Customer Need: Medical organisations and clinical sites worldwide require accurate and timely insights from surgical telemetry data. The needs of this specific customer was no different. With the advent of advanced surgical robotics, the need for predictive maintenance and understanding of robot & instrument health was paramount. Additionally, there was a demand for information regarding the fidelity of instruments, the health of robot joints, the overall co-bot experience, and the usage patterns across different clinical locations.

Derived Solution:

1. Automated Surgical Telemetry Analysis: Introduced algorithms that automatically analyze surgical telemetry data in the cloud.

2. Predictive Maintenance with Deep Learning: Used deep learning techniques and extensive system knowledge to develop a predictive maintenance algorithm specifically for the robot wrist joint, enabling early detection of joint degradation from surgical data.

3. Metrics Development: Acted as a core contributor in developing several key metrics derived autonomously from surgical data in the cloud. These metrics offered precise insights into various critical aspects of the surgical process and instrument health.

Value Delivery:

• Enhanced Instrument Longevity: With the predictive maintenance algorithm, clinical sites can now anticipate joint degradation in the V-Wrist joint, ensuring timely maintenance and potentially extending the lifespan of the instrument.

• Business-Critical Insights: The autonomously derived metrics from surgical data offered invaluable insights that fed directly into business-critical activities.
  
  These insights aided in:

  • Gauging instrument fidelity

  • Monitoring the health of robot joints

  • Understanding the co-bot experience from the customer's perspective

  • Analyzing usage patterns across clinical sites globally, facilitating better resource allocation and improved patient care.