NCT06612606

Transfer Learning of a Neural Network for Robotic Surgical Assessment

Transfer Learning of a Pretrained Preclinical Neural Network for Robotic Surgical Assessment on Limited Clinical Data

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 5 (actual)
• Sponsor: Aalborg University · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Accepted

Summary

The goal of this observational study is to explore how pretrained artificial intelligence (AI) models, trained on preclinical data, can improve the accuracy of action recognition and skills assessment in robot-assisted surgery (RAS) in urological patients by the use of transfer learning. The main questions it aims to answer are: * Can pretrained AI models accurately assess action recognition and skills assessment in clinical surgeries? * How do different training approaches of transfer learning affect the performance of the AI models? A baseline model developed from scratch using clinical data will be compared to pretrained models that are (1) directly applied to clinical data (2) fine-tuned by training only some layers of the AI model, and (3) fully retrained to see if these approaches improve performance. Participants who are robot surgeons will: * Undergo RAS procedures on patients, with no intervention, where video data will be collected for later action recognition and skills assessment. * Contribute to model training and evaluation through clinical dataset integration.

Conditions

• Robot Surgery

Interventions

Type	Name	Description
OTHER	observational study	This was an observational study with no intervention.

Timeline

• Start date: 2023-05-22
• Primary completion: 2023-05-26
• Completion: 2023-05-26
• First posted: 2024-09-25
• Last updated: 2024-09-26

Locations

1 site across 1 country: Denmark