Cloud Based AI-Driven Video Analytics (CAVs) in Laparoscopic Surgery: A Step Closer to a Virtual Portfolio

Area of Science:

• Surgical Technology
• Artificial Intelligence
• Medical Education

Background:

• Minimally invasive surgery requires continuous skill development.
• Traditional surgical training lacks objective performance metrics.
• Need for standardized assessment tools in surgical education.

Purpose of the Study:

• To evaluate cloud-based AI-driven video analytics (CAVs) for minimally invasive surgery.
• To explore CAVs as a virtual portfolio for surgical trainees and professionals.
• To assess platform accessibility, feedback mechanisms, and AI integration.

Main Methods:

• Independent online demonstrations of three CAV platforms: Theator, Touch Surgery™, and C-SATS®.
• Evaluation of online/app accessibility, trainee feedback capabilities, and AI-driven analysis of surgical steps.
• Assessment of AI integration for operation-specific steps and critical views.

Main Results:

• CAVs provide limitless cloud storage and smart theatre integration for surgical videos.
• Platforms offer communication, sharing, and structured feedback for trainees.
• AI facilitates time-based analysis, highlights critical milestones, and includes skills scoring systems.

Conclusions:

• Cloud-based AI-driven video analytics (CAVs) are emerging tools for storing, analyzing, and reviewing surgical videos.
• CAVs can improve surgical training, governance, and standardization.
• Future integration into virtual curricula can provide structured assessment of surgical progression.