible to a broader audience beyond the limited pool of trained radiologists.
3Productivity
If automated image processing is implemented, then processing speed increases, but complexity of the system increases
Solution Approach 1:
The automated processing system is segmented into distinct functional modules: image acquisition, machine learning analysis, abnormality detection, and report generation. This modular architecture simplifies the overall system by allowing each component to be developed, tested, and maintained independently, reducing the complexity burden while maintaining high processing speed.
Data Source
AI summary
In one embodiment, a method comprising accessing radiographic images of an animal, wherein one or more first radiographic images of the radiographic images depict the animal from one or more views, respectively, and wherein one or more second radiographic images of the radiographic images depict one or more body parts of the animal, respectively, determining disease classifications associated with the animal based on analyzing the radiographic images by a machine learning model, generating a diagnostic report associated with the animal based on the machine learning model, wherein the diagnostic report includes the disease classifications and a natural-language textual radiology report, and sending instructions for presenting the diagnostic report to a user device.