Skip to main content
OpenTrials
Completed

NCT Number: NCT06612606

Transfer Learning of a Neural Network for Robotic Surgical Assessment

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.

Completed

Looking for future studies?

Notify Me

Key information

Conditions

Robot Surgery

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Department of urology, Aalborg University Hospital

Aalborg, North Jutland, 9000, Denmark

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • 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.

Treatment and study plan

observational study

Other

This was an observational study with no intervention.

Primary outcomes

  1. Accuracy of action recognition using clinical data from scratch

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

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

  2. Accuracy of skills assessment using clinical data from scratch

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

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

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

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

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

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

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

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

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

    Time frame: From the start to the end of the clinical procedures.

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

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

    Time frame: From the start to the end of the clinical procedures.

    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.

Secondary outcomes

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

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

    Based on the performance of action recognition from the clinical network trained from scratch.

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

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

    Based on the performance of skills assessment from the clinical network trained from scratch.

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

    Time frame: From the start to the end of the clinical procedures.

    Predictive certainty with overall mean, minimum and maximum and depicted in probability plots for action recognition and skills assessment of the network trained from scratch on clinical data.

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

    Time frame: From the start to the end of the clinical procedures.

    Predictive certainty with overall mean, minimum and maximum and depicted in probability plots for action recognition and skills assessment of the partially retrained network, where only the LSTM and deep layers of the network was trained on clinical data.

Sponsors and collaborators

Lead sponsor

Aalborg University

Other

Registry information

Official study title

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

Important dates

Study start
2023
Primary completion
2023
Study completion
2023
First posted
Sep 25, 2024
Registry last updated
Sep 26, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Related clinical trials

Published trials that share one or more normalized conditions with this study.