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Smart Wearable for Canine Osteoarthritis Detection Using AI and Sensor Data

dac.digital

Medical

Health & Fitness

Information technology

Challenges in Non-Invasive Canine Osteoarthritis Detection

Veterinary clinics face high costs, time delays, and resource constraints in diagnosing canine osteoarthritis through traditional methods like radiographs or MRI. Existing tech partnerships failed due to inaccurate models, poor data annotation, and logistic regression limitations, requiring a budget-conscious solution to validate product feasibility.

About the Client

A veterinary-focused startup developing AI-powered wearables for animal health monitoring, targeting professional veterinary clinics and animal hospitals.

Goals for Developing AI-Powered OA Detection System

Create a viable wearable collar prototype with embedded sensors for movement tracking
Develop deep learning algorithms to classify OA severity from raw sensor data
Validate product feasibility through data accuracy and cross-breed universality
Establish a scalable methodology for further development post-testing

Core System Functionalities

Accelerometer/gyroscope sensor integration for movement pattern capture
Real-time data processing via deep convolutional neural networks
OA severity classification and alert system
Data export for veterinary analysis
Cross-breed adaptability through universal deep learning models

Technologies for Implementation

Deep learning frameworks (TensorFlow/PyTorch)

Convolutional Neural Networks (CNN)

Sensor data preprocessing pipelines

Edge computing for on-device processing

System Integrations

Veterinary clinic management software APIs
Cloud storage for longitudinal data analysis
Mobile app for owner alerts and data visualization

Performance and Scalability Requirements

90%+ accuracy in OA classification during testing
Real-time processing latency <50ms
Data encryption for pet health information security
Scalable architecture for multi-device deployment

Expected Business and Healthcare Impact

Enables cost-effective early OA detection in dogs, reducing reliance on expensive imaging. Validates AI feasibility in veterinary diagnostics, positioning the startup for funding and commercialization. Achieves 80%+ model accuracy within 180 development hours, creating a foundation for future smart animal healthcare solutions.