Revolutionizing Laparoscopic Surgery with AI-Based Video Tagging and Real-Time Insight

Problem Statement

Laparoscopic surgeries face persistent challenges due to limited visibility, dynamic instrument and patient movements, and complex anatomical variations. These limitations increase the risk of surgical errors and retained instruments, impacting patient safety and procedural efficiency in the healthcare industry. To address these issues, AI-based surgical video tagging was introduced, enabling real-time tracking of instruments and organs. The solution enhances spatial awareness, improves surgical precision, and automates manual processes, thereby reducing workflow inefficiencies and ensuring safer surgical outcomes.

Solution

The solution uses deep learning and computer vision to deliver real-time instrument and organ segmentation, ensuring enhanced visualisation during procedures. An AI-powered decision support system improves surgical precision by providing automated guidance and spatial awareness. The system integrates seamlessly with existing surgical infrastructure, allowing hospitals to adopt the technology with minimal disruption. Additionally, re-playable and resuable and annotated surgical videos enable training and post-surgical review, building a continuous feedback loop for surgeons and trainees. This approach reduces dependency on manual tracking while delivering consistent, accurate insights.

Impact

The deployment of AI-based surgical video tagging has led to 70% cost savings by eliminating manual tracking and a 5x increase in productivity through automated assistance. Hospitals and training centres benefit from scalable deployment, supporting widespread adoption across surgical facilities. Most importantly, the system significantly reduces the risk of tool retention and surgical errors, ensuring enhanced patient safety and better post-operative outcomes. The solution delivers measurable operational efficiencies while raising the standard of surgical precision.

Services Rendered

• Problem Definition and Requirement Analysis​
• AI Model Development​
• Data Preparation & Augmentation​
• Integration with Surgical Infrastructure​
• MLOps Implementation​
• Testing & Deployment​
• Post-Launch Support