Development of an AI-Powered Wearable System for Early Detection of Osteoarthritis in Dogs

Veterinary clinics and pet owners face challenges with early diagnosis of canine Osteoarthritis (OA), relying on costly and time-consuming medical imaging modalities such as radiographs, MRI, or ultrasound, which often require specialist interpretation. There is a demand for a portable, affordable, and reliable wearable device that can detect OA symptoms early through movement analysis, minimizing the need for expert intervention and reducing diagnostic delays.

A startup focused on developing innovative veterinary health monitoring devices to assist animal clinics and pet owners in early disease detection and health tracking.

• Develop a compact, lightweight wearable device that attaches easily to a dog’s collar to monitor movement patterns.
• Implement advanced machine learning algorithms, specifically deep learning models and convolutional neural networks, for real-time analysis of sensor data to classify and assess the severity of OA.
• Ensure the solution provides high diagnostic accuracy, targeting a minimum of 80% accuracy based on sensor data analysis.
• Create reliable data processing pipelines to clean and annotate raw sensor data, enabling scalable model training and deployment.
• Establish a methodology for large-scale testing and validation to confirm system effectiveness across different dog breeds.
• Design hardware-software interfacing for stable data collection from accelerometers and gyroscopes, supporting continuous monitoring.
• Facilitate future expansion of the system for ongoing disease monitoring and health trend analysis.

• Embedded sensor array including accelerometers and gyroscopes for continuous motion tracking.
• Data preprocessing modules for cleaning and annotating raw sensor data.
• Deep neural network models for pattern recognition and classification of OA presence and severity.
• Real-time analysis engine capable of providing immediate alerts based on movement deviations indicative of OA symptoms.
• Custom data interface to connect hardware with analysis software, ensuring stable and consistent data flow.
• Scalable architecture designed to incorporate additional data points and breed diversity over time.

• Sensor hardware components for data acquisition.
• Mobile or desktop applications for user interface and data visualization.
• Cloud platforms for data storage, model deployment, and further analytics.
• Veterinary management systems for integrating diagnostic results and patient records.

• High accuracy with targeted >80% classification precision and recall.
• Low latency for real-time analysis and alerts.
• System robustness and stability for continuous monitoring over extended periods.
• Data security and privacy compliance for sensitive pet and owner information.
• Scalability to support increased data volume and multiple breeds without degradation of performance.

The development of this AI-powered wearable device is expected to significantly improve early diagnosis accuracy of osteoarthritis in dogs, reaching at least 80% accuracy. It will enable veterinary clinics and pet owners to detect OA symptoms proactively, reducing reliance on expensive medical imaging and specialist interpretation. This system aims to facilitate ongoing health monitoring, improve treatment planning, and ultimately enhance canine quality of life while opening new revenue streams for veterinary practices and pet care companies.