Transfer Learning of a Neural Network for Robotic Surgical Assessment

NCT06612606 | Completed DMC

• 1 other identifier: 2021-247
• Study Type: observational
• Target: 5
• Locations: 1 country
• Sites: 1

Brief 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

Keywords

deep learning; transfer learning; robot surgery; surgical assessment

Outcome Measures

Primary Outcomes (6)

• Accuracy of action recognition using clinical data from scratch

  Accuracy of the deep learning algorithm for action recognition, when training the model from scratch using clinical data from robot surgical procedures.

  From start to end of a the robot surgical procedure that is being assessed in terms of action recognition.

• Accuracy of skills assessment using clinical data from scratch

  Accuracy of the deep learning algorithm for skills assessment, when training the model from scratch using clinical data from robot surgical procedures.

  From start to end of a the robot surgical procedure that is being assessed in terms of action recognition.

• Accuracy of action recognition using the pretrained network directly on clinical data

  Accuracy of the pretrained deep learning algorithm for action recognition, when using the model directly on clinical data from robot surgical procedures.

  From start to end of a the robot surgical procedure that is being assessed in terms of action recognition.

• Accuracy of skills assessment using the pretrained model directly on clinical data

  Accuracy of the pretrained deep learning algorithm for skills assessment, when using the model directly on clinical data from robot surgical procedures.

  From start to end of a the robot surgical procedure that is being assessed in terms of skills assessment.

• K fold accuracies for action recognition and skills assessment for the complete retraining of the pretrained network.

  K fold cross-validation accuracies when retraining the complete pretrained model on the clinical data for both action recognition and skills assessment.

  From the start to the end of the clinical procedures.

• K fold accuracies for action recognition and skills assessment for the partial retraining of the pretrained network.

  K fold cross validation accuracies for action recognition and skills assessment for the retraining of the LSTM and dense layers of the pretrained network using clinical data.

  From the start to the end of the clinical procedures.

Secondary Outcomes (4)

• Weighted recall/sensitivity, precision and F1 score for action recognition of the clinical network trained from scratch

  From start to end of a the robot surgical procedure that is being assessed in terms of action recognition.

• Weighted recall/sensitivity, precision and F1 score for Skills Assessment of the clinical network trained from scratch

  From start to end of a the robot surgical procedure that is being assessed in terms of skills assessment..

• Predictive certainty of the action recognition and skills assessment of the network trained from scratch on the clinical data.

  From the start to the end of the clinical procedures.

• Predictive certainty of the action recognition and skills assessment of the network partially retrained network.

  From the start to the end of the clinical procedures.

Study Arms (2)

Experienced robot surgeons

Robot surgeons with 100 or more performed robot surgical cases.

Other: observational study

Novice robot surgeons

Robot surgeons with less than 100 performed robot surgical cases.

Other: observational study

Interventions

observational study OTHER

This was an observational study with no intervention.

Experienced robot surgeons; Novice robot surgeons

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study population consisted of robot surgeons who where either experienced or novice (being specialized doctors undergoing surgical fellowship to become robot surgeons). All procedures where robot-assisted procedures done on patients, who were admitted for treatment at the urological department. The patients also gave their consent regarding data collection. However, the real participant where the robot surgeons.

You may qualify if:

• Robot surgeons who are experienced with more than 100 cases.
• Robot surgical fellows with less than 100 cases.
• Robot surgeons who worked at the urological department of Aalborg University Hospital.

Sponsors & Collaborators

• Aalborg University: lead

Study Sites (1)

Department of urology, Aalborg University Hospital

Aalborg, North Jutland, 9000, Denmark

Location

Related Links

• The URL for the IPD, also the site where all the datasets and codes will be made available as open source.

MeSH Terms

Interventions

Observation

Intervention Hierarchy (Ancestors)

Methods; Investigative Techniques

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal investigator

Study Record Dates

• First Submitted: September 22, 2024
• First Posted: September 25, 2024
• Study Start: May 22, 2023
• Primary Completion: May 26, 2023
• Study Completion: May 26, 2023
• Last Updated: September 26, 2024

Record last verified: 2024-09

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, ANALYTIC CODE
• Time Frame: The IPD and supporting information will be available from the time of submission to the journal, and will be available for an unlimited amount of time.
• Access Criteria: Everyone who has access to the open source website of GitHub will be able to access the data. And anyone who will have access to the journal will have access to the supporting information.

Locations

DK (1)